Rokade proposed RGB segmentation which is more sensitive to light conditions and the threshold value for conversion of output image to binary image that value is different for different lighting conditions. The limitation for YCbCr segmentation method is that background should be plain and uniform. This method is based on obtaining the image through subtract one image from another sequential image, by measuring the entropy, separating hand region from images, tracking the hand region and recognizing hand gestures. Lee proposed a method to recognize hand gestures extracted from images with complex background for more natural interface. The adaptive method of automatic threshold selection based on the method of maximal between-class variance is proposed for hand gesture segmentation to select optimal threshold. The novel method proposed in is based on difference background image between consecutive video frames, of using the “3σ-principle” of normal distribution for hand gesture detection to cope with the problem. Therefore, the quality of the foreground and the segmented image of hand gesture severely drop. The video streams of backgrounds are frequently influenced by the background changes such as illumination changes and changes due to adding or removing parts of the background. Among natural human gestures occurring during non-verbal communication, pointing gesture can be easily recognized and included in more natural new human computer interfaces. As well as it is considered as a very important function in many practical communication applications, such as sign language understanding, entertainment, and human computer interaction (HCI). Sign language recognition is a comprehensive problem because of the complexity of the visual analysis of hand gesture and the highly structured nature of sign language. Sign language recognition is a research area involving pattern recognition, computer vision, natural language processing. Recognition of sign language is one of the major concerns for dump and deaf people. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a “minimum-possible constraints” approach. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This paper includes experiments for 26 static hand gestures related to A-Z alphabets. Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Second stage extracts hand region using blob and crop is applied for getting region of interest and then “Sobel” edge detection is applied on extracted region. First stage converts captured RGB image into binary image using gray threshold method with noise removed using median filter and Guassian filter, followed by morphological operations. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. Experimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue color space from fixed distance. In this work, real-time hand gesture system is proposed. Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people. Keywords: Image Preprocessing Region Extraction Feature Extraction Median Filter ASL Society’s College of Engineering, Pune, India 3Department of Computer Engineering, School of Computer Engineering, Devi Ahilya University, Indore, India.Įmail: May 8 th, 2012 revised June 5 th, 2012 accepted June 14 th, 2012 Society’s College of Engineering, Pune, India 2Department of Mechanical Engineering, M.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |