ABSTRACT

Segmentation is the classification of the input colored image into skin and non-skin pixels based on skin color information. A wide range of applications that require the segmentation process as a preprocessing operation such as computer vision, face/ hand detection and recognition, medical image analysis, and pattern recognition. Color information is one of the simple cues used for detecting skin color, and the use of proper color space to represent color information of an image is a crucial decision. In this literature different segmentation techniques are presented, examples and comparison between the main three based segmentation techniques are given as well. Skin color modeling based statistical model is explained in detail, with discussion the combination with different segmentation techniques. The selection of appropriate segmentation method depends on the application and system environments. The performance of any segmentation algorithm is quantified using some benchmarking such as recall and precision coefficients, or by calculating the percentage of correct and false detection rates according to the complexion of the technique used.

No abstract

Hand gesture became second language that complements almost many speeches, encounters, lectures, as well as friends chatting even in computer chatting they may gesturing to each other since it is a rotted habit in our behavior, we can notice that even if someone was sitting alone and thinking; he will continue gesturing during his meditation, however, the imitating of this natural behavior is an important issue for transferring this behavior to the human made machines and the intuitive interface will not be changed as compared to human-human communication, in this paper, we have applied a novel approach for recognizing the hand gesture and to maximize the level of unrestricted communication by solving the problem of rotation invariance that matters, we have employed a Gaussian bivariate likelihood function for hand modeling and features fitting and to produce uniform features that can be a reference for gesture database, we have achieved remarkable recognition percentages using 20 different gestures with a high speed recognition time, our system can be used for real time applications in which the time factor is important issue for the success of such systems, we have made a comparative study with some other known gesture algorithms as well.

Statistical approaches become very important tools that interfere and overlap in our daily life and become inevitable event that help us in every tiny details of our life, in this paper; we are going to present a new technique for analyzing the two principal component of any given object by calculating the direction over the occupied coordinates using mean, variance, and covariance statistical functions, and by finding some relationship between those statistical functions; we have extracted the angle degree of the processed object, for pattern recognition applications; this object can be adjusted accordingly to overcome the rotation perturbation shortcoming that hinders the extraction of a unified features especially for object recognition purposes in which we have to present many samples per single pose which makes the processing of this increasing size of the database is a noticeable burden, we have achieved a dramatically results with almost zero time of calculation since the statistical functions applied need little processing time to finish.