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  • 651.
    Wählby, Carolina
    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.
    Erlandsson, Fredrik
    Nyberg, Karl
    Lindblad, Joakim
    Zetterberg, Anders
    Bengtsson, Ewert
    Multiple tissue antigen analysis by sequential immunofluorescence staining and multi-dimensional image analysis2001In: Proceedings of the 12th Scandinavian Conference on Image Analysis (SCIA), 2001, p. 25–32-Chapter in book (Other academic)
    Abstract
  • 652.
    Wählby, Carolina
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Erlandsson, Fredrik
    Zetterberg, Anders
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Multi-dimensional image analysis of sequential immunofluorescence staining2001In: 7th European Society for Analytical Cellular Pathology Congress (ESACP 2001), Caen, France, 2001, p. 61-Conference paper (Other scientific)
  • 653.
    Wählby, Carolina
    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.
    Karlsson, Patrick
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Henriksson, Sara
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Larsson, Chatarina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nilsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Finding cells, finding molecules, finding patterns2006In: Advances in Data Mining: Workshop on Mass-Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry, MDA´2006, Leipzig/Germany, 2006, p. 15-24Conference paper (Refereed)
    Abstract [en]

    Many modern molecular labeling techniques result in bright point signals. Signals from molecules that are detected directly inside a cell can be captured by fluorescence microscopy. Signals representing different types of molecules may be randomly distributed in the cells or show systematic patterns indicating that the corresponding molecules have specific, non-random localizations and functions in the cell. Assessing this information requires high speed robust image segmentation followed by signal detection, and finally pattern analysis. We present and discuss this type of methods and show an example of how the distribution of different variants of mitochondrial DNA can be analyzed.

  • 654.
    Wählby, Carolina
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Karlsson, Patrick
    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.
    Henriksson, Sara
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Larsson, Chatarina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nilsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    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.
    Finding cells, finding molecules, finding patterns2008In: International Journal of Signal and Imaging Systems Engineering, ISSN 1748-0698, Vol. 1, no 1, p. 11-17Article in journal (Refereed)
    Abstract [en]

    Many modern molecular labelling techniques result in bright point signals. Signals from molecules that are detected directly inside a cell can be captured by fluorescence microscopy. Signals representing different types of molecules may be randomly distributed in the cells or show systematic patterns, indicating that the corresponding molecules have specific, non-random localisations and functions in the cell. Assessing this information requires high speed robust image segmentation followed by signal detection, and finally, pattern analysis. We present and discuss these types of methods and show an example of how the distribution of different variants of mitochondrial DNA can be analysed.

  • 655.
    Wählby, Carolina
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Karlsson, Patrick
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Thorlin, Thorleif
    Althoff, Karin
    Degerman, Johan
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Gustavsson, Tomas
    Time-lapse microscopy and image analysis for tracking stem cell migration2004In: Proceedings of the Swedish Symposium on Image Analysis SSBA 2004, 2004, p. 118-121Conference paper (Other scientific)
  • 656.
    Wählby, Carolina
    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.
    Lindblad, Joakim
    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.
    Vondrus, Mikael
    Bengtsson, Ewert
    Björkesten, Lennart
    Algorithms for cytoplasm segmentation of fluorescence labeled cells2002In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 24, no 2-3, p. 101-111Article in journal (Refereed)
    Abstract [en]

    Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

  • 657.
    Wählby, Carolina
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nyström, Ingela
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Robust methods for image segmentation and measurements.2003In: Proceedings for Modern Methods for Quantitative Metallography, 2003Conference paper (Refereed)
  • 658.
    Wählby, Carolina
    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.
    Sintorn, Ida-Maria
    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.
    Erlandsson, Fredrik
    Borgefors, Gunilla
    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.
    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.
    Combining intensity, edge, and shape information for 2D and 3D segmentation of cell nuclei in tissue sections2004In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 215, no 1, p. 67-76Article in journal (Refereed)
    Abstract [en]

    We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.

