Study On Methods Of Object Detection Based On Wavelet Theories And Fast Object Tracking

In this dissertation, the methods of automatic target recognition and fast object tracking are studied for the problems in target tracking system currently.Aiming at detecting objects precisely, a method of image enhancement and noise removing based on the curvelet transform with edge detection based on wavelet transform is proposed. The simulation results of image enhancement and noise removing show the curvelet transform yields the better visual quality than others especially at low SNR.The algorithms of shape matching based on wavelet coefficients are introduced. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour, the Zernike moments are introduced , and a novel Starting-Point-Independent wavelet coefficients shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours, and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments, consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image, which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient, precise, and robust.