Chin-Ya HuangMon-Ju WuECE 533: Project ProposalImage Segmentation in SportPurposed TopicImage segmentation is an important technology for image processing. There are many applications whether on synthesis of the objects or computer graphic images requiring precise segmentation. With the consideration of the characteristics of each object composing images in MPEG4, object-based segmentation cannot be ignored. Nowadays, sports programs are among the most popular programs, and there is no doubt that viewers’ interest is concentrated on the athletes. People most likely want to see more detail or clarity of the athletes’ movements in images of a football game, the posture of anathlete who shoots the three point basket in an NBA tournament or the movement of a baseball in a major league game. Therefore, demand for image segmentation of sport scenes is very high in terms of both visual compression and image handling using extracted athletes. There are many algorithms used for image segmentation, and there doesn’t seem to be an optimal one proposed. According to class notes, we propose to segment images by using boundary extraction to get the shapes of athletes, and then compare the results by using the method in [3]. The comparison is based on the executiontime for a series frames, and the segmentation error probability. Since they may segment images automatically, they are useful for broadcasting during the games or establishing the video database on objects composing the image sequences.MethodsTo compare the difference of image segmentation by applying different methods, ourapproaches are as follows:(1) Segment images by applying the concept of boundary extraction with the formula)()( BAAA . Afterwards, we try to find a way to improve the precision ofimage segmentation.(2) Segment images by applying the concept of [3]. We first calculate the colorhistogram to figure out the suitable HSI for image segmentation. Afterwards, weperform course image segmentation with the information of color histogram, andthen apply marker watershed transform to get the fine image segmentation.(3) Compare the results of image segmentation of applying different methods and compare the advantages and disadvantages of both methods. References[1] Class notes [2] Text book.[3] M. Naemura, A. Fukuda, Y. Mizutani, Y. Izumi, Y. Tanaka, and K. Enami, “Morphological Segmentation of Sport Scenes using Color Information, ” IEEE Transactions on broadcasting, vol. 46, no. 3, Sep. 2000.[4] M. Tabb and N. Ahuja, “Multiscale Image Segmentation by Integrated Edge and Region Detection, ” IEEE Transactions on image processing, vol. 6, no. 5, May. 1997.[5] F. Meyer, “Color image segmentation,” in Proc. Int. Conf. Image Processing, Maastricht, The Netherlands,
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