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This paperpresent a novel framework for inferring global behaviour patterns through modelling behaviour correlations in a wide-area scene and detecting any
anomaly in behaviours occurring both locally and globally. Specifically,
This paperpropose
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This paper addresses the problem of segmenting an image into regions. We de¯ ne
a predicate for measuring the evidence for a boundary between two regions using a
graph-based representation of the image. We then develop an e±cient segmentation
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In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the minimal cost criterion. Starting from an over-segmented image, neighboring regions are progressivel
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Global and local methods of unsupervised Byesian segmentation of images
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Image segmentation by region growing method is
robust fast and very easy to implemented, but it suffers from:
the threshold problem, initialization, and sensitivity to noise.
Genetic algorithms are particular methods for optimizing
functions
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提出一种利用视觉显著性对图像进行分割的方法。首先提取图像的底层视觉特征，从局部显著性、全局显著性和稀少性3个方面计算各特征图像中各像素的视觉显著性，得到各特征显著图；对各特征显著图进行综合，生
成最终的综合显著图。然后对综合显著图进行阈值分割，得到二值图像，将二值图像与原始图像叠加，将前景和背景分离，得到图像分割结果-It presents a significant advantage of visual image segmentation method. First, extract
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