| Traditional video standards such as MPEG-1, MPEG-2, H.261 or H.263 are low-level techniques in the sense that no segmentation or analysis of the scene is required. They can achieve high compression ratios and are suitable for a wide range of applications. However, with the increasing popularity of multimedia applications and content-based interactivity, new video coding schemes are necessary.The standard MPEG-4, which is currently being developed, enables content-based functionalities by introducing the concept of video object planes (VOP's). Each frame of the input sequence is segmented into arbitrarily shaped image regions (VOP's) such that each VOP describes one semantically meaningful object or video content of interest.Decomposing a video sequence into VOP's is very difficult in this field. An intrinsic problem of VOP generation is that objects of interest are not homogeneous with respect to low-level features such as color, intensity, or optical flow. Thus, conventional segmentation algorithms will fail to obtain meaningful partitions.This paper addresses video object plane generation and presents a new algorithm that can automatically extract moving objects from a sequence. Since these objects are characterized by a different motion from that of the background, some type of motion information must be incorporated into the segmentation algorithm.Optical flow or motion fields could theoretically be used, but they are extremely noise sensitive and their accuracy is limited due to the aperture and occlusion problem. Change detectors or difference images on the other hand mark occlusion areas as changed,while the objects themselves are unchanged unless they contain sufficient texture. This makes exact boundary location difficult and an additional mechanism is necessary to fill the holes inside objects.In this thesis we propose a set of algorithms which can efficiently segment sequences into video object planes. The proposed algorithms have three versions: one is based on morphological motion filter and active contour model, and another is based on difference of edges of moving objects and modified Hausdorff distance object tracker. Furthermore, if parts of an object only move occasionally , there is no sufficient motion information using morphological motion filter or difference of edges to identify the whole semantic meaningful object. We propose a new technique which is based on high correlation between edge of frame difference and edge of gray-level image. From collecting enough motion information it can form the binary edge of the whole object and can extract the VOP of the corresponded foreground object.As for the first VOP segmentation algorithm, the initial object is first extracted using morphological motion filter. The filter criterion is the difference between the global dominant motion and local motion. The initial moving object and its binary contour model is obtained after thresholding initial object and removing the noise. Then the binary contour model is tracked and matched to accommodate the change of position and shape of object using active contour model in the following frames. From a series of binary contour models we can extract the video object from the sequences. This algorithm is much suitable for sequences with little motion and stationary or moving background.For the second version VOP segmentation algorithm the moving foreground is identified using absolute difference between edge images first, and its binary edge model is obtained by cannyoperator . In order to accommodate the change of position of object the translation the object has undergone is indicated by the modified Hausdorff object tracker and a two-stage model update method accommodate changes in shape of the object in the following frames. Finally from a series of binary edge models we can extract the video object. This algorithm is much suitable for sequences with fast motion and stationary background.It is difficult to identify the whole semantic meaningful object which suffers from very little moti...
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