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  • 1.
    Abrate, Matteo
    et al.
    CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    Bacciu, Clara
    CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    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. CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    Marchetti, Andrea
    CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    Minutoli, Salvatore
    CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    Tesconi, Maurizio
    CNR Natl Res Council, Inst Informat & Telemat, I-56124 Pisa, Italy.
    Geomemories - A Platform for Visualizing Historical, Environmental and Geospatial Changes of the Italian Landscape2013In: ISPRS International Journal of Geo-Information. Special issue: Geospatial Monitoring and Modelling of Environmental Change, ISSN 2220-9964, Vol. 2, no 2, p. 432-455Article in journal (Refereed)
    Abstract [en]

    The GeoMemories project aims at publishing on the Web and digitally preserving historical aerial photographs that are currently stored in physical form within the archives of the Aerofototeca Nazionale in Rome. We describe a system, available at http://www.geomemories.org, that lets users visualize the evolution of the Italian landscape throughout the last century. The Web portal allows comparison of recent satellite imagery with several layers of historical maps, obtained from the aerial photos through a complex workflow that merges them together. We present several case studies carried out in collaboration with geologists, historians and archaeologists, that illustrate the great potential of our system in different research fields. Experiments and advances in image processing technologies are envisaged as a key factor in solving the inherent issue of vast amounts of manual work, from georeferencing to mosaicking to analysis.

  • 2. Amelio, Alessia
    et al.
    Borgefors, GunillaHast, AndersUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Visual Pattern Extraction and Recognition for Cultural Heritage Understanding: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understandingco-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020)2020Collection (editor) (Refereed)
  • 3.
    Azar, Jimmy C.
    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.
    Simonsson, Martin
    Bengtsson, Ewert
    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.
    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.
    Image segmentation and identification of paired antibodies in breast tissue2014In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, p. 647273:1-11Article in journal (Refereed)
    Abstract [en]

    Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

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  • 4.
    Azar, Jimmy
    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.
    Simonsson, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    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 Glandular Tissue by Statistical Proximity Sampling2015In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, article id 943104Article in journal (Refereed)
    Abstract [en]

    Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.

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  • 5.
    Barrera, Tony
    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.
    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.
    Bengtsson, Ewert
    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.
    A chronological and mathematical overview of digital circle generation algorithms: Introducing efficient 4- and 8-connected circles2016In: International Journal of Computer Mathematics, ISSN 0020-7160, E-ISSN 1029-0265, Vol. 93, no 8, p. 1241-1253Article in journal (Refereed)
    Abstract [en]

    Circles are one of the basic drawing primitives for computers and while the naive way of setting up an equation for drawing circles is simple, implementing it in an efficient way using integer arithmetic has resulted in quite a few different algorithms. We present a short chronological overview of the most important publications of such digital circle generation algorithms. Bresenham is often assumed to have invented the first all integer circle algorithm. However, there were other algorithms published before his first official publication, which did not use floating point operations. Furthermore, we present both a 4- and an 8-connected all integer algorithm. Both of them proceed without any multiplication, using just one addition per iteration to compute the decision variable, which makes them more efficient than previously published algorithms.

  • 6. Barrera, Tony
    et al.
    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.
    Bengtsson, Ewert
    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.
    An Algorithm for Parallel Calculation of Trigonometric and Exponential Functions2013In: ACM International Conference on Computing Frontiers, 2013Conference paper (Refereed)
  • 7.
    Chanda, Sukalpa
    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.
    Chakrapani, Asish
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    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.
    Pal, Umapada
    Doermann, David
    Face Recognition - A One-Shot Learning Perspective2019In: 15th IEEE Conference on Signal Image Technology and Internet based Systems, 2019, p. 113-119Conference paper (Refereed)
    Abstract [en]

    Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.

  • 8.
    Chanda, Sukalpa
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    P.K, Prasad
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Mårtesson, Lasse
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
    Pal, Umapada
    Finding Logo and Seal in Historical Document Images -: An Object Detection based Approach2019In: The 5th Asian Conference on Pattern Recognition (ACPR 2019), 2019, p. 821-834Conference paper (Refereed)
    Abstract [en]

    Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.

