Generation of Random Fields for Object Recognization using Binarization Technique

Abstract Object Recognization plays an important role in Computer Vision. In the literature many methods have existed but no method is suitable for object recognization with shadow, noise, and low contrasted images. In this paper, a new binarization technique has been proposed to recognize the object even with shadow, noise and low contrasted images. This technique contains three steps. First step is preprocessing, second step is Binarization and third step is Recognization. Preprocessing can be implemented using normalization, edge perseverance. In Binarization has been done by using phase congruency model and the then object recognization is done by optimum parameters. The experimental results show that the proposed method is more efficient for generation of random fields for object recognization than the existing IOOPL method.