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Texture segmentation by Pseudo Jacobi -Fourier moments is presented in this paper. Given a window size, moments for each pixel in the image are computed within small local windows, and then texture feature images be obtained by using a nonlinear transducer. Finally, each pixel in the image is classified by K-mean clustering algorithm.
We propose an automatic moment-based image recognition technique in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering...
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