  • 9. Estevez, B.
    et al.
    Pazos, M.
    Franco, J. M.
    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.
    Recreating with photogrammetric techniques a submerged megalithic landscape: the case of the salas reservoir2015In: 3 Encontro Internacional de Arqueoloxia de Vilalba, 2015, p. 9-16Conference paper (Refereed)
  • 10.
    Hast, Anders
    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.
    3D Stereoscopic Rendering: An Overview of Implementation Issues2010In: Game Engine Gems / [ed] Eric Lengyel, 2010, p. 123-138Chapter in book (Other (popular science, discussion, etc.))
  • 11.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers2023In: Advanced Concepts for Intelligent Vision Systems / [ed] Jaques Blanc-Talon; Patrice Delmas; Wilfried Philips; Paul Scheunders, Springer Nature, 2023, p. 88-99Conference paper (Refereed)
    Abstract [en]

    One of the major difficulties in face recognition while comparing photographs of individuals of different ages is the influence of age progression on their facial features. As a person ages, the face undergoes many changes, such as geometrical changes, changes in facial hair, and the presence of glasses, among others. Although biometric markers like computed face feature vectors should ideally remain unchanged by such factors, face recognition becomes less reliable as the age range increases. Therefore, this investigation was carried out to examine how the use of Embedded Prototype Subspace Classifiers could improve face recognition accuracy when dealing with age-related variations using face feature vectors only.

  • 12.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall2023In: Image Analysis and Processing – ICIAP 2023, Springer Nature, 2023, Vol. 14233, p. 159-170Conference paper (Refereed)
    Abstract [en]

    Efficient face image retrieval, i.e. searching for existing  photographs of a person in unlabelled photo collections using a query photo, is  evaluated for a novel method to find the top $n$ results for Consensus Ranking. The approach aims to maximise precision and recall by using the retrieved photos, all ranked on similarity. The proposed method aims to retrieve all photos of the queried person while excluding images of other individuals. To achieve this, the method uses the top n results as temporary queries, recalculates similarities, and combines the obtained ranked lists to produce a better overall ranking. The method includes a novel and reliable procedure for selecting $n$, which is evaluated on two datasets, and considers the impact of age variation in the datasets.

  • 13.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Consensus Ranking for Increasing Mean Average Precision in Keyword Spotting2020In: Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding. co-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020) Bari, Italy, January 29, 2020., 2020, Vol. 2602, p. 46-57Conference paper (Refereed)
    Abstract [en]

    Word spotting use a query word image to find any instances of that word among document images. The obtained list of words is ranked according to similarity to the query word. Ideally, any false positives should only occur in the end of that list. However, in reality they often occur higher up, which decreases the so called mean average precision. It is shown how creating new ranked lists by re-scoring using the top n occurrences in the original list, and then fusing the scores, can increase the mean average precision.

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  • 14.
    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.
    How to Promote Student Creativity and Learning using Tutorials in Teaching Graphics and Visualisation2014In: Proc. 16th International Conference on Geometry and Graphics, Innsbruck University Press, 2014, p. 626-633Conference paper (Other academic)
  • 15.
    Hast, Anders
    Uppsala University, Interfaculty Units, Centre for Image Analysis.
    Improved Fundamental Algorithms for Fast Computer Graphics2002Licentiate thesis, monograph (Other scientific)
  • 16.
    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.
    Interest Point Detection Based on the Extended Structure Tensor with a Scale Space Parameter2015In: International Conference on Computer Vision Theory and Applications, 2015, p. 1-8Conference paper (Refereed)
  • 17.
    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.
    Magnitude of Semicircle Tiles in Fourier-space: A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers2022In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 30, no 1-2, p. 82-90Article in journal (Refereed)
    Abstract [en]

    The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edges in an image. In comparison to Histogram of Oriented Gradients, the proposed feature descriptor performs better in this scenario. Compression using PCA turns out to be able to increase both the F1-score as well as decreasing the Variability.

  • 18.
    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.
    Mathematics + Computer Science = True2015Conference paper (Refereed)
  • 19.
    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.

  • 20.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sex Classification of Face Images using Embedded Prototype Subspace Classifiers.2023In: Proceedings of the 31th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision WSCG 2023 / [ed] Vaclav Skala, Czech Republic, 2023, p. 43-52Conference paper (Refereed)
    Abstract [en]

    In recent academic literature Sex and Gender have both become synonyms, even though distinct definitions doexist. This give rise to the question, which of those two are actually face image classifiers identifying? It will beargued and explained why CNN based classifiers will generally identify gender, while feeding face recognitionfeature vectors into a neural network, will tend to verify sex rather than gender. It is shown for the first time howstate of the art Sex Classification can be performed using Embedded Prototype Subspace Classifiers (EPSC) andalso how the projection depth can be learned efficiently. The automatic Gender classification, which is producedby the InsightFace project, is used as a baseline and compared to the results given by the EPSC, which takes thefeature vectors produced by InsightFace as input. It turns out that the depth of projection needed is much largerfor these face feature vectors than for an example classifying on MNIST or similar. Therefore, one importantcontribution is a simple method to determine the optimal depth for any kind of data. Furthermore, it is shown howthe weights in the final layer can be set in order to make the choice of depth stable and independent of the kind oflearning data. The resulting EPSC is extremely light weight and yet very accurate, reaching over 98% accuracy forseveral datasets.

  • 21.
    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.

  • 22.
    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)
  • 23.
    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)
  • 24.
    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.

