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  • 901.
    Wilkinson, Tomas
    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.
    Lindström, Jonas
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of History.
    Brun, 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.
    Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections2017Conference paper (Other academic)
  • 902.
    Wilkinson, Tomas
    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.
    Lindström, Jonas
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of History.
    Brun, 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.
    Neural Ctrl-F: Segmentation-free query-by-string word spotting in handwritten manuscript collections2017In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, p. 4443-4452Conference paper (Refereed)
    Abstract [en]

    In this paper, we approach the problem of segmentation-free query-by-string word spotting for handwritten documents. In other words, we use methods inspired from computer vision and machine learning to search for words in large collections of digitized manuscripts. In particular, we are interested in historical handwritten texts, which are often far more challenging than modern printed documents. This task is important, as it provides people with a way to quickly find what they are looking for in large collections that are tedious and difficult to read manually. To this end, we introduce an end-to-end trainable model based on deep neural networks that we call Ctrl-F-Net. Given a full manuscript page, the model simultaneously generates region proposals, and embeds these into a distributed word embedding space, where searches are performed. We evaluate the model on common benchmarks for handwritten word spotting, outperforming the previous state-of-the-art segmentation-free approaches by a large margin, and in some cases even segmentation-based approaches. One interesting real-life application of our approach is to help historians to find and count specific words in court records that are related to women's sustenance activities and division of labor. We provide promising preliminary experiments that validate our method on this task.

  • 903.
    Wu, Chi-Chih
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
    Klaesson, Axel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Buskas, Julia
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
    Ranefall, Petter
    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.
    Mirzazadeh, Reza
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wolf, Jochen B. W.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to speciation2019In: Journal of Experimental Biology, ISSN 0022-0949, E-ISSN 1477-9145, Vol. 222, no 5Article in journal (Refereed)
  • 904.
    Wuttke, Anne
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Gandasi, Nikhil
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Barg, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Tengholm, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology.
    Continuous imaging of exocytosis in β-cells reveals negative feedback of insulinManuscript (preprint) (Other academic)
  • 905.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    High throughput phenotyping of model organisms2012In: BioImage Informatics 2012 / [ed] Fuhui Long, Ivo F. Sbalzarini, Pavel Tomancak and Michael Unser, Dresden, Germany, 2012, p. 45-45Conference paper (Refereed)
    Abstract [en]

    Microscopy has emerged as one of the most powerful and informative ways to analyze cell-based high-throughput screening samples in experiments designed to uncover novel drugs and drug targets. However, many diseases and biological pathways can be better studied in whole animals – particularly diseases that involve organ systems and multi-cellular interactions, such as metabolism, infection, vascularization, and development. Two model organisms compatible with high-throughput phenotyping are the 1mm long round worm C. elegans and the transparent embryo of zebrafish (Danio rerio). C. elegans is tractable as it can be handled using similar robotics, multi-well plates, and flow-sorting systems as are used for high-throughput screening of cells. The worm is also transparent throughout its lifecycle and is attractive as a model for genetic functions as its genes can be turned off by RNA-interference. Zebrafish embryos have also proved to be a vital model organism in many fields of research, including organismal development, cancer, and neurobiology. Zebrafish, being vertebrates, exhibit features common to phylogenetically higher organisms such as a true vasculature and central nervous system.

     

    Basically any phenotypic change that can be visually observed (in untreated or stained worms and fish) can also be imaged. However, visual assessment of phenotypic variation is tedious and prone to error as well as observer bias. Screening in high throughput limits image resolution and time-lapse information. Still, the images are typically rich in information and the number of images for a standard screen often exceeds 100 000, ruling out visual inspection. Generation of automated image analysis platforms will increase the throughout of data analysis, improve the robustness of phenotype scoring, and allow for reliable application of statistical metrics for evaluating assay performance and identifying active compounds.

