A Review on Indian Sign Language Recognition <s> Preprocessing <s> Otsu reference proposed a criterion for maximizing the between-class variance of pixel intensity to perform picture thresholding. However, Otsu's method for image segmentation is very time-consuming because of the inefficient formulation of the be- tween-class variance. In this paper, a faster version of Otsu's method is proposed for improving the efficiency of computation for the optimal thresholds of an image. First, a criterion for maximizing a modified between-class variance that is equivalent to the criterion of maximizing the usual between-class variance is proposed for image segmen- tation. Next, in accordance with the new criterion, a recursive algorithm is designed to efficiently find the optimal threshold. This procedure yields the same set of thresholds as the original method. In addition, the modified between-class variance can be pre-computed and stored in a look-up table. Our analysis of the new criterion clearly shows that it takes less computation to compute both the cumulative probability (zeroth order moment) and the mean (first order moment) of a class, and that determining the modified between-class variance by accessing a look-up table is quicker than that by performing mathematical arithmetic operations. For example, the experimental results of a five-level threshold selection show that our proposed method can reduce down the processing time from more than one hour by the conventional Otsu's method to less than 107 seconds. <s> BIB001 </s> A Review on Indian Sign Language Recognition <s> Preprocessing <s> This paper presents an algorithm for segmenting and tracking moving objects in a scene. Temporal information provided by a region tracking strategy is integrated for improving frame-to-frame motion segmentation. The method has been applied to a tra$c monitoring system and it provides facilities such as estimating trajectories of vehicles, detecting stopped vehicles, counting vehicles and estimating the mean velocity of the tra$c. ( 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. <s> BIB002 </s> A Review on Indian Sign Language Recognition <s> Preprocessing <s> An algorithm for segmenting skin regions in color images using color and edge information is presented. Skin colored regions are first detected using a Bayesian model of the human skin color. These regions are further segmented into skin region candidates that satisfy the homogeneity property of the human skin. We show that Bayesian skin color model outperforms many other models such as the piece-wise linear models, Gaussian models and model based on multilayer perceptrons. Experimental results indicate that the proposed segmentation algorithm reduces false detection caused by background pixels having skin colors, and more significantly it is capable of separating true skin regions from falsely detected regions. <s> BIB003
A preprocessing step is carried out on the training images to extract the region of interest (ROI). The ROI can be hands if only hand gestures are considered or both face and hands if the facial gestures are also included. Usually the preprocessing step consists of filtering, image enhancement, image resizing, segmentation and morphological filtering. Filtering and image enhancement can be any one of the commonly used methods. For segmentation, the algorithm that better suits the input video/images has to be selected. Otsu's thresholding BIB001 , Background subtraction  , skin color based segmentation BIB003 and motion based segmentation BIB002 are the commonly used segmentation techniques. During testing phase, the test images or videos are also preprocessed to extract the region of interest.