Shape Profile Matching and Its Applications

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;">The focus of this paper is on the development of a novel cross correlation technique for shape matching between two similar objects. The shape profile is derived from the curvature of the object profile. The cross correlation technique is applied to the shape profile of the two image objects to evaluate their similarity. An application of the shape profile technique is presented in the context of pose detection of human models from image sequences or videos. To extract the feature points on the human model, a number of human motion templates are constructed with designated feature points on each of the human motion profiles. The human model feature extraction procedure is divided into two steps, including: (1) pre-process and construction of motion templates, and (2) extraction of feature points. The best matched template image pairs and best fitted feature points are identified by evaluating the cross correlation coefficient of the corresponding shape profiles. The proposed method is demonstrated by experimental results.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;"></span></p