     

    We have developed a platform for automated analysis of C. elegans assays, and are currently developing tools for analysis of zebrafish embryos. Our worm analysis tools, collected in the WormToolbox, can identify individual worms also as they cross and overlap, and quantify a large number of features, including mapping of reporter protein expression patterns to the worm anatomy. We have evaluated the tools on screens for novel treatments of infectious disease and genetic perturbations affecting fat metabolism. The WormToolbox is part of the free and open source CellProfiler software, also including methods for image assay quality control and feature selection by machine learning.

  • 906.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. 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, Processing and Analysis in Microscopy and Life Science2015In: Mathematical Models in Biology: Bringing Mathematics to Life, Springer, 2015, p. 1-16Chapter in book (Other academic)
  • 907.
    Wählby, Carolina
    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, Science for Life Laboratory, SciLifeLab.
    The quest for multiplexed spatially resolved transcriptional profiling2016In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 13, no 8, p. 623-624Article in journal (Other academic)
  • 908.
    Wählby, Carolina
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conery, Annie Lee
    Bray, Mark-Anthony
    Kamentsky, Lee
    Larkins-Ford, Jonah
    Sokolnicki, Katherine L.
    Veneskey, Matthew
    Michaels, Kerry
    Carpenter, Anne E.
    O'Rourke, Eyleen J.
    High- and low-throughput scoring of fat mass and body fat distribution in C. elegans2014In: Methods, ISSN 1046-2023, E-ISSN 1095-9130, Vol. 68, no 3, p. 492-499Article in journal (Refereed)
    Abstract [en]

    Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling, feeding, activity, and reproductive output. Accordingly, the most informative screens for genes and compounds affecting fat accumulation would be those carried out in whole living animals. Caenorhabditis elegans is a well-established and effective model organism, especially for biological processes that involve organ systems and multicellular interactions, such as metabolism. Every cell in the transparent body of C. elegans is visible under a light microscope. Consequently, an accessible and reliable method to visualize worm lipid-droplet fat depots would make C. elegans the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust, formaldehyde-free, and inexpensive, and requires only 15 min of hands-on time to process a 96-well plate. Together with our free and user-friendly automated image analysis package, this method enables C. elegans sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals.

  • 909.
    Wählby, Carolina
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kamentsky, Lee
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
    Liu, Zihan H
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
    Riklin-Raviv, Tammy
    Conery, Annie L
    Dept. of Molecular Biology and Center for Computational and Integrative Biology, Mass. General Hospital, Boston, MA.
    O'Rourke, Eyleen
    Sokolnicki, Katherine
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
    Visvikis, Orane
    Developmental Immunology Program, Dept. of Pediatrics, Mass. General Hospital, Boston, MA.
    Ljosa, Vebjorn
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
    Irazoqui, Javier E
    Developmental Immunology Program, Dept. of Pediatrics, Mass. General Hospital, Boston, MA.
    Golland, Polina
    Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA.
    Ruvkun, Gary
    Ausubel, Frederick M
    Dept. of Molecular Biology and Center for Computational and Integrative Biology, Mass. General Hospital, Boston, MA.
    Carpenter, Anne E
    Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
    An image analysis toolbox for high-throughput C. elegans assays2012In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 9, no 7, p. 714-716Article in journal (Refereed)
    Abstract [en]

    We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems from different laboratories. The toolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays using this unique model organism for the study of diverse biological pathways relevant to human disease.

  • 910.
    Yeh, Alexander
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Ratsamee, Photchara
    Osaka University, Osaka, Japan.
    Kiyokawa, Kiyoshi
    Nara Institute of Science and Technology (NAIST), Nara, Japan.
    Uranishi, Yuki
    Osaka University, Osaka, Japan.
    Mashita, Tomohiro
    Osaka University, Osaka, Japan.
    Takemura, Haruo
    Osaka University, Osaka, Japan.
    Fjeld, Morten
    Chalmers University of Technology, Gothenburg, Sweden.
    Obaid, Mohammad
    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.
    Exploring proxemics for human-drone interaction2017In: Proc. 5th International Conference on Human Agent Interaction, New York: ACM Press, 2017, p. 81-88Conference paper (Refereed)
    Abstract [en]

