| In this thesis, we describe a new algorithm for color image segmentation and a novel approach for image sequence segmentation.; The color image segmentation algorithm can be used for image sequence intra-frame segmentation, and it gives accurate region boundaries. Because this method produces accurate boundaries, the accuracy of motion boundaries of the image sequence segmentation algorithms may be improved when it is integrated in the sequence segmentation framework.; To implement this algorithm, we have also developed a new multiresolution technique, called the “Narrow Band”, which is significantly faster than both single resolution and traditional multiresolution methods. As a color image segmentation technique, it is unsupervised, and its segmentation is accurate at the object boundaries. Since it uses the MRF and mean field theory, the results of the segmentation are smooth. Good results are shown in dermatoscopic images and image sequence frames.; Our new approach to image sequence segmentation contains three parts: global motion compensation, robust frame differencing, and curve evolution. In the global motion compensation, we adopt a fast method, which needs only a sparse set of pixels evenly distributed in the image frames. Block-matching and regression are used to classify the sparse set of pixels into inliers and outliers according to the affine model. With the regression, the inliers of the sparse set, which are related to the global motion, will be determined iteratively. For the robust frame differencing, we used a local structure tensor field, which robustly represents the object motion characteristics. With the level set curve evolution, the algorithm can detect the all moving objects and circle out the objects' outside contours. Our approach is computationally efficient, does not require a dense motion field, and is insensitive to global/background motion and to noise. Its efficacy is demonstrated on both TV and surveillance video. |