  • 659.
    Wählby (nee Linnman), Carolina
    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.
    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, Centre for Image Analysis.
    Ekholm-Jensen, Susanna
    Zetterberg,, Anders
    Detection of fluorescent foci and evaluation of spatial relationships in 3D-fluorescence microscopy images of mammalian cells1999In: Analytical Cellular Pathology, Proceedings of 6th ESACP Congress in Heidelberg, April 7-11: Also in proceedings, SSAB Symposium on Image Analysis, March 9-10, 1999, Gothenburg, p. 57-60., 1999, p. 36-37Conference paper (Other academic)
  • 660.
    Wählby (nee Linnman), Carolina
    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.
    Lindblad, Joakim
    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, Centre for Image Analysis.
    Vondrus, Mikael
    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, Centre for Image Analysis.
    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, Centre for Image Analysis.
    Björkesten, Lennart
    Algorithms for cytoplasm segmentation of fluorescence labeled cells grown in micro-fabricated structures1999In: Amersham Pharmacia Biotech Uppsala R&D day, December 2, 1999., 1999Conference paper (Other (popular science, discussion, etc.))
    Abstract
  • 661.
    Wählby (née Linnman), Carolina
    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.
    Lindblad, Joakim
    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.
    Vondrus, Mikael
    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.
    Jarkrans, Torsten
    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.
    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.
    Björkesten, Lennart
    Automatic cytoplasm segmentation of fluorescence labelled cells2000In: Symposium on Image Analysis - SSAB 2000, 2000, p. 29-32Conference paper (Other academic)
  • 662. Zdravkovic, V
    et al.
    Carling, E
    Seipel, S
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hast, A
    Swedish Universities Prepare Students for Entry into Industry2002Other (Other (popular scientific, debate etc.))
  • 663.
    Zieba, Agata
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Hjelm, Fredrik
    Jordan, Lee
    Berg, Jonathan
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Pardali, Katerina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bright-Field Microscopy Visualization of Proteins and Protein Complexes by In Situ Proximity Ligation with Peroxidase Detection2010In: Clinical Chemistry, ISSN 0009-9147, E-ISSN 1530-8561, Vol. 56, no 1, p. 99-110Article in journal (Refereed)
    Abstract [en]

    BACKGROUND:

    The in situ proximity ligation assay (PLA) allows a protein or protein complex to be represented as an amplifiable DNA molecule. Recognition is mediated by proximity probes consisting of antibodies coupled with oligonucleotides. Upon dual binding of the proximity probes, the oligonucleotides direct the formation of a circular DNA molecule, which is then amplified by rolling-circle replication. The localized concatemeric product is then detected with fluorescent probes. The in situ PLA enables localized detection of individual native proteins or interacting protein pairs in fixed cells or tissue sections, thus providing an important tool for basic and clinical research.

    METHODS:

    We used horseradish peroxidase (HRP)conjugated oligonucleotides to couple in situ PLA with enzymatic visualization of the localized detection event.

    RESULTS:

    We demonstrate the detection of protein complexes, both in cells and in tissue sections, and show that we can quantify the complexes with image-analysis software specially developed for recognizing HRP signals in bright-field microscopy images. We show that fluorescence and HRP signals produce equivalent results, both ill cultured cells and in tissue samples.

    CONCLUSIONS:

    The combination of in situ PLA with bright-field detection and automated image analysis allows the signals present to be Counted in an automated fashion and thus provides a sensitive and specific method for quantification of proteins and protein complexes with bright-field microscopy. With this approach, in situ PLA can be used without the requirement for expensive fluorescence microscopes, thereby avoiding problems with nonspecific fluorescence while maintaining compatibility with conventional histologic staining.

  • 664.
    Zunic, Jovisa
    et al.
    Faculty of Technical Sciences, University of Novi Sad.
    Sladoje, Nataša
    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. Faculty of Technical Sciences, University of Novi Sad .
    A characterization of digital disks by discrete moments1997In: International Conference on Computer Analysis of Images and Patterns / [ed] Sommer G., Daniilidis K., Pauli J., Springer, 1997, p. 582-589Conference paper (Refereed)
  • 665.
    Zunic, Jovisa
    et al.
    Faculty of Technical Sciences, University of Novi Sad.
    Sladoje, Nataša
    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. Faculty of Technical Sciences, University of Novi Sad.
    Efficiency of Characterizing Ellipses and Ellipsoids by Discrete Moments2000In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 22, no 4, p. 407-414Article in journal (Refereed)
    Abstract [en]

    In this paper, our studies are focused on ellipses and problems related to their representation and reconstruction from the data resulting from their digitization. The main result of the paper is that a finite number of discrete moments, corresponded to digital ellipses, is in one-to-one correspondence with digital ellipses, which enables coding of digital ellipses with an asymptotically optimal amount of memory. In addition, the problem of reconstruction, based on the same parameters, is considered. Since the digitization of real shapes causes an inherent loss of information about the original objects, the precision of the original shape estimation from the corresponding digital data is limited. We derive a sharp upper bound for the errors in reconstruction of the center position and half-axes of the ellipse, in function of the applied picture resolution (i.e., the number of pixels per unit). An extension of these results to the 3D case is also given

  • 666.
    Åhlen, Julia
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Color correction of underwater images based on estimation of diffuse attenuation coefficients2003In: Proceedings of 3rd conference for the promotion of research in IT, 2003Conference paper (Other scientific)
  • 667.
    Åhlén, Julia
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Colour Correction of Underwater Images Using Spectral Data2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For marine sciences sometimes there is a need to perform underwater photography. Optical properties of light cause severe quality problems for underwater photography. Light of different energies is absorbed at highly different rates under water causing significant bluishness of the images. If the colour dependent attenuation under water can be properly estimated it should be possible to use computerised image processing to colour correct digital images using Beer’s Law.

