SYNERGISTIC FIBROBLAST OPTIMIZATION BASED BOUNDARY DETECTION IN TAMIL SIGN LANGUAGE IMAGES

Sign Language (SL) is a three dimensional language used for communication by deaf people. The recognition system for SL is always an apprehensive task which is handled by vision collaboration and technology. Basically, detection of edges is deliberated to be the precursor for detection of objects, as the edges are the outline of the objects. Detecting continuous edges in real time images is a hard problem, especially in Tamil Sign Language (TSL) recognition system. This paper proposes an algorithm which finds optimal threshold values (L and H) based on Synergistic Fibroblast Optimization (SFO) for detection of continuous, smooth and thin edges of TSL hand pose images. A novel SFO algorithm is proposed with sphere objective function and two constraints for reducing the ruined edges. The efficiency of the algorithm is compared experimentally with conventional Canny, Classical PSO and variant based PSO on TSL Consonants images. Experimental results suggested that the novel SFO based canny operator detects edges more accurately, and the edges detected are smoother and thinner when compared to other analyzed algorithms.