Signature of a Shape Based on Its Pixel Coverage Representation

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• 关键词：Shape signature ; Centroid distance function ; Pixel coverage representation ; Sub ; pixel accuracy ; Precision
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9647
• 期：1
• 页码：181-193
• 全文大小：574 KB
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  6.Sladoje, N., Lindblad, J.: Estimation of moments of digitized objects with fuzzy borders. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 188–195. Springer, Heidelberg (2005)CrossRef
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• 作者单位：Vladimir Ilić (16)
  Joakim Lindblad (17) (18)
  Nataša Sladoje (17) (18)
  
  16. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
  17. Centre for Image Analysis, Uppsala University, Uppsala, Sweden
  18. Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia
  
• 丛书名：Discrete Geometry for Computer Imagery
• ISBN：978-3-319-32360-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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 \(\alpha \)-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.