Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding methods using neighborhood processing is presented. Local adaptive techniques are more effective in eliminating both uneven lighting disturbance, noise and ghost objects. In order to demonstrate the effectiveness, locally adaptive thresholding methods namely Niblack, S...

Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it...

This paper presented a hybrid optimal estimation algorithm for solving multi-level thresholding problems in image segmentation. The distribution of image intensity is modeled as a random variable, which is approximated by a mixture Gaussian model. The Gaussian’s parameter estimates are iteratively computed by using the proposed PSO + EM algorithm, which consists of two main components: (i) glob...

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surf...

This paper provides an hybrid method for segmentation using FCM thresholding with fuzzy levelset . FCM thresholding gives fine segmented results when compared to edge detection method. The optimization property of FCM is improved when it is combined with local thresholding. The application of Fuzzy levelset gives enhanced segmentation results. The experimental result proves that the hybrid meth...