    We present a human-centered designed social drone aiming to be used in a human crowd environment. Based on design studies and focus groups, we created a prototype of a social drone with a social shape, face and voice for human interaction. We used the prototype for a proxemic study, comparing the required distance from the drone humans could comfortably accept compared with what they would require for a nonsocial drone. The social shaped design with greeting voice added decreased the acceptable distance markedly, as did present or previous pet ownership, and maleness. We also explored the proximity sphere around humans with a social shaped drone based on a validation study with variation of lateral distance and heights. Both lateral distance and the higher height of 1.8 m compared to the lower height of 1.2 m decreased the required comfortable distance as it approached.

  • 911.
    Zhang, Hanqian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Ericsson, Maja
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Virtanen, Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Weström, Simone
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Wählby, Carolina
    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.
    Vahlquist, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Törmä, Hans
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Quantitative image analysis of protein expression and colocalisation in skin sections2018In: Experimental dermatology, ISSN 0906-6705, E-ISSN 1600-0625, Vol. 27, no 2, p. 196-199Article in journal (Refereed)
    Abstract [en]

    Immunofluorescence (IF) and in situ proximity ligation assay (isPLA) are techniques that are used for in situ protein expression and colocalisation analysis, respectively. However, an efficient quantitative method to analyse both IF and isPLA staining on skin sections is lacking. Therefore, we developed a new method for semi-automatic quantitative layer-by-layer measurement of protein expression and colocalisation in skin sections using the free open-source software CellProfiler. As a proof of principle, IF and isPLA of ichthyosis-related proteins TGm-1 and SDR9C7 were examined. The results indicate that this new method can be used for protein expression and colocalisation analysis in skin sections.

  • 912.
    Zhang, Hanqian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Virtanen, Marie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology. en..
    Weström, Simone
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Bygum, A.
    Odense Univ Hosp, Dept Dermatol & Allergy, Odense, Denmark..
    Wählby, Carolina
    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, Science for Life Laboratory, SciLifeLab.
    Vahlquist, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Törmä, Hans
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Quantitative analysis of immunofluorescence and in situ PLA staining using CellProfiler reveals impaired epidermal lipid processing pathway in ARCI patients with CYP4F22 mutations2016In: Journal of Investigative Dermatology, ISSN 0022-202X, E-ISSN 1523-1747, Vol. 136, no 9, p. S180-S180Article in journal (Other academic)
  • 913. Zhang, Peilin
    et al.
    Gao, Alex Yuan
    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.
    Theel, Oliver
    Bandit learning with concurrent transmissions for energy-efficient flooding in sensor networks2018In: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, ISSN 2410-0218, Vol. 4, no 13, article id e4Article in journal (Refereed)
  • 914. Zhang, Peilin
    et al.
    Gao, Alex Yuan
    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.
    Theel, Oliver
    Less is More: Learning more with concurrent transmissions for energy-efficient flooding2017In: Proc. 14th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, New York: ACM Press, 2017Conference paper (Refereed)
  • 915. Zuluaga, Maria A.
    et al.
    Orkisz, Maciej
    Dong, Pei
    Pacureanu, Alexandra
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gouttenoire, Pierre-Jean
    Peyrin, Françoise
    Bone canalicular network segmentation in 3D nano-CT images through geodesic voting and image tessellation2014In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 9, p. 2155-2171Article in journal (Refereed)
    Abstract [en]

    Recent studies emphasized the role of the bone lacuno-canalicular network (LCN) in the understanding of bone diseases such as osteoporosis. However, suitable methods to investigate this structure are lacking. The aim of this paper is to introduce a methodology to segment the LCN from three-dimensional (3D) synchrotron radiation nano-CT images. Segmentation of such structures is challenging due to several factors such as limited contrast and signal-to-noise ratio, partial volume effects and huge number of data that needs to be processed, which restrains user interaction. We use an approach based on minimum-cost paths and geodesic voting, for which we propose a fully automatic initialization scheme based on a tessellation of the image domain. The centroids of pre-segmented lacunae are used as Voronoi-tessellation seeds and as start-points of a fast-marching front propagation, whereas the end-points are distributed in the vicinity of each Voronoi-region boundary. This initialization scheme was devised to cope with complex biological structures involving cells interconnected by multiple thread-like, branching processes, while the seminal geodesic-voting method only copes with tree-like structures. Our method has been assessed quantitatively on phantom data and qualitatively on real datasets, demonstrating its feasibility. To the best of our knowledge, presented 3D renderings of lacunae interconnected by their canaliculi were achieved for the first time.