    In this thesis we have developed such estimation and correction methods that have become progressively more complicated and more accurate giving successively better correction results. A process of estimation of downwelling attenuation coefficients from multi or hyper spectral data is a basis for automatic colour restoration of underwater taken images. The results indicate that for each diving site the unique and precise coefficients can be obtained.

    All standard digital cameras have built in white balancing and colour enhancement functions designed to make the images as aesthetically pleasing as possible. These functions can in most cameras not be switched off and the algorithms used are proprietary and undocumented. However, these enhancement functions can be estimated. Applying their reverse creates un-enhanced images and we show that our algorithms for underwater colour correction works significantly better when applied to such images.

    Finally, we have developed a method that uses point spectra from the spectrometer together with RGB colour images from a camera to generate pseudo-hyper-spectral images. Each of these can then be colour corrected. Finally, the images can be weighted together in the proportions needed to create new correct RGB images. This method is somewhat computationally demanding but gives very encouraging results.

    The algorithms and applications presented in this thesis show that automatic colour correction of underwater images can increase the credibility of data taken underwater for marine scientific purposes.

    List of papers
    1. Color Correction of Underwater Images Based on Estimation of Diffuse Attenuation Coefficients
    Open this publication in new window or tab >>Color Correction of Underwater Images Based on Estimation of Diffuse Attenuation Coefficients
    2003 In: Proceedings of the PICS 2003: The PICS Conference, An International Technical Conference on The Science and Systems of Digital Photography, including the Fifth International Symposium on Multispectral Color Science, ISSN 0-89208-245-3, p. 325-329Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93762 (URN)
    Available from: 2005-11-25 Created: 2005-11-25Bibliographically approved
    2. Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images
    Open this publication in new window or tab >>Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images
    2003 (English)In: Lecture Notes in Computer Science. Proceedings of the 13th Scandinavian Conference on Image Analysis (SCIA), ISSN 0302-9743, Vol. 2749, p. 922-926Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93763 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
    3. Improvement of a Color Correction Algorithm for Underwater Images Through Compensating for Digital Camera Behaviour
    Open this publication in new window or tab >>Improvement of a Color Correction Algorithm for Underwater Images Through Compensating for Digital Camera Behaviour
    2004 (English)In: Proceedings of the Swedish Symposium on Image Analysis, p. 142-145Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93764 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
    4. Pre-Processing of Underwater Images Taken in Shallow Water for Color Reconstruction Purposes
    Open this publication in new window or tab >>Pre-Processing of Underwater Images Taken in Shallow Water for Color Reconstruction Purposes
    2005 (English)In: Proceedings of the 7th IASTED International Conference on Signal and Image ProcessingArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93765 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
    5. Dissolved Organic Matters Impact on Colour Reconstruction in Underwater Images
    Open this publication in new window or tab >>Dissolved Organic Matters Impact on Colour Reconstruction in Underwater Images
    2005 (English)In: Lecture Notes in Computer Science. Proceedings of the 14th Scandinavian Conference on Image Analysis, ISSN 3-540-26320-9, Vol. 3540, p. 1148-1156Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-93766 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
    6. Evaluation of Underwater Spectral Data for Colour Correction Applications
    Open this publication in new window or tab >>Evaluation of Underwater Spectral Data for Colour Correction Applications
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93767 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
    7. Application of Underwater Spectral Data for Colour Correction Purposes
    Open this publication in new window or tab >>Application of Underwater Spectral Data for Colour Correction Purposes
    (English)Article in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-93768 (URN)
    Available from: 2005-11-25 Created: 2005-11-25 Last updated: 2010-03-01Bibliographically approved
  • 668.
    Åhlén, Julia
    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.
    Bengtsson, Ewert
    Evaluation of Underwater Spectral Data for Colour Correction ApplicationsArticle in journal (Refereed)
  • 669.
    Åhlén, Julia
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Bengtsson, Ewert
    Lindell, Tommy
    Color Correction of Underwater Images Based on Estimation of Diffuse Attenuation Coefficients2003In: Proceedings of the PICS 2003: The PICS Conference, An International Technical Conference on The Science and Systems of Digital Photography, including the Fifth International Symposium on Multispectral Color Science, ISSN 0-89208-245-3, p. 325-329Article in journal (Refereed)
  • 670.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Early Recognition of Smoke in Digital Video2010In: Proceedings of European Conference of Computer Science (ECCS'10) / [ed] Valeri Mladenov and Kleanthis Psarris and Nikos Mastorakis and Amauri Caballero and George Vachtsevanos, WSEAS Press , 2010, p. 301-307Conference paper (Refereed)
  • 671.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    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.
    Prediction of Ventilation Paths in Urban Environments using Digitized Maps2009In: Proceedings of the IADIS International Conference Applied Computing 2009, 2009, p. 217-221Conference paper (Refereed)
    Abstract [en]

