This paper introduces a new contour-based shape descriptor for both simple and complex trademark retrieval. The proposed approach not only overcomes the conventional limitation of a contour-based shape descriptor but also outperform the conventional descriptors. In the paper, first, we use the well-known morphological operation to extract the boundary points from a binary trademark. Then, a sorting labeling method is applied for computing the similarity. Experimental results show that the proposed two methods are robust and it reduces the complexity of extracting features significantly. This paper presents an image registration technique in frequency domain. We apply phase correlation to find the transition between images. However, phase correlation can find only translation onx-y axis. We have to apply other technique before performing phase correlation to fine an angular transition. Therefore, hough transform technique is implemented to overcome this problem. In this paper, images will be pre-processed using wavelet transform, Multi-resolution property of wavelet transform can help reducing processing time. The experiments are conducted to show the proposed structure of wavelet; such as number of levels; selected sub-band and other parameters, suitable for image registration.For medical image sequences, the method of contour-based tracking proved to be a powerful tool for boundary delineation. During contour evolution, the Particle Filter (PF) 111 can be used to track the feature points by enforcing spatietemporal local constraints to handle the observation noise. To optimize the importance ratios OR) of PF and improve its capability, a new approach of Generalized Fuzzy Particle Filter (GFPF) is presented. Compared with the Unscented Particle Filter (UPF) 12, 31 that is currently a good method for object tracking, GFPF shows more advantages, including lower particles degeneracy, higher precision and so on. In addition, a likelihood estimation model is constructed to provide the observation data for GFPF. By theoretic analysis and contrast experiments, it is clear that GFPF is a good method for left ventricle tracking.