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Signature of a Shape Based on Its Pixel Coverage Representation
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
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, Serbi. (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. 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, Serbi. (Centre for Image Analysis)
2016 (English)In: Discrete Geometry for Computer Imagery, DGCI 2016. Lecture Notes in Computer Science, Vol. 9647, pp. 181-193, Springer 2016: 19th IAPR International Conference, DGCI 2016, Nantes, France, April 18-20, 2016. Proceedings / [ed] Normand, Nicolas, Guédon, Jeanpierre, Autrusseau, Florent, Springer Berlin/Heidelberg, 2016, Vol. 9647, 181-193 p.Conference paper (Refereed)
Abstract [en]

Distance from the boundary of a shape to its centroid, a.k.a. signature of a shape, is a frequently used shape descriptor. Commonly, the observed shape results from a crisp (binary) segmentation of an image. The loss of information associated with binarization leads to a significant decrease in accuracy and precision of the signature, as well as its reduced invariance w.r.t. translation and rotation. Coverage information enables better estimation of edge position within a pixel. In this paper, we propose an iterative method for computing the signature of a shape utilizing its pixel coverage representation. The proposed method iteratively improves the accuracy of the computed signature, starting from a good initial estimate. A statistical study indicates considerable improvements in both accuracy and precision, compared to a crisp approach and a previously proposed approach based on averaging signatures over α-cuts of a fuzzy representation. We observe improved performance of the proposed descriptor in the presence of noise and reduced variation under translation and rotation.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2016. Vol. 9647, 181-193 p.
Keyword [en]
Shape signature, Centroid distance function, Pixel coverage representation, Sub-pixel accuracy, Precision
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-308084DOI: 10.1007/978-3-319-32360-2_14OAI: oai:DiVA.org:uu-308084DiVA: diva2:1049165
Conference
The 19th international conference on Discrete Geometry for Computer Imagery, DGCI 2016.
Funder
VINNOVA
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23

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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Computer Vision and Robotics (Autonomous Systems)

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