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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)
2017 (English)In: In Proc. of the 18th International Workshop on Combinatorial Image Analysis, IWCIA2017, Springer, 2017, Vol. 10256, 243-255 p.Conference 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, 243-255 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10256
National Category
Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-334200DOI: 10.1007/978-3-319-59108-7_19OAI: oai:DiVA.org:uu-334200DiVA: diva2:1158956
Conference
18th International Workshop on Combinatorial Image Analysis, IWCIA2017
Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2017-11-21

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