    Urban populations and the density of settlements constantly become higher. This leads to higher energy consumption and generally to deterioration of life comfort. Study of urban heat and cool islands is of great importance for social community planners and building engineers. Ventilation paths are defined by turbulent mass, momentum and energy transport conditions and can thus be modeled. This area is usually studied by measurements of the conditions and air flows in laboratory environments. This paper presents a method for the prediction of free ventilation paths in a small city using digital imagery. A digitized map created from a eographic data base is used as input. Image analysis is performed in order to create an optimal edge image. A modified Hough transform is applied. Points of interest are defined and surroundings are calculated. These points are inputs to a parameter space. As a result a free wind passage is predicted based on the position of the observer. Prediction is done by calculation of possible straight lines in a parameter space. Finally, the method is verified by comparison with position vectors from the same space in the image and the best fitted path is chosen.

  • 672.
    Åhlén, Julia
    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.
    Sundgren, David
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003In: Lecture Notes in Computer Science. Proceedings of the 13th Scandinavian Conference on Image Analysis (SCIA), ISSN 0302-9743, Vol. 2749, p. 922-926Article in journal (Refereed)
  • 673.
    Åhlén Julia, Sundgren David
    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.
    Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images2003In: 13th Scandinavian Conference, SCIA 2003 Göteborg, Sweden, June 29-July 2, 2003, 2003, p. 922-926Conference paper (Refereed)
  • 674.
    Åhlén, Julia
    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.
    Sundgren, David
    Improvement of a Color Correction Algorithm for Underwater Images Through Compensating for Digital Camera Behaviour2004In: Proceedings of the Swedish Symposium on Image Analysis, p. 142-145Article in journal (Refereed)
  • 675.
    Åhlén, Julia
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Sundgren, David
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Application of underwater hyperspectral data for color correction purposes2007In: Pattern Recognition and Image Analysis, ISSN 1054-6618 (Print) 1555-6212 (Online), Vol. 17, no 1, p. 170-173Article in journal (Refereed)
    Abstract [en]

    Color correction of underwater images has been considered a difficult task for a number of reasons. Those include severe absorption of the water column, the unpredictable behavior of light under the water surface, limited access to reliable data for correction purposes, and the fact that we are only able to process three spectral channels, which is insufficient for most color correction applications. Here, the authors present a method to estimate a hyperspectral image from an RGB image and pointwise hyperspectral data. This is then used to color correct the hyperspectral underwater image and transform it back into RGB color space.

  • 676.
    Åhlén, Julia
    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.
    Sundgren, David
    Bengtsson, Ewert
    Application of Underwater Spectral Data for Colour Correction PurposesArticle in journal (Refereed)
  • 677.
    Åhlén, Julia
    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.
    Sundgren, David
    Bengtsson, Ewert
    Pre-Processing of Underwater Images Taken in Shallow Water for Color Reconstruction Purposes2005In: Proceedings of the 7th IASTED International Conference on Signal and Image ProcessingArticle in journal (Refereed)
  • 678.
    Åhlén, Julia
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sundgren, David
    KTH.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Pre-Processing of Underwater Images Taken in shallow Water for Color Reconstruction Purposes2005In: IASTED Proceeding (479): IASTED 7th Conference on Signal and Image Processing - 2005, 2005Conference paper (Refereed)
    Abstract [en]

    Coral reefs are monitored with different techniques in or der to examine their health. Digital cameras, which pro vide an economically defendable tool for marine scientists to collect underwater data, tend to produce bluish images due to severe absorption of light at longer wavelengths. In this paper we study the possibilities of correcting for this color distortion through image processing. The decrease of red light by depth can be predicted by Beer’s Law. An other parameter that has been taken into account is the image enhancement functions built into the camera. We use a spectrometer and a reflectance standard to obtain the data needed to approximate the joint effect of these func tions. This model is used to pre-process the underwater images taken by digital cameras so that the red, green and blue channels show correct values before the images are subjected to correction for the effects of the water column through application of Beer’s Law. This process is fully automatic and the amount of processed images is limited only by the speed of computer system. Experimental re sults show that the proposed method works well for cor recting images taken at different depths with two different cameras.