  • 916.
    Åberg, Anna Cristina
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Halvorsen, Kjartan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    From, Ingrid
    Dalarna Univ, Sch Educ Hlth & Social Studies, SE-79188 Falun, Sweden.
    Bergman Bruhn, Åsa
    Dalarna Univ, Sch Educ Hlth & Social Studies, SE-79188 Falun, Sweden.
    Oestreicher, Lars
    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.
    Melander-Wikman, Anita
    Lulea Univ Technol, Div Hlth & Rehab, Dept Hlth Sci, SE-97187 Lulea, Sweden.
    A study protocol for applying user participation and co-learning: Lessons learned from the eBalance project2017In: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 14, no 5, article id 512Article in journal (Refereed)
    Abstract [en]

    The eBalance project is based on the idea that serious exergames-i.e., computer gaming systems with an interface that requires physical exertion to play-that are well adapted to users, can become a substantial part of a solution to recognized problems of insufficient engagement in fall-prevention exercise and the high levels of fall-related injuries among older people. This project is carried out as a collaboration between eight older people who have an interest in balance training and met the inclusion criteria of independence in personal activities of daily living, access to and basic knowledge of a computer, four staff working with the rehabilitation of older adults, and an interdisciplinary group of six research coordinators covering the areas of geriatric care and rehabilitation, as well as information technology and computer science. This paper describes the study protocol of the project's initial phase which aims to develop a working partnership with potential users of fall-prevention exergames, including its conceptual underpinnings. The qualitative methodology was inspired by an ethnographical approach implying combining methods that allowed the design to evolve through the study based on the participants' reflections. A participatory and appreciative action and reflection (PAAR) approach, accompanied by inquiries inspired by the Normalization Process Theory (NPT) was used in interactive workshops, including exergame testing, and between workshop activities. Data were collected through audio recordings, photos, and different types of written documentation. The findings provide a description of the methodology thus developed and applied. They display a methodology that can be useful for the design and development of care service and innovations for older persons where user participation is in focus.

  • 917. Åhlén, Julia
    et al.
    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.
    Automatic Water Body Extraction From Remote Sensing Images Using Entropy2015In: SGEM2015 Conference Proceedings, 2015, Vol. 2, p. 517-524Conference paper (Refereed)
    Abstract [en]

    This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible.As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference.

  • 918.
    Åhlén, Julia
    et al.
    Högskolan i Gävle.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Indication of Methane Gas in IR-Imagery2011In: Proceedings of IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2011 (CGVCVIP 2011), 2011, p. 187-192Conference paper (Refereed)
  • 919.
    Åhlén, Julia
    et al.
    Högskolan i 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.
    Knowledge Based Single Building Extraction and Recognition2014In: Proceedings WSEAS International Conference on Computer Engineering and Applications, 2014, 2014, p. 29-35Conference paper (Refereed)
    Abstract [en]

    Building facade extraction is the primary step in the recognition process in outdoor scenes. It is also achallenging task since each building can be viewed from different angles or under different lighting conditions. Inoutdoor imagery, regions, such as sky, trees, pavement cause interference for a successful building facade recognition.In this paper we propose a knowledge based approach to automatically segment out the whole facade or majorparts of the facade from outdoor scene. The found building regions are then subjected to recognition process. Thesystem is composed of two modules: segmentation of building facades region module and facade recognition module.In the facade segmentation module, color processing and objects position coordinates are used. In the facaderecognition module, Chamfer metrics are applied. In real time recognition scenario, the image with a building isfirst analyzed in order to extract the facade region, which is then compared to a database with feature descriptors inorder to find a match. The results show that the recognition rate is dependent on a precision of building extractionpart, which in turn, depends on a homogeneity of colors of facades.

