3 results for “Shortest Augmenting Path Method”

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Research ArticleOpen accessIn the keywordsA Successive Admissible Cell Method for Solving Large Scale Linear Assignment Problem with Dense Cost MatrixWarut Boonphakdee, Peerayuth CharnsethikulNormally, the Linear Assignment Problem (LAP) has been solved by successful algorithms such as Lapjv and Munkres programmed as MATLAB codes. This study presented an improved algorithm for solving large scale LAP. A preprocessing (PP) algorithm was proposed to apply for constructing the kth transferred reduced cost matrix then this matrix was solved by Lapjvalgorithm. Performances of PP-Lapjvalgorithm were faster … …Journal of Computer Science, Vol. 12, Issue 8 · 16 Nov 2016 · 6,948 accessesView PDF Cite

Research ArticleOpen accessIn the abstractSHAPE RETRIEVAL THROUGH MAHALANOBIS DISTANCE WITH SHORTEST AUGMENTING PATH ALGORITHMS. Muruganathan, N. Devarajan, D. Chitra, T. ManigandanShape matching and object recognition plays an vital role in the computer vision. The shape matching is difficult in case of the real world images like mpeg database images since the real world images has the internal and external contours. The Mahalanobis distance based shape context approach is proposed to measure similarity between shapes and exploit it for shape retrieval. … …Journal of Computer Science, Vol. 10, Issue 4 · 3 Dec 2013 · 6,856 accessesView PDF Cite

Research ArticleOpen accessIn the abstractShape Retrieval through Angular Distance with Shortest Augmenting Path AlgorithmD. Chitra, N. Devarajan, T. ManigandanProblem statement: The shape of an object is very important in object recognition. Shape matching is a challenging problem, especially when articulation and deformation of a part occurs. These variations may be insignificant for human recognition but often cause a matching algorithm to give results that are inconsistent with our perception. Approach: We proposed a customized approach to measure similarity … …Journal of Computer Science, Vol. 7, Issue 12 · 21 Oct 2011 · 6,817 accessesView PDF Cite