A Review on Gesture Segmentation and Classification

The aim is to bring Human Computer Interaction to a regime where interactions with computers will be as natural as an interaction between humans. HCI techniques like keyboard, mouse, joysticks etc. Hand segmentation is the most crucial step in every hand gesture recognition system. Hand segmentation which overcomes problems such as skin colour detection One of the major concerns with respect to hand gesture recognition is segregation or segmentation of the hand and identifying the gesture. the various possible ways of segmentation using different colour spaces and models and presents the best algorithm with highest accuracy to perform the human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm has three stages: pre-processing, feature extraction and classification. The pre-processing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to build the precise algorithm, filtering that affectfully retains background noise and object noise from binary image by morphological filtering technique. The 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. The k-mean supported radial basis operational neural n/w is also presented here for subdivided of hand gestures from LCS based feature. We use HSV (Hue Saturation Value) colour space mix with skin detection to remove the complex background and create segmented images.