  • 920. Åhlén, Julia
    et al.
    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.
    Mapping of roof types in orthophotos using feature descriptors2018In: Proc. International Multidisciplinary Scientific GeoConference: SGEM 2018, 2018, p. 285-291Conference paper (Refereed)
  • 921.
    Åhlén, Julia
    et al.
    Akademi för Teknik och Miljö, Högskolan i 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.
    Segmentation of shadows and water bodies in high resolution images using ancillary data2016In: Proc. 16th International Multidisciplinary Scientific GeoConference, 2016, Vol. 1, p. 827-834Conference paper (Refereed)
    Abstract [en]

    High spatial resolution imagery is often affected by shadows, both in urban environments with large variations in surface elevation and in vegetated areas. It is a common bias in classification when waters and shadows are registered as the same area. The radiometric response for the shadowed regions should be restored prior to classification. To enable that, separate classes of non-shadowed regions and shadowed areas should be created. Previous work on water extraction using low/medium resolution images, mainly faced two difficulties. Firstly, it is difficult to obtain accurate position of water boundary and secondly, shadows of elevated objects e.g. buildings, bridges, towers and trees are a typical source of noise when facing water extraction in urban regions. In high resolution images the problem of separation water and shadows becomes more prominent since the small local variation of intensity values gives rise to misclassification. This paper proposes a robust method for separation of shadowed areas and water bodies in high spatial resolution imagery using hierarchical method on different scales combined with classification of PCA (Principal Component Analysis) bands, which reduces the effects of radiometric and spatial differences that is commonly associated with the pixel-based methods for multisource data fusion. The method uses ancillary data to aid in classification of shadows and waters. The proposed method includes three steps: segmentation, classification and postprocessing. To achieve robust segmentation, we apply the merging region with three features (PCA bands, NSVDI (Normalized Saturation-value Difference Index) and height data). NSVDI discriminates shadows and some water. In the second step we use hierarchic region based classification to identify water regions. After that step candidates for water pixels are verified by the LiDAR DEM data. As a last step we consider shape parameters such as compactness and symmetry to completely remove shadows.

  • 922.
    Åhlén, Julia
    et al.
    Högskolan i 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.
    TIME-SPACE VISUALISATION OF AMUR RIVER CHANNEL CHANGES DUE TO FLOODING DISASTER2014In: Proceedings of International Multidisciplinary Scientific GeoScience Conference (SGEM), 2014, 2014Conference paper (Refereed)
    Abstract [en]

    The analysis of flooding levels is a highly complex temporal and spatial assessment task that involves estimation of distances between references in geographical space as well as estimations of instances along the time-line that coincide with given spatial locations. This work has an aim to interactively explore changes of Amur River boundaries caused by the severe flooding in September 2013. In our analysis of river bank changes we use satellite imagery (Landsat 7) to extract parts belonging to Amur River. We use imagery from that covers time interval July 2003 until February 2014. Image data is pre-processed using low level image processing techniques prior to visualization. Pre-processing has a purpose to extract information about the boundaries of the river, and to transform it into a vectorized format, suitable as inputs subsequent visualization. We develop visualization tools to explore the spatial and temporal relationship in the change of river banks. In particular the visualization shall allow for exploring specific geographic locations and their proximity to the river/floods at arbitrary times. We propose a time space visualization that emanates from edge detection, morphological operations and boundary statistics on Landsat 2D imagery in order to extract the borders of Amur River. For the visualization we use the time-spacecube metaphor. It is based on a 3D rectilinear context, where the 2D geographical coordinate system is extended with a time-axis pointing along the 3rd Cartesian axis. Such visualization facilitates analysis of the channel shape of Amur River and thus enabling for a conclusion regarding the defined problem. As a result we demonstrate our time-space visualization for river Amur and using some amount of geographical point data as a reference we suggest an adequate method of interpolation or imputation that can be employed to estimate value at a given location and time.

