0 Object Recognition Using Multidimensional Receptive Field Histograms and Its Robustness to View Point Changes

This chapter presents a technique to determine the identity of objects in a scene using multidimensional histograms of the responses of a vector of local linear neighborhood operators (receptive elds). This technique can be used to determine the most probable objects in a scene, independent of the object's position, image-plane orientation and scale. The rst part of the chapter summarizes the mathematical foundations of multidimensional Receptive Field Histograms 1] and gives a recognition example on a database of 103 objects. The second part of the chapter describes experiments to evaluate the robustness of multidimensional receptive eld histograms to view point changes, using the Columbia image database 2]. In this experiment we examine the performance of diierent lter combinations, histogram matching functions and design parameter of the multidimensional histograms.