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Distance Between Vector-Valued Representations of Objects in Images with Application in Object Detection and Classification
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. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)ORCID iD: 0000-0002-6041-6310
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. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia. (Centre for Image Analysis)ORCID iD: 0000-0001-7312-8222
2017 (English)In: In Proc. of the 18th International Workshop on Combinatorial Image Analysis, IWCIA2017 / [ed] Brimkov, Valentin E. & Barneva, Reneta P., Springer, 2017, Vol. 10256, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel approach to measuring distances between objects in images, suitable for information-rich object representations which simultaneously capture several properties in each image pixel. Multiple spatial fuzzy sets on the image domain, unified in a vector-valued fuzzy set, are used to model such representations. Distance between such sets is based on a novel point-to-set distance suitable for vector-valued fuzzy representations. The proposed set distance may be applied in, e.g., template matching and object classification, with an advantage that a number of object features are simultaneously considered. The distance measure is of linear time complexity w.r.t. the number of pixels in the image. We evaluate the performance of the proposed measure in template matching in presence of noise, as well as in object detection and classification in low resolution Transmission Electron Microscopy images.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10256, p. 243-255
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10256
Keywords [en]
Membership Function, Object Representation, Template Match, Fuzzy Membership Function, Catchment Basin
National Category
Discrete Mathematics Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-334200DOI: 10.1007/978-3-319-59108-7_19ISI: 000432061200019ISBN: 978-3-319-59107-0 (print)ISBN: 978-3-319-59108-7 (electronic)OAI: oai:DiVA.org:uu-334200DiVA, id: diva2:1158956
Conference
18th International Workshop on Combinatorial Image Analysis, IWCIA 2017, June 19-21, 2017, Plovdiv, Bulgaria.
Funder
VINNOVAAvailable from: 2017-11-21 Created: 2017-11-21 Last updated: 2018-08-24Bibliographically approved

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Sladoje, NatasaLindblad, Joakim

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Computerized Image Analysis and Human-Computer InteractionDivision of Visual Information and Interaction
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