We use prior and boundary estimates as the approximation of outside probability and establish our beam thresholding strategies based on these estimates. Lexical items, e.g. head word and head tag, are also incorporated to lexicalized prior and boundary estimates. Experiments on the Penn Chinese Treebank show that beam thresholding with lexicalized prior works much better than that with unlexica...

Thresholdging video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detect...

This paper presents a novel wavelet-based denoising method using coefficient thresholding technique. The proposed method uses the adaptive thresholding which overcome the shortcomings of discontinuous function in hard-thresholding and also can eliminate the permanent bias in soft-thresholding. The qualitative evaluation of the denoising performance has shown that the proposed method cancels noi...

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 segmentation is the technique in which an image into meaningful parts .It plays an important role in the image analysis and computer version. GA algorithm are evolutionary in nature so, it proved to be very time consuming. So, to overcome the limitation of the GA based on the multilevel thresholding, Ant Colony based Optimization on multilevel thresholding segmentation algorithm will be p...

This paper presents a new signal denoising method based on the classical three step procedure analysis-thresholdsynthesis and the Spectral Intrinsic Decomposition (SID). This method consists of an iterative thresholding of the SID components. If the wavelets denoising approach depends on the choice of the wavelet form, the SID-denoising proposed in this paper is self adaptive. The SID-based rem...

In this paper, we compare Fourier-based and wavelet-based denoising techniques applied to both synthetic and real experimental geophysical data. The Fourier-based technique used for comparison is the classical Wiener estimator, and the wavelet-based techniques tested include soft and hard wavelet thresholding and the empirical Bayes (EB) method. Both real and synthetic data sets were used to co...

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 presents a novel image thresholding algorithm, termed as random spatial sampling and majority voting based image thresholding algorithm (RMIT). RMIT firstly obtains a population of binary subimages by using random spatial sampling and Otsu’s thresholding algorithm [1]. Then RMIT aggregates these binary subimages into a consensus binary image by majority voting technique. Since the su...