  • 679.
    Åhlén, Julia
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Sundgren, David
    KTH.
    Lindell, Tommy
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Dissolved Organic Matters Impact on Colour2005In: Image Analysis: 14th Scandinavian Conference, SCIA 2005, 2005, p. 1148-1156Conference paper (Refereed)
    Abstract [en]

    The natural properties of water column usually affect under-water

    imagery by suppressing high-energy light. In application such as

    color correction of underwater images estimation of water column parameters is crucial. Diffuse attenuation coefficients are estimated and used for further processing of underwater taken data. The coefficients will give information on how fast light of different wavelengths decreases with increasing depth. Based on the exact depth measurements and data from a spectrometer the calculation of downwelling irradiance will be done. Chlorophyll concentration and a yellow substance factor contribute to a great variety of values of attenuation coefficients at different depth. By taking advantage of variations in depth, a method is presented to

    estimate the in uence of dissolved organic matters and chlorophyll on color correction. Attenuation coefficients that depends on concentration of dissolved organic matters in water gives an indication on how well any spectral band is suited for color correction algorithm.

  • 680.
    Åhlén, Julia
    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.
    Sundgren, David
    Lindell, Tommy
    Bengtsson, Ewert
    Dissolved Organic Matters Impact on Colour Reconstruction in Underwater Images2005In: Lecture Notes in Computer Science. Proceedings of the 14th Scandinavian Conference on Image Analysis, ISSN 3-540-26320-9, Vol. 3540, p. 1148-1156Article in journal (Refereed)
  • 681.
    Östlund, C
    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, Biology, Department of Ecology and Evolution, Limnology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Flink, P
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Evolution, Limnology.
    Strömbeck, N
    Pierson, D
    Lindell, T
    Mapping of the water quality of Lake Erken, Sweden, from Imaging Spectrometry and Landsat Thematic Mapper2001In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 268, no 1-3, p. 139-154Article in journal (Refereed)
    Abstract [en]

    Hyperspectral data have been collected by the Compact Airborne Spectrographic Imager (CASI) and multispectral data by the Landsat Thematic Mapper (TM) instrument for the purpose of mapping lake water quality. Field campaigns have been performed on Lake Erken

  • 682.
    Östlund, Catherine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Analysis of Imaging Spectrometer Data with Lake Environment Applications1999Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In this thesis the image processing and analysis aspects of imaging spectrometer (IS) data have been investigated for water and wetland applications. The Compact Airborne Spectrographic Imager (CASI) has been the main instrument in the evaluations. To fully benefit from the high spectral and spatial resolution data in the analysis phase, the preprocessing of data, is important and has been a focus of this thesis. To restore, improve and evaluate the data, the radiometric calibration, wavelength band positioning, noise and other radiometric anomalies, geometric calibration and atmospheric calibration have been studied. Existing methods have been evaluated, and new ones proposed, and the most appropriate methods applied to the data.

    On the image analysis aspects of hyperspectral data sets, spatial true physical structures in the images were studied using data compression and segmentation methods, and a new technique combining compression and colour transformation. The latter was shown to be a fast and objective method to visualise the spatial structures in a large data set.

    The usefulness of IS data in water quality applications was evaluated developing statistical relationships between image data and data collected in the field. A comprehensive in situ data set, collected along a transect in Lake Erken, Sweden, during a bloom of the cyanobacteria Gloeotrichia echinulata was used. It was found that a correlation of the image data to chlorophyll a and phaeophytine a could be established, but also that the preprocessing of images is important, and that the dynamic character of water is a complicating factor. Aquatic macrophytes in Lake Mälaren, Sweden, were classified. IS data was found to be powerful for these kinds of applications, but the analysis suffered from poor data.

  • 683.
    Östlund, Catherine
    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.
    The Coast of the South-East Mediterrenian - El Alamein to Haifa1997In: Third International Airborne Remote Sensing Conference and Exhibition, TERIM INternational Inc. , 1997, p. 526-533Conference paper (Refereed)
11121314 651 - 683 of 683
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