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In this paper, kernel fuzzy c-means (KFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, KFCM algorithm computes the fuzzy membership values for each pixel. On the basis of KFCM the edge indicator function was redefined. Using the edge indicator function the segmentation of medical images which are added with salt and pepper...
Clustering algorithms are increasingly employed for the image segmentation. By incorporating the spatial information and the term used in the punishing the distance, a new robust fuzzy c-means (NRFCM) algorithm was proposed for effectively improving the quality of the image segmentation. Its main characteristics are as follows:(1) The negative influence of the noise can be effectively reduced by using...
Traditional pest detection methods can't provide the grain pests' categories, densities and other parameters. In addition, with the increase of the stored-grain pests' drug fastness, their categories and densities are increasing in recent years, With the development of pattern recognition, image processing, intelligent algorithm, computer technology, develop a new detection system is necessary and...
The possibilistic C-means (PCM) was proposed to overcome some of the drawbacks associated with the fuzzy C-means (FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in the initial parameter values and to patterns that have relatively short distances between the prototypes. To overcome theses drawbacks, we propose an interval type-2 fuzzy approach...
The wide application of Binary segmentation for grayscale images could be found in computer vision and pattern recognition, especially for the purpose of object identification and recognition with industry and military images. This paper proposes a noise robust binary segmentation approach which incorporates Ant Colony System (ACS) with the modified Fuzzy C-Means (FCM) clustering algorithm. The ACS...
Fuzzy c-means algorithm with spatial constraints (FCM_S) is more effective for image segmentation. However, it still lacks enough robustness to noise and outliers, and costs much time in computation. To overcome the above problem, a new algorithm for image segmentation based on fast fuzzy c-means clustering is proposed in this paper. In order to reduce the number of iteration, the algorithm selects...
It is an important work to extract a garment style from garment photo, which can get much valuable information to guide manufacture and design for enterprises. But this research is so difficult that no one can study it successfully so far. This paper proposes a set of computer recognition method based on cluster pattern recognition and silhouette knot points detecting. Firstly, garment photos are...
Focusing on the problem that prior knowledge is always ignored in the Remote Sensing Classification by the unsupervised Fuzzy C-Means, a semi-supervised modified Fuzzy C-Means model for Remote Sensing image processing is proposed. The proper cluster centrals are obtained after a fast iteration going through the whole prior knowledge, which overcomes the affectation by the stochastic initializing the...
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