  • 25.
    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)
  • 26.
    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)
  • 27.
    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.
    Cullhed, Per
    Uppsala University, University Library.
    Vats, Ekta
    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.
    TexT – Text extractor tool for handwritten document transcription and annotation2018In: Digital Libraries and Multimedia Archives, Springer, 2018, p. 81-92Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, TexT for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. TexT is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of TexT is demonstrated on an archival manuscript collection from well-known publicly available dataset.

  • 28.
    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.
    Cullhed, Per
    Uppsala University, University Library.
    Vats, Ekta
    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.
    Abrate, Matteo
    Making large collections of handwritten material easily accessible and searchable2019In: Digital Libraries: Supporting Open Science, Springer, 2019, p. 18-28Conference paper (Refereed)
  • 29.
    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.

  • 30.
    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.
    Hanke, Michael
    Royal Inst Technol, KTH, Dept Math, Sch Engn Sci, Stockholm, Sweden.
    Karlsson, Hans O.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Theoretical Chemistry.
    Swedish eScience Education – a Graduate School in eScience2015In: Proc. 11th International Conference on e-Science, IEEE Computer Society, 2015, p. 31-35Conference paper (Refereed)
    Abstract [en]

    Swedish eScience Education (SeSE) is a national graduate school in eScience in Sweden. It comes from the collaboration between two major research initiatives in eScience and the school has turned out to be very successful. It has made it possible for students at different universities to get access to education that is not normally available at their home universities. With SeSE they get access to education by the top experts within their respective field. We argue why such graduate school is important and how it is different from training offered by many HPC centres in Europe. Furthermore, examples of courses and their structure is discussed as well as lessons learned from SeSE and its two predecessors in Sweden.

  • 31.
    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.
    Jenke, Peter
    University of Gävle.
    Seipel, Stefan
    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.
    Shortest Diagonal Triangulation of Convex Layers2013In: The IASTED International Conference on Signal Processing, Pattern Recognition and Applications., 2013, p. 1-7Conference paper (Refereed)
  • 32.
    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.
    Kylberg, Gustav
    Clustering in 2D as a Fast Deterministic Alternative to RANSAC2015Conference paper (Refereed)
  • 33.
    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.
    Kylberg, Gustav
    Vironova AB, Stockholm, Sweden.
    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. Vironova AB, Stockholm, Sweden.
    An efficient descriptor based on radial line integration for fast non invariant matching and registration of microscopy images2017In: Advanced Concepts for Intelligent Vision Systems, Springer, 2017, p. 723-734Conference paper (Refereed)
    Abstract [en]

    Descriptors such as SURF and SIFT contain a framework for handling rotation and scale invariance, which generally is not needed when registration and stitching of images in microscopy is the focus. Instead speed and efficiency are more important factors. We propose a descriptor that performs very well for these criteria, which is based on the idea of radial line integration. The result is a descriptor that outperforms both SURF and SIFT when it comes to speed and the number of inliers, even for rather short descriptors.

  • 34.
    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.
    Lind, Mats
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Ensembles and Cascading of Embedded Prototype Subspace Classifiers2020In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 28, no 1/2, p. 89-95Article in journal (Refereed)
    Abstract [en]

    Deep learning approaches suffer from the so called interpretability problem and can therefore be very hard to visualise. Embedded Prototype Subspace Classifiers is one attempt in the field of explainable AI, which is both fast and efficient since it does not require repeated learning epochs and has no hidden layers. In this paper we investigate how ensembles and cascades of ensembles perform on some popular datasets. The focus is on handwritten data such as digits, letters and signs. It is shown how cascading can be efficiently implemented in order to both increase accuracy as well as speed up the classification.

    Download full text (pdf)
    Ensembles and Cascading of Embedded Prototype Subspace Classifiers
  • 35.
    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.
    Lind, Mats
    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.
    Embedded Prototype Subspace Classification: A subspace learning framework2019In: Computer Analysis of Images and Patterns, CAIP 2019, PT II, Springer, 2019, p. 581-592Conference paper (Refereed)
    Abstract [en]

    Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the blackbox learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.

  • 36.
    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.
    Lind, Mats
    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.
    Vats, Ekta
    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.
    Subspace Learning and Classification2019In: Proc. 3rd Swedish Symposium on Deep Learning, 2019Conference paper (Other academic)
  • 37.
    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
    Institute of Informatics and Telematics, Pisa, Italy.
    Putative Match Analysis: A Repeatable Alternative to RANSAC for Matching of Aerial Images2012In: VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications, Volume 2 / [ed] Gabriela Csurka, José Braz, SciTePress , 2012, p. 341-344Conference paper (Refereed)
    Abstract [en]

     One disadvantage with RANSAC is that it is based on randomness and will therefore often yield a different set of inliers in each run, especially if the dataset contains a large number of outliers. A repeatable algorithm for finding both matches and the homography is proposed, which in our case is used for image stitching and the obtained points are also used for georeferencing. This algorithm will yield the same set of matches every time and is therefore a useful tool when trying to evaluate other algorithms involved and their parameters. Moreover a refining step is proposed that finds the best matches depending on what geometric transformation is used, which also can be utilized as a refining step for RANSAC. 

