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Can I read Shape recognition based on Kernel-edit distance on EtoBox?
Shape recognition based on Kernel-edit distance by Mohammad Reza Daliri; Vincent Torre is a Computer Science article available to read on EtoBox.
What is Shape recognition based on Kernel-edit distance about?
In this paper a kernel method for shape recognition is proposed. The approach is based on the edit distance between pairs of shapes after transforming them into symbol strings. The transformation of shapes into symbol strings is invariant to similarity transforms and can handle partial occlusions. Representation of shape contours uses the shape contexts and applies dynamic programming for finding the correspondence between points over shape contours. Corresponding points are then transformed into symbolic representation and the normalized edit distance computes the dissimilarity between pairs of strings in the database. Obtained distances are then transformed into suitable kernels which are classified using support vector machines. Experimental results over a variety of shape databases show that the proposed approach is suitable for shape recognition.
Who reads Shape recognition based on Kernel-edit distance?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Mohammad Reza Daliri; Vincent Torre
- Publisher
- Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 1077-3142)
- Published
- 2010
- Language
- EN
- Field
- Computer Science (Physical Sciences)