Hybrid Image Segmentation Using Edge Detection With Fuzzy Thresholding For Hand Gesture Image Recoginition

The need of computer recognition sign language image processing is essential for hearing impaired people. Preprocessing is very much required task to be done in hand gesture recognition system. In preprocessing process, the segmentation is an important part to detect the hand sign. The faculty of vision based gesture recognition to be a natural, powerful, and friendly tool for supporting efficient interaction between human and machine. In this paper new hybrid image segmentation is provided to detect the hand sign language image based on canny edge detection method and fuzzy c means clustering with thresholding technique. Here canny edge detection is applied to extract the finger tips of hand sign image accurately. Followed by fuzzy c means clustering method for final tuning of segmented image with better image quality of index. The   new method is validated with the parameters in terms of energy level, Entropy level, and Evaluation time (ET).  The experimental result shows that the proposed model works efficiently.