| Summary: | 碩士 === 義守大學 === 資訊工程學系碩士班 === 93 === Active contour model (ACM) is an important contour segmentation method for image processing especially for low contrast and high noised image such as medical and remote sensing images. Traditional methods adopt the combination of internal and external energies, which is combination of optimized so as to determine the contour. Such methods require sequential computations composed of a vest amount of convolution-wise operations with if-then optimization branchings. Therefore, it is time consuming. This project proposes a cellular neural network method to implement ACM, which provides parallel computation in the CNN universal machine (CNN-UM) architecture. The proposed CNN method can follow the local rules of the optimization in searching the contour. Moreover, it takes into account the global properties of images to introduce noise-proof mechanism, which is intrinsic done by propagation. As a consequence faster computation can be achieved and robust segmentation can be obtained against to low contrasts and high noises.
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