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  • 151.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conze, Tim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy2011In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, p. 518-527Article in journal (Refereed)
    Abstract [en]

    Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.

  • 152.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Quantification and Localization of Colocalization2007In: Proceedings SSBA 2007: Symposium on Image Analysis, Linköping, March 14-15 / [ed] Magnus Borga, Anders Brun, Michael Felsberg, Linköping: Linköping University , 2007, p. 93-96Conference paper (Other academic)
    Abstract [en]

    This paper presents a comparison of two well known and commonly used methods for quantification of color colocalization in fluorescence microscopy image data. We also propose a new method based on a modified spectral decomposition borrowed from the field of remote sensing. Quantification and localization of colocalized pixels using modified spectral decomposition proved to be more robust than previous method when tested on a data set of artificial images with increasing levels of noise. The proposed method was also tested on a data set consisting of 16 color channels, showing that it is easily extendable to colocalization problems in more than two color dimensions.

  • 153.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles2009In: Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (OPTIMHisE): A satellite workshop associated with MICCAI / [ed] Daniel Elson and Nasir Rajpoot, London, 2009, p. 135-146Conference paper (Refereed)
    Abstract [en]

    Background fluorescence, also known as autofluorescence, and cross-talk are two problems in fluorescence microscopy that stem from similar phenomena. When biological specimens are imaged, the detected signal often contains contributions from fluorescence originating from sources other than the imaged fluorophore. This fluorescence could either come from the specimen itself (autofluorescence), or from fluorophores with partly overlapping emission spectra (cross-talk). In order to resolve spectral components at least two distinct wavelength intervals have to be imaged. This paper shows how autofluorescence can be presented statistically using a spectral angle histogram. Pixel classification by spectral angles was previously developed for detection and quantification of colocalization. Here we show how the spectral angle histogram can be employed to suppress autofluorescence. First, classical background subtraction (also referred to as linear unmixing) is presented in the form of a fuzzy classification by spectral angles. A modification of the fuzzy classification rules is also presented and we show that sigmoid membership functions lead to better suppression of background and amplification of true signals.

  • 154.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, p. 64-64Conference paper (Other academic)
    Abstract [en]

     

     

     

  • 155.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Dimensionality Reduction for Colour Based Pixel Classification2009In: Proceedings SSBA 2009: Symposium on Image Analysis, Halmstad, March 18-20 / [ed] Josef Bigun, Antanas Verikas, Halmstad: Halmstad University , 2009, p. 65-68Conference paper (Other academic)
    Abstract [en]

    In digital images, providing classification based on colour, hue or spectral angle is a problem usually solved by combining a variety of pre-processing steps, as well as object wise classifiers. We have developed a method for transforming colour or multispectral image data to a 1D colour histogram with respect to the digital characteristics of intensity measurements. Classification is then reduced to 1D histogram segmentation which is a simpler problem. The proposed method, based on ideas of spectral decomposition, was previously applied in dual-colour fluorescence microscopy for quantification and detection of colocalization insensitive to cross-talk. In this paper the principle is expanded to unsupervised colour based pixel classification algorithms in hue-saturation-lightness or luminance-chrominance colour spaces.

  • 156.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Spectral Angle Histogram: a Novel Image Analysis Tool for Quantification of Colocalization and Cross-talk2009In: 9th International ELMI Meeting on Advanced Light Microscopy / [ed] Kurt Anderson, Gail McConnell, Glasgow, UK, 2009, p. 66-67Conference paper (Other academic)
    Abstract [en]

    In fluorescence microscopy, when analyzing spectral components, it is common to record two (or more) greyscale images. Each greyscale image, referred to as a channel, corresponds to intensities in different wavelength intervals. If each pixel of a two-channel image is plotted in a space spanned by the two intensity channels a conventional scatter-plot is obtained. Single-coloured pixels are distributed along the axes, while colocalized pixels are distributed closer to the diagonal of the scatter-plot, and cross-talk (as well as noise) is observed as deviations of the single-coloured vectors from the axes. Detection of colocalized pixels is often based on a division of this 2D space into different regions by intensity thresholding. We have developed a method for reducing the scatter-plot to a 1D spectral angle histogram through a series of steps that compensate for the quantization noise which is always present in digital image data.