  • 923.
    Åhlén, Julia
    et al.
    Högskolan i 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.
    Liu, Fei
    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.
    Evaluation of the Automatic methods for Building Extraction2014In: International Journal of Computers and Communications, ISSN 2074-1294, Vol. 8, p. 171-176Article in journal (Refereed)
  • 924.
    Öfverstedt, Johan
    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.
    Lindblad, Joakim
    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. Serbian Acad Arts & Sci, Math Inst, Belgrade 11001, Serbia.
    Sladoje, Natasa
    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. Serbian Acad Arts & Sci, Math Inst, Belgrade 11001, Serbia.
    Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information2019In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, no 7, p. 3584-3597Article in journal (Refereed)
    Abstract [en]

    Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

  • 925.
    Öfverstedt, Johan
    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.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    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.
    Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information2018Manuscript (preprint) (Other academic)
    Abstract [en]

    Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

  • 926.
    Öfverstedt, Johan
    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.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    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 Symmetric Affine Image Registration2019In: Swedish Symposium on Image Analysis, 2019Conference paper (Other academic)
  • 927.
    Öfverstedt, Johan
    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.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    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.
    Stochastic Distance Functions with Applications in Object Detection and Image Segmentation2019In: Swedish Symposium on Image Analysis, 2019Conference paper (Other academic)
  • 928.
    Öfverstedt, Johan
    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.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    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.
    Stochastic Distance Transform2019In: Discrete Geometry for Computer Imagery, Springer, 2019, p. 75-86Conference paper (Refereed)
    Abstract [en]

    The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

  • 929.
    Öfverstedt, Johan
    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.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection2017In: Mathematical Morphology and its Applications to Signal and Image Processing, Springer, 2017, Vol. 10225, p. 395-407Conference paper (Refereed)
    Abstract [en]

    We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single-and multi-channel distance measures between information-rich image/ object representations.

  • 930.
    Öfverstedt, Johan
    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.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection2018In: Swedish Symposium on Image Analysis, 2018Conference paper (Other academic)
  • 931.
    Ćurić, Vladimir
    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.
    Distance Functions and Their Use in Adaptive Mathematical Morphology2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    One of the main problems in image analysis is a comparison of different shapes in images. It is often desirable to determine the extent to which one shape differs from another. This is usually a difficult task because shapes vary in size, length, contrast, texture, orientation, etc. Shapes can be described using sets of points, crisp of fuzzy. Hence, distance functions between sets have been used for comparing different shapes.

    Mathematical morphology is a non-linear theory related to the shape or morphology of features in the image, and morphological operators are defined by the interaction between an image and a small set called a structuring element. Although morphological operators have been extensively used to differentiate shapes by their size, it is not an easy task to differentiate shapes with respect to other features such as contrast or orientation. One approach for differentiation on these type of features is to use data-dependent structuring elements.

    In this thesis, we investigate the usefulness of various distance functions for: (i) shape registration and recognition; and (ii) construction of adaptive structuring elements and functions.

    We examine existing distance functions between sets, and propose a new one, called the Complement weighted sum of minimal distances, where the contribution of each point to the distance function is determined by the position of the point within the set. The usefulness of the new distance function is shown for different image registration and shape recognition problems. Furthermore, we extend the new distance function to fuzzy sets and show its applicability to classification of fuzzy objects.

    We propose two different types of adaptive structuring elements from the salience map of the edge strength: (i) the shape of a structuring element is predefined, and its size is determined from the salience map; (ii) the shape and size of a structuring element are dependent on the salience map. Using this salience map, we also define adaptive structuring functions. We also present the applicability of adaptive mathematical morphology to image regularization. The connection between adaptive mathematical morphology and Lasry-Lions regularization of non-smooth functions provides an elegant tool for image regularization.

