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Title: Nonparametric models for Image Segmentation

Speaker: Yunmei Chen

             (Florida University)

Abstract : We present a novel nonparametric
active region model for image segmentation. This model
partitions an image by maximizing the similarity between
that image and a label image, which is generated by setting
different constants as the intensities of partitioned sub-
regions. The intensities of these two images can not be
compared directly as they are of different modalities. In this
work we use R′enyi’s statistical dependence measure, maximum
 cross correlation, as a criterion to measure their similarity. By
using this measure the proposed model deals directly with independent
samples and does not need to estimate the continuous joint probability
density function. Moreover, the computation is further simplified by using
the theory of reproducing kernel Hilbert spaces. Experimental results
based on medical and real images are provided to demonstrate the
effectiveness of the proposed method.