    Using the spectral angle histogram, we can quantify colocalization in a fully automated and robust manner. As compared to previous methods for quantification of colocalization, this approach is insensitive to cross-talk. In fact, it can also be employed to quantify and compensate for cross-talk, using either linear unmixing or fuzzy classification by spectral angle, ensuring complete suppression of cross-talk with minimal loss of information. Recently we started investigating how the method can deal with autofluorescence. Initial tests on real image data show that the method may be useful for improved background suppression and amplification of the true signals.

    The article “Quantification of colocalization and cross-talk based on spectral angles”, describing the method, is about to be published in the Journal of Microscopy. Authors have also filed a patent application “Pixel classification in image analysis” in 2008.

  • 157.
    Gedda, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Automatisk spårning av dendriter i konfokalmikroskopibilder av nervceller2008In: Medicinteknikdagarna 2008, 2008, p. 61-61Conference paper (Other academic)
  • 158.
    Gedda, Magnus
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements2005In: Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 2005, Proceedings, Part II, 2005, p. 809-Conference paper (Refereed)
    Abstract [en]

    This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes.

  • 159.
    Gedda, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    With the emergence of new imaging techniques, researchers are always eager to push the boundaries by examining objects either smaller or further away than what was previously possible. The development of image analysis techniques has greatly helped to introduce objectivity and coherence in measurements and decision making. It has become an essential tool for facilitating both large-scale quantitative studies and qualitative research. In this Thesis, methods were developed for analysis of low-resolution (in respect to the size of the imaged objects) three-dimensional (3D) images with low signal-to-noise ratios (SNR) applied to images from cryo-electron tomography (cryo-ET) and fluorescence microscopy (FM). The main focus is on methods of low complexity, that take into account both grey-level and shape information, to facilitate large-scale studies. Methods were developed to localise and represent complex macromolecules in images from cryo-ET. The methods were applied to Immunoglobulin G (IgG) antibodies and MET proteins. The low resolution and low SNR required that grey-level information was utilised to create fuzzy representations of the macromolecules. To extract structural properties, a method was developed to use grey-level-based distance measures to facilitate decomposition of the fuzzy representations into sub-domains. The structural properties of the MET protein were analysed by developing a analytical curve representation of its stalk. To facilitate large-scale analysis of structural properties of nerve cells, a method for tracing neurites in FM images using local path-finding was developed. Both theoretical and implementational details of computationally heavy approaches were examined to keep the time complexity low in the developed methods. Grey-weighted distance definitions and various aspects of their implementations were examined in detail to form guidelines on which definition to use in which setting and which implementation is the fastest. Heuristics were developed to speed up computations when calculating grey-weighted distances between two points. The methods were evaluated on both real and synthetic data and the results show that the methods provide a step towards facilitating large-scale studies of images from both cryo-ET and FM.

    List of papers
    1. Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
    Open this publication in new window or tab >>Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
    2005 (English)In: Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 2005, Proceedings, Part II, 2005, p. 809-Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes.

    Keywords
    clustering, shape, electron tomography, protein
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-74174 (URN)doi:10.1007/11552499_43 (DOI)3-540-28833-3 (ISBN)
    Available from: 2005-10-05 Created: 2005-10-05 Last updated: 2018-01-14
    2. Separation of blob-like structures using fuzzy distance based hierarchical clustering
    Open this publication in new window or tab >>Separation of blob-like structures using fuzzy distance based hierarchical clustering
    2006 (English)In: Symposium on Image Analysis: SSBA 2006, Umeå, Sweden, March 16-17, 2006, Proceedings, 2006Conference paper, Published paper (Other academic)
    Abstract [en]

    We present a method for separation of clustered biomedical blob-like structures in 2D and 3D images. The local maxima found on the fuzzy distance transform of the image are grouped by fuzzy distance based hierarchical clustering and used as seeds in a seeded watershed segmentation to delineate each individual blob. The method shows good initial results and is illustrated on two types of images: bright field microscopy (2D) images of in vitro stem cells and cryo electron tomography (3D) images of the antibody immunoglobulin G.

    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-21703 (URN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2018-01-12Bibliographically approved
    3. Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
    Open this publication in new window or tab >>Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
    2006 (English)In: Combinatorial Image Analysis: 11th International Workshop, IWCIA 2006, Berlin, Germany, June 19-21, 2006, Proceedings, 2006, p. 101-115Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a method for calculating fuzzy distances between pairs of points in an image using the A* algorithm and, furthermore, apply this method for fuzzy distance based hierarchical clustering. The method is general and can be of use in numerous applications. In our case we intend to use the clustering in an algorithm for delineation of objects corresponding to parts of proteins in 3D images. The image is defined as a fuzzy object and represented as a graph, enabling a path finding approach for distance calculations. The fuzzy distance between two adjacent points is used as edge weight and a heuristic is defined for fuzzy sets. A* is applied to the calculation of fuzzy distance between pair of points and hierarchical clustering is used to group the points. The normalised Hubert's statistic is used as validity index to determine the number of clusters. The method is tested on three 2D images; two synthetic images and one fuzzy distance transformed microscopy image of stem cells. All experiments show promising initial results.