    List of papers
    1. On set distances and their application to image registration
    Open this publication in new window or tab >>On set distances and their application to image registration
    2009 (English)In: Proc. 6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria: IEEE , 2009, p. 449-454Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper we study set distances that are used in image processing. We propose a generalization of Sum of minimal distances and show that its special cases include a metric by Symmetric difference. The Hausdorff metric and the Chamfer matching distances are also closely related with the presented framework. In addition, we define the Complement set distance of a given distance. We evaluate the observed distance with respect to applicability to image object registration. We perform comparative evaluations with respect to noise sensitivity, as well as with respect to rigid body transformations. We conclude that the family of Generalized sum of minimal distances has many desirable properties for this application.

    Place, publisher, year, edition, pages
    Salzburg, Austria: IEEE, 2009
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis; Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-110684 (URN)10.1109/ISPA.2009.5297672 (DOI)978-953-184-135-1 (ISBN)
    Conference
    6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria, 16-18 September, 2009
    Available from: 2009-11-26 Created: 2009-11-23 Last updated: 2018-12-18
    2. A new set distance and its application to shape registration
    Open this publication in new window or tab >>A new set distance and its application to shape registration
    Show others...
    2014 (English)In: Pattern Analysis and Applications, ISSN 1433-7541, E-ISSN 1433-755X, Vol. 17, no 1, p. 141-152Article in journal (Refereed) Published
    National Category
    Discrete Mathematics
    Identifiers
    urn:nbn:se:uu:diva-220413 (URN)10.1007/s10044-012-0290-x (DOI)000330839400011 ()
    Available from: 2012-08-23 Created: 2014-03-13 Last updated: 2018-12-18Bibliographically approved
    3. Distance measures between digital fuzzy objects and their applicability in image processing
    Open this publication in new window or tab >>Distance measures between digital fuzzy objects and their applicability in image processing
    2011 (English)In: Combinatorial Image Analysis / [ed] Jake Aggarwal, Reneta Barneva, Valentin Brimkov, Kostadin Koroutchev, Elka Koroutcheva, Springer Berlin/Heidelberg, 2011, p. 385-397Conference paper, Published paper (Refereed)
    Abstract [en]

    We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2011
    Series
    Lecture Notes in Computer Science ; 6636
    Keywords
    Fuzzy sets, set distance, registration, classification
    National Category
    Computer Vision and Robotics (Autonomous Systems) Discrete Mathematics
    Research subject
    Computerized Image Analysis; Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-157186 (URN)10.1007/978-3-642-21073-0_34 (DOI)978-3-642-21072-3 (ISBN)
    Conference
    Internatiional Workshop on Combinatorial Image Analysis, IWCIA 2011
    Available from: 2011-08-18 Created: 2011-08-18 Last updated: 2018-12-18
    4. Salience adaptive structuring elements
    Open this publication in new window or tab >>Salience adaptive structuring elements
    2012 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 6, no 7, p. 809-819Article in journal (Refereed) Published
    Abstract [en]

    Spatially adaptive structuring elements adjust their shape to the local structures in the image, and are often defined by a ball in a geodesic distance or gray-weighted distance metric space. This paper introduces salience adaptive structuring elements as spatially variant structuring elements that modify not only their shape, but also their size according to the salience of the edges in the image. Morphological operators with salience adaptive structuring elements shift edges with high salience to a less extent than those with low salience. Salience adaptive structuring elements are less flexible than morphological amoebas and their shape is less affected by noise in the image. Consequently, morphological operators using salience adaptive structuring elements have better properties.

