Object Shape Recognition

Abstract

Objects around us make our environment; in day to day life we tend to classify each of the objects visible to us. We tend to classify each object like a ball is spherical; a notebook is rectangular and so on using our senses. A machine like computer does not have senses to recognize or even detect an object. We have to train or develop an algorithm for a machine like computer to do so. The approach here is to classify some of the common objects around us and decide whether they belong to any geometric shape or not. The shape of the objects can be represented by some feature space which may be used for recognizing shape of the objects. We use the corner detection method, signature method and chain code method to achieve a good recognition. The corner detection method is based on detecting corners on the boundary and then deriving the feature vector from the distance between the corners. The signature method is based on the distance of the boundary points from the centre of the object and all those distances from the feature vector. The chain code method is based on finding the chain code of the object and then finds the histogram of it, which forms the feature vector. The purpose of this thesis is to use all the three methods of recognition and visualize their performances.