    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 4040
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:uu:diva-21701 (URN)10.1007/11774938_9 (DOI)3-540-35153-1 (ISBN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
    4. An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
    Open this publication in new window or tab >>An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
    2006 (English)In: Discrete Geometry for Computer Imagery: 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006, Proceedings, 2006, p. 259-270Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we compare two different definitions of distance transform for gray level images: the Gray Weighted Distance Transform (GWDT), and the Weighted Distance Transform On Curved Space (WDTOCS). We show through theoretical and experimental comparisons the differences, the strengths and the weaknesses of these two distances.

    Series
    Lecture notes in computer science, ISSN 0302-9743 ; 4245
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-21702 (URN)10.1007/11907350_22 (DOI)978-3-540-47651-1 (ISBN)
    Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2018-01-12Bibliographically approved
    5. Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
    Open this publication in new window or tab >>Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
    2007 (English)In: 12th International Conference on Computer Analysis of Images and Patterns, 2007, p. 173-180Conference paper, Published paper (Refereed)
    Abstract [en]

    The MET protein controls growth, invasion, and metastasis in cancer cells and is thereby of interest to study, for example from a structural point of view. For individual particle imaging by Cryo-Electron Tomography of the MET protein, or other proteins, dedicated image analysis methods are required to extract information in a robust way as the images have low contrast and resolution (with respect to the size of the imaged structure). We present a method to identify the two parts of the MET protein, Beta-propeller and stalk, using a fuzzy framework. Furthermore, we describe how a representation of the MET stalk, denoted stalk curve, can be identified based on the use of non-uniform B-splines. The stalk curve is used to extract relevant geometrical information about the stalk, e.g., to facilitate curvature and length measurements.

    Keywords
    Cryo-ET, fuzzy distance, geometrical feature extraction
    Identifiers
    urn:nbn:se:uu:diva-11902 (URN)doi:10.1007/978-3-540-74272-2_22 (DOI)978-3-540-74271-5 (ISBN)
    Available from: 2007-11-05 Created: 2007-11-05 Last updated: 2010-03-25
    6. Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
    Open this publication in new window or tab >>Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
    2010 (English)In: 2010 IEEE International Conference On Acoustics, Speech And Signal Processing, 2010. ICASSP 2010, 2010, p. 646-649Conference paper, Published paper (Refereed)
    Abstract [en]

    Neurite tracing in 3D neuron images is important when it comes to analysing the growth and functionality of nerve cells. The methods used today are either of high complexity, limiting throughput, or semi-automatic, i.e., requiring user interaction. This makes them unsuitable for analysis where high throughput is needed. In this work we propose a method designed for low complexity and void of user interaction by using local path-finding. The method is illustrated on both phantom and real data, and compared with a widely used commercial software package with promising results.

    Series
    International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
    Keywords
    Tracing, neurite, path-finding, 3D
    National Category
    Computer and Information Sciences
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121571 (URN)000287096000158 ()978-1-4244-4296-6 (ISBN)
    Conference
    2010 IEEE International Conference on Acoustics, Speech, and Signal Processing Dallas, TX, MAR 14-19, 2010
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2018-01-12Bibliographically approved
    7. Heuristics for grey-weighted distance computations
    Open this publication in new window or tab >>Heuristics for grey-weighted distance computations
    2010 (English)In: Symposium on Image Analysis, Uppsala, March 11-12. Proceedings SSBA 2010., 2010Conference paper, Published paper (Other academic)
    Abstract [en]

    With new imaging techniques and computing power come increasing demands on the computation cost of image analysis algorithms. Grey-weighted distance transforms are traditionally computed using graph-based region-growing algorithms. These algorithms expand nodes in all directions by evolving an isotropic wave front. When calculating point-to-point distances, the isotropic propagation can be unnecessarily costly since it expands many nodes in directions away from the goal node. By introducing a heuristic to guide the search, the number of excessive nodes can be decreased. Here we introduce heuristics for computing Distance on Curved Spaces and Weighted Distance on Curved Spaces. We also examine the impact of these heuristics together with a heuristic previously proposed for fuzzy distance computations. The results show that the number of nodes expanded in point-to-point grey-weighted distances can be decreased by up to ~79%.

    National Category
    Computer and Information Sciences
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121572 (URN)
    Conference
    SSBA 2010
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2018-01-12Bibliographically approved
    8. Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
    Open this publication in new window or tab >>Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    A major challenge in today's molecular biology research is to understand the interaction between proteins at the molecular level. Cryo-electron tomography (ET) has come to play an important role in facilitating objective qualitative experiments on protein structures and their interaction. Various protein conformation structures can be qualitatively analysed as complete galleries of proteins are captured by ET. To facilitate fast and objective macromolecular structure analysis procedures, image processing has become a crucial tool. This paper presents an image processing system for localising individual proteins from in vitro samples imaged by ET. We have evaluated the system using simulated data as well as experimental data.

    Keywords
    Fuzzy set, watershed segmentation, distance transform, proteins
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121573 (URN)
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25
    9. Algorithms for Grey-Weighted Distance Computations
    Open this publication in new window or tab >>Algorithms for Grey-Weighted Distance Computations
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    With the increasing size of datasets and demand for real time response for interactive applications, improving runtime for algorithms with excessive computational requirements has become increasingly important. Many different algorithms combining efficient priority queues with various helper structures have been proposed for computing grey-weighted distance transforms. Here we compare the performance of popular competitive algorithms in different scenarios to form practical guidelines easy to adopt. The label-setting category of algorithms is shown to be the best choice for all scenarios. The hierarchical heap with a pointer array to keep track of nodes on the heap is shown to be the best choice as priority queue. However, if memory is a critical issue, then the best choice is the Dial priority queue for integer valued costs and the Untidy priority queue for real valued costs.

    Keywords
    Grey-weighted distance, Geodesic time, Geodesic distance, Fuzzy distance, Algorithms, Region growing
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-121578 (URN)
    Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25
  • 160. Gupta, Anindya
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images2017In: Image Analysis: Part I, Springer, 2017, p. 407-418Conference paper (Refereed)
    Abstract
  • 161.
    Gustavson, Stefan
    et al.
    Department of Science and Technology, Linköping University.
    Strand, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Anti-aliased Euclidean distance transform2011In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 32, no 2, p. 252-257Article in journal (Refereed)
    Abstract [en]

    We present a modified distance measure for use with distance transforms of anti-aliased, area sampled grayscale images of arbitrary binary contours. The modified measure can be used in any vector-propagation Euclidean distance transform. Our test implementation in the traditional SSED8 algorithm shows a considerable improvement in accuracy and homogeneity of the distance field compared to a traditional binary image transform. At the expense of a 10× slowdown for a particular image resolution, we achieve an accuracy comparable to a binary transform on a supersampled image with 16 × 16 higher resolution, which would require 256 times more computations and memory.

  • 162.
    Haindl, W
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lundqvist, R
    Som, S
    Mitchell, G
    Frawley, J
    A statistical analysis of cerebral cortical perfusion changes following carotid endarterectomy2001Other (Other scientific)
  • 163.
    Hamid Muhammed, H
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Characterizing and Estimating Fungal Disease Severity in Wheat2004In: Swedish Society for Automated Image Analysis Symposium - SSBA 2004, Ångströmlaboratoriet, Uppsala University, 2004, p. 194-198Conference paper (Other scientific)
  • 164.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Determination of Water Quality of Lake Erken, Sweden, by using Feature-Vector Based Analysis2001In: Irish Machine Vision and Image Processing Conference (IMVIP 2001), Maynooth, Ireland, 2001, p. 261-Conference paper (Other scientific)
  • 165.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis2001In: Irish Machine Vision and Image Processing Conference (IMVIP 2001), Maynooth, Ireland, 2001, p. 219-226Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel technique, which can be called Feature-Vector Based

  • 166.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks2003Conference paper (Refereed)
    Abstract [en]

    A new class of neuro-fuzzy systems, based on so-called Weighted Neural Networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) Weighted Neural Networks are presented and

  • 167.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks2002In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA, 2002Conference paper (Other scientific)
    Abstract [en]

    Segmenting hyperspectral images is an important task for simplifying the

  • 168.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Image Segmentation Using New Neuro-Fuzzy Systems2002In: Swedish Society for Automated Image Analysis Symposium - Proceedings of SSAB 2002, 2002, p. 83-87Conference paper (Other scientific)
  • 169.
    Hamid muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Using Hyperspectral Reflectance Data for Discrimination Between Healthyand Diseased Plants, and Determination of Damage-Level in Diseased Plants2002In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA., 2002Conference paper (Other scientific)
    Abstract [en]

    It has been proven, through many research works, that hyperspectral

  • 170.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering2002In: International Journal of Neural Systems (IJNS), Vol. 12, no 6, p. 425-434Article in journal (Refereed)
    Abstract [en]

    A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Networks (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces

  • 171.
    Hamid Muhammed, H.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    What is Feature­Vector Based Analysis?2001In: Swedish Society for Automated Image Analysis Symposium - SSAB 2001,ITN, Campus Norrköping, LinköpingUniversity, 2001, p. 131-134Conference paper (Other scientific)
  • 172.
    Hamid Muhammed, H.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ammenberg, P.
    Bengtsson, E.
    Using Feature-Vector Based Analysis, based on Principal Component Analysis andIndependent Component Analysis, for Analysing Hyperspectral Images2001In: 11th International Conference for Image Analysis and Processing (ICIAP2001), Palermo, Italy, 2001, p. 309-315Conference paper (Refereed)
    Abstract [en]

    A pixel in a hyperspectral image can be considered as a mixture of the reflectance

  • 173.
    Hamid Muhammed, H.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Larsolle, A.
    Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat2003In: Biosystems Engineering, Vol. 86, no 2, p. 125-134Article in journal (Refereed)
    Abstract [en]

    The impact of plant pathological stress on crop reflectance can be measured both in broad-band vegetation indices and in narrow or local characteristics of the reflectance spectra. This work is concerned with using the whole spectra in the objective exami

  • 174.
    Hamid Muhammed, Hamed
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat2005In: Biosystems Engineering, Vol. 91, no 1, p. 9-20Article in journal (Other (popular scientific, debate etc.))
    Abstract [en]

    Many studies have shown the usefulness of hyperspectral crop reflectance data for detecting plant pathological stress. However, there is still a need to identify unique signatures for specific stresses amidst the constantly changing background associated with normal crop growth and development. Comparing spatial and temporal patterns in crop spectra can provide such signatures. This work was concerned with characterising and estimating fungal disease severity in a spring wheat crop. This goal can be accomplished by using a reference data set consisting of hyperspectral crop reflectance data vectors and the corresponding disease severity field assessments. The hyperspectral vectors were first normalised into zero-mean and unit-variance vectors by performing various combinations of spectral- and band-wise normalisations. Then, after applying the same normalisation procedures to the new hyperspectral data, a nearest-neighbour classifier was used to classify the new data against the reference data. Finally, the corresponding stress signatures were computed using a linear transformation model. High correlation was obtained between the classification results and the corresponding field assessments of fungal disease severity, confirming the usefulness and efficiency of this approach. The effects of increased disease severity could be characterised by analysing the resulting disease signatures obtained when applying the different normalisation procedures. The low computational load of this approach makes it suitable for real-time on-vehicle applications.

  • 175.
    Hamid Muhammed, Hamed
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Hyperspectral Image Generation, Processing and Analysis2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.

    In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.

    Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.

    However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.

    Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.

    List of papers
    1. Sensitivity analysis of multi-channel images intended for spectrometry applications
    Open this publication in new window or tab >>Sensitivity analysis of multi-channel images intended for spectrometry applications
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93367 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    2. Using Multiple Colour Mosaics for Multi- and Hyperspectral Imaging
    Open this publication in new window or tab >>Using Multiple Colour Mosaics for Multi- and Hyperspectral Imaging
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93368 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    3. New approaches for surface water quality estimation in Lake Erken, Sweden, using remotely sensed hyperspectral data
    Open this publication in new window or tab >>New approaches for surface water quality estimation in Lake Erken, Sweden, using remotely sensed hyperspectral data
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93369 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    4. Industrial plume detection by employing spectral descriptive signatures for anomaly detection
    Open this publication in new window or tab >>Industrial plume detection by employing spectral descriptive signatures for anomaly detection
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93370 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    5. Using Feature Vector Based Analysis, based on Principal Component Analysis and Independent Component Analysis, for Analysing Hyperspectral Images
    Open this publication in new window or tab >>Using Feature Vector Based Analysis, based on Principal Component Analysis and Independent Component Analysis, for Analysing Hyperspectral Images
    2001 In: Proceedings of 11th International Conference for Image Analysis and Processing, p. 309-315Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93371 (URN)
    Available from: 2005-09-05 Created: 2005-09-05Bibliographically approved
    6. Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis
    Open this publication in new window or tab >>Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis
    2001 In: Proceedings of Irish Machine Vision and Image Processing Conference, p. 219-226Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93372 (URN)
    Available from: 2005-09-05 Created: 2005-09-05Bibliographically approved
    7. Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat
    Open this publication in new window or tab >>Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat
    2003 (English)In: Biosystems Engineering, Vol. 86, no 2, p. 125-134Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93373 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    8. Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat
    Open this publication in new window or tab >>Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat
    2005 (English)In: Biosystems Engineering, Vol. 91, no 1, p. 9-20Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93374 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    9. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application on disease severity and amount of plant density
    Open this publication in new window or tab >>Measuring crop status using multivariate analysis of hyperspectral field reflectance with application on disease severity and amount of plant density
    2005 (English)In: Proceedings of 5th European Conference on Precision Agriculture, Vol. Precision Agriculture ’05, p. 217-225Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93375 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    10. Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
    Open this publication in new window or tab >>Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
    2002 (English)In: International Journal of Neural Systems, Vol. 12, no 6, p. 425-434Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93376 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    11. Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
    Open this publication in new window or tab >>Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
    2004 (English)In: International Journal of Neural Systems, Vol. 14, no 6, p. 355-371Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93377 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    12. Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks
    Open this publication in new window or tab >>Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks
    2003 (English)In: Proceedings of 12th International Conference for Image Analysis and Processing, p. 308-313Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93378 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
    13. Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks
    Open this publication in new window or tab >>Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks
    2002 (English)In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA, 2002Conference paper, Published paper (Other scientific)
    Abstract [en]

    Segmenting hyperspectral images is an important task for simplifying the

    Keywords
    Hyperspectral images, Unsupervised Image Segmentation, Unsupervised Fuzzy Clustering, Neuro-Fuzzy Systems, Weighted IncrementalNeural Network (WINN), Watersheds.
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-42446 (URN)
    Available from: 2005-08-25 Created: 2005-08-25 Last updated: 2018-01-11
    14. A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos
    Open this publication in new window or tab >>A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos
    2002 (English)In: Proceedings of PCV'02: PHOTOGRAMMETRIC COMPUTER VISION 2002Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93380 (URN)
    Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
  • 176.
    Hamid Muhammed, Hamed
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks2004In: International Journal of Neural Systems (IJNS), Vol. 14, no 6, p. 355-371Article in journal (Refereed)
    Abstract [en]

    A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information bout the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.

  • 177.
    Hamid Muhammed, Hamed
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bergholm, Fredrik
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Camera-spectrometer for instantaneous multi- and hyperspectral imaging2005In: 5th European Conference on Precision Agriculture, 2005, p. 1008-Conference paper (Other scientific)
    Abstract [en]

    The most serious limitation of conventional multi- and hyperspectral imagery systems is the need for scanning time to be able to acquire the whole image cube, which contains huge amount of data needing large memory-capacity and giving rise to another serious limitation of these systems. In this work, a novel cost-effective technique, that solves the problems mentioned above, is presented. The system has no moving parts and the whole multi- or hyperspectral image cube is acquired instantaneously, making it ready to record multi- and hyperspectral digital video.

  • 178.
    Hamid Muhammed, Hamed
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bergholm, Fredrik
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Camera-spectrometer for multi- and hyperspectral imaging2005In: Swedish Society for Automated Image Analysis Symposium - SSBA 2005, 2005, p. 45-48Conference paper (Other scientific)
    Abstract [en]

    This paper presents a novel approach for modifying an

    ordinary digital camera to be able to generate multior

    hyperspectral images. The basic idea is to use

    miniature (spatially and spectrally) overlapping colormosaic

    filters with a spatial resolution which is as

    close as possible to the actual resolution of the sensor

    plate. What is new here is that common (cheap)

    printing techniques can be used to produce these filter

    mosaics. The resulting image which shows locally

    filtered scene areas, can be transformed into a

    hyperspectral image by considering each group of

    neighboring pixels and transforming it into a single

    image element in the final hyperspectral image.

  • 179.
    Hast, A.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, T.
    Bengtsson, E.
    Approximated Phong Shading by using the Euler Method2001Conference paper (Refereed)
    Abstract [en]

    After almost three decades and several improvements, Gouraud shading is still

  • 180.
    Hast, A.
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, T.
    Bengtsson, E.
    Improved Shading Performance by Avoiding Vector Normalization2001In: Conference for Promotion of Research in IT at New Universities and at University Colleges in Sweden, 2001, p. 73-85Conference paper (Other scientific)
  • 181.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Robust and Invariant Phase Based Local Feature Matching2014In: 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, p. 809-814Conference paper (Refereed)
    Abstract [en]

    Any feature matching algorithm needs to be robust, producing few false positives but also needs to be invariant to changes in rotation, illumination and scale. Several improvements are proposed to a previously published Phase Correlation based algorithm, which operates on local disc areas, using the Log Polar Transform to sample the disc neighborhood and the FFT to obtain the phase. It will be shown that the matching can be done in the frequency domain directly, using the Chi-squared distance, instead of computing the cross power spectrum. Moreover, it will be shown how combining these methods yields an algorithm that sorts out a majority of the false positives. The need for a peak to sub lobe ratio computation in order to cope with sub pixel accuracy will be discussed as well as how the FFT of the periodic component can enhance the matching. The result is a robust local feature matcher that is able to cope with rotational, illumination and scale differences to a certain degree.

  • 182.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Simple filter design for first and second order derivatives by a double filtering approach2014In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 42, p. 65-71Article in journal (Refereed)
    Abstract [en]

    Spline filters are usually implemented in two steps, where in the first step the basis coefficients are computed by deconvolving the sampled function with a factorized filter and the second step reconstructs the sampled function. It will be shown how separable spline filters using different splines can be constructed with fixed kernels, requiring no inverse filtering. Especially, it is discussed how first and second order derivatives can be computed correctly using cubic or trigonometric splines by a double filtering approach giving filters of length 7.

  • 183.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Towards Automatic Stereo Pair Extraction for 3D Visualisation of Historical Aerial Photographs2014In: IC3D, the International Conference on 3D Imaging, 2014, p. 1-8Conference paper (Refereed)
    Abstract [en]

    An efficient and almost automatic method for stereo pair extraction of aerial photos is proposed. There are several challenging problems that needs to be taken into consideration when creating stereo pairs from historical aerial photos. These problems are discussed and solutions are proposed in order to obtain an almost automatic procedure with as little input as possible needed from the user.  The result is a rectified and illumination corrected stereo pair. It will be discussed why viewing aerial photos in stereo is important since the depth cue gives more information than single photos do.

  • 184.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, Tony
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A modified Phong-Blinn light model for shadowed areas2003In: Graphics programming methods / [ed] Jeff Lander, Hingham: Charles River Media , 2003, p. 231-235Chapter in book (Refereed)
  • 185.
    Hast, Anders
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, Tony
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A modified Phong-Blinn light model for shadowed areas2003In: Proceedings of 3rd conference for the promotion of research in IT, 2003Conference paper (Other scientific)
  • 186.
    Hast Anders, Barrera Tony, Bengtsson Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    A ntialiasing for bump maps and a fast normalization trick2003Chapter in book (Refereed)
  • 187.
    Hast Anders, Barrera Tony, Bengtsson Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Fast setup for bilinear and biquadratic interpolation over triangles2003Chapter in book (Refereed)
  • 188.
    Hast Anders, Barrera Tony, Bengtsson Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Improved Bump Mapping by using Quadratic Vector Interpolation2002Conference paper (Refereed)
  • 189.
    Hast, Anders
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, Tony
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Incremental Spherical Interpolation with Quadratically Varying Angle2006In: SIGRAD 2006. The Annual SIGRAD Conference, Special Theme: Computer Games, November 22–23, 2006, Skövde, Sweden, 2006Conference paper (Refereed)
    Abstract [en]

    Spherical linear interpolation has got a number of important applications in computer graphics. We show how spherical interpolation can be performed efficiently even for the case when the angle vary quadratically over the interval. The computation will be fast since the implementation does not need to evaluate any trigonometric functions in the inner loop. Furthermore, no renormalization is necessary and therefore it is a true spherical interpolation. This type of interpolation, with non equal angle steps, should be useful for animation with accelerating or decelerating movements, or perhaps even in other types of applications.

