Skeleton based shape matching using reweighted random walks

Giang, Ngo Truong; Tao, Ngo Quoc; Dung, Nguyen Duc

doi:10.1109/icics.2013.6782781

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…In a subsequent publication [3], the skeleton paths shape descriptor is used in conjunction with contour fragments to design a shape classifier. Using a similar endpoints representation for the shape skeleton, Giang et al [16] further compare tuples of terminal nodes. They define a hyper-graph that relates a node to its k-nearest neighbors.…”

Section: Related Workmentioning

confidence: 99%

Shape matching by part alignment using extended chordal axis transform

Yasseen

Verroust-Blondet

Nasri

2016

Pattern Recognition

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One of the main challenges in shape matching is overcoming intra-class variation where objects that are conceptually similar have significant geometric dissimilarity. The key to a solution around this problem is incorporating the structure of the object in the shape descriptor which can be described by a connectivity graph customarily extracted from its skeleton. In a slightly different perspective, the structure may also be viewed as the arrangement of protruding parts along its boundary. This arrangement does not only convey the protruding part's ordering along the anti clockwise direction, but also relates these parts on different levels of detail. In this paper, we propose a shape matching method that estimates the distance between two objects by conducting a part-to-part matching analysis between their visual protruding parts. We start by a skeleton-based segmentation of the shape inspired by the Chordal Axis Transform. Then, we extract the segments that represent the protruding parts in its silhouette on varied levels of detail. Each one of these parts is described by a feature vector. A shape is thus described by the feature vectors of its parts in addition to their angular and linear proximities to each other. Using dynamic programming, our algorithm finds a minimal cost correspondence between parts. Our experimental evaluations validate the proposition that part correspondence allows conceptual matching of precisely dissimilar shapes.

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Section: Related Workmentioning

confidence: 99%

Shape matching by part alignment using extended chordal axis transform

Yasseen

Verroust-Blondet

Nasri

2016

Pattern Recognition

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Refining graph matching using inherent structure information

Song

Cavallaro

2015

2015 IEEE International Conference on Multimedia and Expo (ICME)

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Vision-Based Gesture Recognition Referring to Human Structure

Zhang

Liang

et al. 2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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Abstract. This article proposed a new method which introduces human body structure into gesture recognition besides the traditional vision information. By establishing the target's skeleton model, the system could rule out background interference quickly from the real-time frames, then locate the target's joints accurately, and track the gestures automatically. This method applies a common monocular webcam for image input instead of expensive depth cameras or complex binocular cameras. It can build a vivid human model and simulate the target's gestures well. After a modified KNN classification which has self-learning ability, the relationships between these joints are analyzed, and the corresponding postures and movements are identified. This method avoids global scanning in each frame and simplifies the classification, thus the computation decreases sharply. The results show that the method has a good tolerance of complex background, it can solve the target missing well.

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Skeleton based shape matching using reweighted random walks

Cited by 4 publications

References 24 publications

Shape matching by part alignment using extended chordal axis transform

Shape matching by part alignment using extended chordal axis transform

Refining graph matching using inherent structure information

Vision-Based Gesture Recognition Referring to Human Structure

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