| Summary: | 碩士 === 淡江大學 === 電機工程學系碩士班 === 93 === Many video compression standards, such as MPEG-4 and MPEG-7, have supported object-based multimedia coding that allows user to interact, search and exchange the objects in the images or video sequences. For supporting these features, the object segmentation and tracking in the video sequences play an essential and important role. This thesis proposes a solution algorithm to track one or multiple moving objects in frames of a video sequence, including edge detection algorithm, image segmentation algorithm and trajectory estimation functions.
The object segmentation algorithm proposed in this thesis is based on exploring contour of the image. Therefore, to extract the desired objects with more precisely, an effective method for extracting the contour of the image is needed. The conventional edge detection algorithm is no longer satisfy there requirements. For example, Sobel’s edge detector can successfully sketch out the apparently contour of the objects. However, most of the thin edges in the image normally be eliminated by Sobel’s edge detector. The DoE (difference of exponential) method is able to track the stronger edges of the image, but the performance of extracting thin edges is not acceptable. However, in many cases, thin edges represent important features in the image, and should not be eliminated or discarded. This thesis presents a novel mathematical morphology based edge detector to enhance the performance of extracting thin edges in a still image. According to the mean value and standard derivation of the pixel in the image, the proposed method can enhance thin edges in the image for extracting by a global threshold value.
After the edge detection process, this thesis proposes for applying a novel object segmentation algorithm to the image for extracting objects of the image. There are several popular algorithm had been developed for image segmentation, such as snake energy model and watershed algorithm. For snake energy model, it requires a manually-drawn initial snake and adjusts weighting parameters in the snake model. The snake is controlled by two energies, which are internal energy and external energy. The snake iterations are converged when these two energies reach to a balanced state. As to the watershed algorithm, it has the drawback of over-segmentation problem. This thesis presents a novel edge-based image segmentation algorithm that is capable of performing global image segmentation or segmenting desired objects in an image. The proposed algorithm provides more effective segmentation result than other methods by region growing method. A snake-energy-like cost function is developed to control the growing process for the algorithm to produce better segmentation results. While the growing phase is completed, the algorithm combines homogeneous regions together to extract more meaningful image objects.
The segmentation algorithm proposed in this thesis is initialized by planting growing seeds into the image. Therefore, to extent our algorithms to video object tracking, this study proposes a scheme based on the previously segmentation result for automatically planting growing seeds into following video frames to extract the same objects on the following frames by the proposed scheme.
Experimental results show that the proposed algorithms produce good performance on object segmentation and tracking.
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