Human hand/body posture recognition based on partial shape matching скачать в хорошем качестве

Human hand/body posture recognition based on partial shape matching

We present a novel approach to the problem of establishing the best match between an open contour and a part of a closed contour. At the heart of the proposed scheme lies a novel shape descriptor that also permits the quantification of local scale. Shape descriptors are computed along open or closed contours in a spatially non-uniform manner. The resulting ordered collections of shape descriptors constitute the global shape representation. A variant of an existing DTW matching technique is proposed to handle the matching of shape representations. Due to the properties of the employed shape descriptor, sampling scheme and matching procedure, the proposed approach performs partial shape matching that is invariant to Euclidean transformations, starting point as well as to considerable shape deformations. Additionally, the problem of matching closed-to-closed contours is naturally treated as a special case. Extensive experiments on benchmark datasets but also in the context of specific applications, demonstrate that the proposed scheme outperforms existing methods for the problem of partial shape matching and performs comparably to methods for full shape matching. The video demonstrates the application of the proposed approach to the problem of human hand and human body posture recognition. For more information, see: http://www.ics.forth.gr/~argyros/rese... Relevant publications: D. Michel, I. Oikonomidis, A.A. Argyros, "Scale invariant and deformation tolerant partial shape matching", in Image and Vision Computing (IVC), Elsevier, vol. 29, issue 7, pp. 459-469, June 2011.