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Articles by Yunhua Zhang
Total Records ( 4 ) for Yunhua Zhang
  Yaming Wang , Yunhua Zhang , Li Cao , Weida Zhou and Wenqing Huang
  To segment the moving objects from image sequence is an important problem in computer vision. In this paper, a novel approach is proposed for moving objects segmentation from grayscale image sequence. In order to increase the robustness of segmentation, the information-theoretic principle of mutual information is proposed to combine two different segmentation results. First, the adaptive Gaussian distribution model for each image pixel is presented. Based on this model, each image frame is mapped from spatial domain to statistic domain. The segmentation is consequently performed in statistic domain. Meanwhile the image frame is segmented using Otsu`s method (Otsu, N., 1979) This two segmentation results are then combined by using mutual information and the final result is obtained. Experimental results from a real image sequence show the feasibility of the proposed approach.
  Yunhua Zhang , Yaming Wang , Sun Li and Wenqing Huang
  This study uses neural networks to estimate three-Dimensional (3D) rigid motion parameters based on two-Dimensional (2D) motion fields. The motion fields are computed from image sequences. The neural networks update their weights by Newton-Raphson procedure for minimizing the error measures. Experimental results are presented for validating the proposed approach.
  Yunhua Zhang , Yaming Wang and Wenqing Huang
  A novel approach to 3-D motion estimation of elastic body from monocular image sequence is proposed in this paper. First, with the establishment of feature point correspondence between consecutive image frames, the affine motion model and the central projection model are presented for local elastic motion. Then, in order to obtain the global motion parameters and overcome the ill-posed 3-D estimation problem, a framework of Markov Random Field (MRF) with entropic constraints is proposed. By incorporating the motion prior constraints into the MRF, the motion smoothness feature between local regions is reflected. This converts the ill-posed problem into a well-posed one and guarantees the robust solution. Experimental results from a sequence of synthetic image sequence demonstrate the feasibility of the proposed approach.
  Yaming Wang , Li Sun , Jueliang Hu , Yunhua Zhang and Wenqing Huang
  3D rigid motion estimation from images is crucial for many important applications. In this study, an approach is proposed for 3D rigid motion estimation from feature points using feed-forward neural-networks. The correspondence of feature points between consecutive images is assumed to be established beforehand. The proposed neural network is composed of 3 layers and 3 points are randomly selected from all points on the object to train the network using Newton-Raphson procedure. Experimental results from synthetic data are presented for validating the proposed approach.
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