  • 38.
    Hast, Anders
    et al.
    Institute of Informatics and Telematics, CNR, Pisa, Italy.
    Marchetti, Andrea
    Institute of Informatics and Telematics, Pisa, Italy.
    Putative Match Analysis: A Repeatable Alternative to RANSAC for Matching of Aerial Images2012In: VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications, Volume 2 / [ed] Gabriela Csurka, José Braz, SciTePress , 2012, p. 341-344Conference paper (Refereed)
    Abstract [en]

     One disadvantage with RANSAC is that it is based on randomness and will therefore often yield a different set of inliers in each run, especially if the dataset contains a large number of outliers. A repeatable algorithm for finding both matches and the homography is proposed, which in our case is used for image stitching and the obtained points are also used for georeferencing. This algorithm will yield the same set of matches every time and is therefore a useful tool when trying to evaluate other algorithms involved and their parameters. Moreover a refining step is proposed that finds the best matches depending on what geometric transformation is used, which also can be utilized as a refining step for RANSAC. 

  • 39.
    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. 

  • 40.
    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
    CNR, Ist Informat & Telemat, Pisa, Italy.
    Stereo Visualisation of Historical Aerial Photos – a Valuable Digital Heritage Research Tool2015In: 2015 Digital Heritage International Congress, Vol 2: Analysis & Interpretation Theory, Methodologies, Preservation & Standards Digital Heritage Projects & Applications, 2015, p. 663-666Conference paper (Refereed)
    Abstract [en]

    We demonstrate with several examples how historical aerial photos can benefit from being viewed in stereo and how this can be useful as tool in digital heritage research. The main reason why stereo images are important is that they give a much better understanding of what is actually in the scene than single photos can. The important factor is the depth cue that helps understanding the content and adds the ability to distinguish between objects such as houses and trees and the ground as well as estimating heights of objects. There are however still challenges but also possibilities that will be discussed.

  • 41.
    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
    Stereo Visualisation of Historical Aerial Photos as a Valuable Tool for Archeological Research2015In: Computer Applications and Quantitative Methods in Archaeology, CAA, 2015, p. 1-3Conference paper (Refereed)
  • 42.
    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)
  • 43.
    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.
    Mårtensson, Lasse
    Vats, Ekta
    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.
    Heil, Raphaela
    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.
    Creating an Atlas over Handwritten Script Signs2019In: Digital Humanities in the Nordic Countries, 2019Conference paper (Refereed)
  • 44.
    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)
  • 45.
    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)
  • 46.
    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
    Kylberg, Gustav
    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.
    A Simple and Efficient Feature Descriptor for Fast Matching2015In: WSCG / [ed] V. Skala, 2015, p. 135-142Conference paper (Refereed)
  • 47.
    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.
    Vats, Ekta
    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.
    An intelligent user interface for efficient semi-automatic transcription of historical handwritten documents2018In: Proc. 23rd International Conference on Intelligent User Interfaces Companion, New York: ACM Press, 2018, article id 48Conference paper (Refereed)
    Abstract [en]

    Transcription of large-scale historical handwritten document images is a tedious task. Machine learning techniques, such as deep learning, are popularly used for quick transcription, but often require a substantial amount of pre-transcribed word examples for training. Instead of line-by-line word transcription, this paper proposes a simple training-free gamification strategy where all occurrences of each arbitrarily selected word is transcribed once, using an intelligent user interface implemented in this work. The proposed approach offers a fast and user-friendly semi-automatic transcription that allows multiple users to work on the same document collection simultaneously.

  • 48.
    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.
    Vats, Ekta
    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.
    Radial line Fourier descriptor for historical handwritten text representation2018In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 26, no 1, p. 31-40Article in journal (Refereed)
  • 49.
    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.

  • 50.
    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.
    Vats, Ekta
    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.
    Word Recognition using Embedded Prototype Subspace Classifiers on a new Imbalanced Dataset2021In: Journal of WSCG, ISSN 1213-6972, E-ISSN 1213-6964, Vol. 29, no 1-2, p. 39-47Article in journal (Refereed)
    Abstract [en]

    This paper presents an approach towards word recognition based on embedded prototype subspace classification. The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extracted from the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clustering techniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classes of words is heavily imbalanced, and some word classes are very similar, which renders the classification task rather challenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits is evaluated for different clustering techniques. It will be demonstrated that the original clustering technique based on scaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that finds k clusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clusters based on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both a clustering and embedding technique.

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