    Keywords
    Adaptive mathematical morphology, anisotropic filtering, morphological amoebas, salience distance transform
    National Category
    Other Mathematics
    Identifiers
    urn:nbn:se:uu:diva-181248 (URN)10.1109/JSTSP.2012.2207371 (DOI)000310138400007 ()
    Available from: 2012-09-20 Created: 2012-09-20 Last updated: 2017-12-07Bibliographically approved
    5. Adaptive structuring elements based on salience information
    Open this publication in new window or tab >>Adaptive structuring elements based on salience information
    2012 (English)In: Computer Vision and Graphics / [ed] L. Bolc, K. Wojciechowski, R. Tadeusiewicz, L.J. Chmielewski, Springer, 2012, p. 321-328Conference paper, Published paper (Other academic)
    Abstract [en]

    Adaptive structuring elements modify their shape and size according to the image content and may outperform fixed structuring elements. Without any restrictions, they suffer from a high computational complexity, which is often higher than linear with respect to the number of pixels in the image. This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures. The size of adaptive structuring elements is determined by the salience map that corresponds to the salience of the edges in the image, which can be computed in linear time. We illustrate the difference between the new adaptive structuring elements and morphological amoebas. As an example of its usefulness, we show how the new adaptive morphological operations can isolate the text in historical documents.

    Place, publisher, year, edition, pages
    Springer, 2012
    Series
    Lecture Notes in Computer Science, ISSN 03029743 ; 7594
    National Category
    Other Mathematics Other Computer and Information Science
    Identifiers
    urn:nbn:se:uu:diva-181246 (URN)10.1007/978-3-642-33564-8-39 (DOI)000313005700039 ()978-3-642-33564-8 (ISBN)
    Conference
    International Conference on Computer Vision and Graphics, September 24-26, 2012, Warsaw, Poland
    Available from: 2012-09-20 Created: 2012-09-20 Last updated: 2018-01-12Bibliographically approved
    6. Salience-Based Parabolic Structuring Functions
    Open this publication in new window or tab >>Salience-Based Parabolic Structuring Functions
    2013 (English)In: Mathematical Morphology and Its Applications to Signal and Image Processing, Springer Berlin/Heidelberg, 2013, p. 183-194Conference paper, Published paper (Refereed)
    Abstract [en]

    It has been shown that the use of the salience map based on the salience distance transform can be useful for the construction of spatially adaptive structuring elements. In this paper, we propose salience-based parabolic structuring functions that are defined for a fixed, predefined spatial support, and have low computational complexity. In addition, we discuss how to properly define adjunct morphological operators using the new spatially adaptive structuring functions. It is also possible to obtain flat adaptive structuring elements by thresholding the salience-based parabolic structuring functions.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2013
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 7883
    National Category
    Other Mathematics
    Research subject
    Mathematics with specialization in Applied Mathematics; Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-204715 (URN)10.1007/978-3-642-38294-9_16 (DOI)978-3-642-38293-2 (ISBN)
    Conference
    11th International Symposium on Mathematical Morphology
    Available from: 2013-08-09 Created: 2013-08-09 Last updated: 2014-04-29Bibliographically approved
    7. Morphological image regularization using adaptive structuring functions
    Open this publication in new window or tab >>Morphological image regularization using adaptive structuring functions
    (English)Manuscript (preprint) (Other academic)
    National Category
    Other Mathematics
    Identifiers
    urn:nbn:se:uu:diva-221161 (URN)
    Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2014-04-29
    8. Adaptive Mathematical Morphology: a survey of the field
    Open this publication in new window or tab >>Adaptive Mathematical Morphology: a survey of the field
    2014 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 47, p. 18-28Article in journal (Refereed) Published
    Abstract [en]

    We present an up-to-date survey on the topic of adaptive mathematical morphology. A broad review of research performed within the field is provided, as well as an in-depth summary of the theoretical advances within the field. Adaptivity can come in many different ways, based on different attributes, measures, and parameters. Similarities and differences between a few selected methods for adaptive structuring elements are considered, providing perspective on the consequences of different types of adaptivity. We also provide a brief analysis of perspectives and trends within the field, discussing possible directions for future studies.

    Keywords
    Overview, Mathematical morphology, Adaptive morphology, Adaptive structuring elements, Adjunction property
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-221159 (URN)10.1016/j.patrec.2014.02.022 (DOI)000339999200003 ()
    Available from: 2014-03-18 Created: 2014-03-25 Last updated: 2018-01-11Bibliographically approved
16171819 901 - 931 of 931
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