Improved method of object detection based on chord-tangent transformation

Shape is a kind of important feature to detect or recognize objects in computer vision. However, some disadvantages still exist in many methods based on shape feature at present, such as having no abundant information, easily affected by the default and distortion of edge, or having no local property. Especially, it is very difficult to detect object correctly under some complicated environments. In order to overcome these defects, a kind of shape feature extracted by using the chord-tangent transformation and corresponding algorithm of target detection was presented in this paper, which was obtained according to the finite edge point. This geometric feature had the invariant character of translation, rotation and scale. Some important parameters about movement including the location of object could be obtained through this method, even under some complicated environments. However,because the edge extraction is usually affected by the quality of image, contrast between the object and background and quantized error, the precision can be decreased. Therefore, some gray information was added to the feature in order to improve the algorithm. Finally, experimental results indicate the effectivity of the algorithm. The matching rate between the object and model can be extended to more than 90% after improving.