  • 190.
    Hast, Anders
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Barrera, Tony
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Reconstruction Filters for Bump Mapping2002In: Proceedings from Promote IT 2002, 2002, p. 244-256Conference paper (Other scientific)
  • 191.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Capurro, Carlotta
    Nollet, Dries
    Pletinckx, Daniel
    Estevez, B.
    Pazos, M.
    Franco, J. M.
    Marchetti, Andrea
    Stereo Visualisation of Historical Aerial Photos: A Useful and Important Aerial Archeology Research Tool2016Conference paper (Refereed)
  • 192.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fornés, Alicia
    Univ Autonoma Barcelona, Comp Vis Ctr, Dept Comp Sci, Bellaterra, Spain.
    A segmentation-free handwritten word spotting approach by relaxed feature matching2016In: Proc. 12th IAPR Workshop on Document Analysis Systems, IEEE, 2016, p. 150-155Conference paper (Refereed)
    Abstract [en]

    The automatic recognition of historical handwritten documents is still considered a challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.

  • 193.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Marchetti, Andrea
    Improved illumination correction that preserves medium sized objects2014In: Machine Graphics & Vision, ISSN 1230-0535, Vol. 23, no 1/2, p. 3-20Article in journal (Refereed)
  • 194.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Marchetti, Andrea
    IIT, CNR.
    Invariant Interest Point Detection Based on Variations of the Spinor Tensor2014In: WSCG, Communication papers proceedings , ISBN 978-80-86943-71-8, 2014, p. 49-56Conference paper (Refereed)
    Abstract [en]

    Image features are obtained by using some kind of interest point detector, which often is based on a symmetric matrix such as the structure tensor or the Hessian matrix. These features need to be invariant to rotation and to some degree also to scaling in order to be useful for feature matching in applications such as image registration. Recently, the spinor tensor has been proposed for edge detection. It was investigated herein how it also can be used for feature matching and it will be proven that some simplifications, leading to variations of the response function based on the tensor, will improve its characteristics. The result is a set of different approaches that will be compared to the well known methods using the Hessian and the structure tensor. Most importantly the invariance when it comes to rotation and scaling will be compared.

  • 195.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Marchetti, Andrea
    IIT, CNR.
    Rotation Invariant Feature Matching - Based on Gaussian Filtered Log Polar Transform and Phase Correlation.2013In: 8th International Symposium on Image and Signal Processing and Analysis: (ISPA 2013), 2013, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Rotation invariance is an important property for any feature matching method and it has been implemented in different ways for different methods. The Log Polar Transform has primarily been used for image registration where it is applied after phase correlation, which in its turn is applied on the whole images or in the case of template matching, applied on major parts of them followed by an exhaustive search. We investigate how this transform can be used on local neighborhoods of features and how phase correlation as well as normalized cross correlation can be applied on the result. Thus, the order is reversed and we argue why it is important to do so. We demonstrate a common problem with the log polar transform and that many implementations of it are not suitable for local feature detectors. We propose an implementation of it based on Gaussian filtering. We also show that phase correlation generally will perform better than normalized cross correlation. Both handles illumination differences well, but changes in scale is handled better by the phase correlation approach. 

  • 196.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Marchetti, Andrea
    The Challenges and Advantages with a Parallel Implementation of Feature Matching2016In: Proc. 11th International Conference on Computer Vision Theory and Applications, 2016Conference paper (Refereed)
  • 197.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nysjö, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Optimal RANSAC - Towards a Repeatable Algorithm for Finding the Optimal Set2013In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 21, no 1, p. 21-30Article in journal (Refereed)
  • 198.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sablina, Victoria A.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kylberg, Gustaf
    A fast Fourier based feature descriptor and a cascade nearest neighbour search with an efficient matching pipeline for mosaicing of microscopy images2018In: Pattern Recognition and Image Analysis, ISSN 1054-6618, Vol. 28, no 2, p. 261-272Article in journal (Refereed)
  • 199.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Vats, Ekta
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Radial line Fourier descriptor for historical handwritten text representation2018In: Proc. 26th International Conference on Computer Graphics: Visualization and Computer Vision, 2018Conference paper (Other academic)
    Abstract [en]

    Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.

  • 200.
    Homman M., Sintorn I., Hultenby K., Borgefors G., Söderberg-Naucler C.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nuclear egress of human Cytomegalovirus capsids by budding through thenuclear membrane,2002In: Proc. Int. Conf. on Electron Microscopy, Durban, South Africa, 2002., 2002Conference paper (Other scientific)
1234567 151 - 200 of 557
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