Literature survey on Image Segmentation and Shape Analysis for Road-Sign Recognition

Road sign detection has been an important issue for research recently The goal of this work is to find and classify road signs. In developing a new algorithm for road-sign detection and recognition, we think that any road-sign recognition algorithm should be as general as possible to work with large groups of road signs.. This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition.