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Though FCM has long been widely used in image segmentation, it yet faces several challenges. Traditional FCM needs a laborious process to decide cluster center number by repetitive tests. Moreover, random initialization of cluster centers can let the algorithm easily fall onto local minimum, causing the segmentation results to be suboptimal. Traditional FCM is also sensitive to noise due to the reason...
Image segmentation is a critical stage in many computer vision and image process applications. Accurate segmentation of medical images is very essential in Medical applications but it is very difficult job due to noise and in homogeneity. Fuzzy C-Mean (FCM) is one of the most popular Medical image clustering methods. We noticed that for some images, FCM is sensitive to initialization of centre of...
In experiments of soil mechanics, it is important to study the motion of sand grains at high pressures. Technologies of image segmentation and object tracking can help to resolve this problem. Aiming at the dense sand grains images, this paper proposes a method to track sand grains from image sequences. A novel approach of dense grain segmentation based on boundary exploration is proposed. Firstly,...
Visual analysis of human motion in video sequences has attached more and more attention to computer visions in recent years. In order to indicate pedestrian movement in Intelligent Monitoring System, a Euclidean distance based on centroid method is proposed. And then according to the movement of body a set of standard images contour are made. All matrixes which represent human silhouette are normalized...
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