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This paper presents a survey of latest image segmentation techniques using fuzzy clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for image segmentation because of its robust characteristics for data classification. In this paper, four image segmentation algorithms using clustering, taken from the literature are reviewed. To address the drawbacks of conventional...
A robust foreground object segmentation technique is proposed, capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds. The method employs contextual spatial information by analysing each image on an overlapping patch-by-patch basis and obtaining a low-dimensional texture descriptor for each patch. Each descriptor is passed through an adaptive multi-stage...
The fuzzy c-means clustering algorithm has been successfully applied to a wide variety of problems. However, the image may be corrupted by noise, which leads to inaccuracy with segmentation. In the paper, a local fuzzy clustering regularization model is introduced in the objective function of the standard fuzzy c-means (FCM) algorithm. It can allow the membership of a pixel to be influenced by the...
This paper describes developing of an occlusion robust tracking algorithm of pedestrians in the panning images by the combination between the S-T MRF model and pattern recognition methods of Snakes and HOG classifier. Tracking in panning images would extend the field of view of single camera. In addition, an algorithm to match pedestrians between cameras that have overlapping area with each other...
Speeded-Up Robust Features (SURF) is a novel scale-invariant and rotation-invariant feature. It is perfect in its high computation speed and robustness. In this paper, we apply SURF in SAR image matching accord to its characteristic, and then acquire its invariant feature for matching in an addition of no any pre-processing. In the process of image matching, we use the nearest neighbor rule for initial...
This paper presents a supervised foreground segmentation method that uses local and global feature similarity with edge constraint. This framework integrates and extends the notion of region growing and classification to deal with local and global fitness. It parameterizes constraint of growing using Chebyshev's inequality. The constraint is used to stop segmentation before matting. Matting relies...
A novel robust watermark embedding and extracting algorithm in ridgelet domain is proposed. Since the ridgelet transform (RT) can efficiently represent image with linear singularities and has directional sensitivity, the image is first partitioned into small pieces. Firstly these small pieces are classified to different characteristic categories (with weak texture, strong texture) according to the...
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