Research On Object Recognition Based On Invariant Eigenspace Method

The aim of Computer Visual System is the interpretation for the existing "Visual" data, which can be utilized to complete tasks. Nowadays, object recognition has become an active research field, in need of a wide variety of practical applications, such as robots' work piece snatching at tasks, auto-navigation, auto-detection, tasks equipment and analysis of medical image, and so on. Presently many methods of object recognition are mainly based on the techniques of the template matching and the classification of the pick-up for the characteristic points of the geometric shape, which possess a large number of disadvantages. For instance, a kind of method based on image matching directly compares the sum of gray of corresponding pixel in two images. But this discriminating method is not provided with the inflexibility of direction, scale and geometric aberrance, so it demands that object image and the sample image must be of strong relativity. Adopting the appropriate method to get over the aberrances of the target and background is one of the difficulties to the object recognition field. The feature extraction is one of the key problems of pattern recognition. Maybe the amount of original features is very large, sample objects maybe situate in high dimensional space, but they can be expressed in low dimensional space by mapping or transforming method or etc. We aim at classifying and recognizing objects much better in low dimensional space. This thesis mainly researches the methods of improving recognition ratio and quickening recognition speed by extracting feature information benefited to recognition from sample objects as much as possible, while the objects and backgrounds are under the aberrant conditions such as having pose variety, noise disturbance, partial occlusion, illumination alteration and etc. Firstly, this thesis lucubrates in the theory of eigenspace and does a lot of experiments and further researches in the two steps, character expressing and measuring of similarity. The object recognition system based on eigenspace was fulfilled, which adopts some effective and ameliorative means to solve the problems existing in the course of recognition such as illumination and pose estimation, and finally enhances the robustness of recognition. After finishing arithmetic of the subspace object recognition, in order to effectively extracting classifying imformation of different patterns, more research work on linear discriminant analysis methods based on Fisher discriminant criterion was done. The basis of Fisher optimal discriminant vectors method is projecting high dimensional pattern samples over optimal discriminant vectors space so as to reducing the dimensions of feature subspaces. After projecting they have maximum between-class distance and minimum within-class distance, that is, patterns have the best separability in these new feature subspaces. While coming down to high dimensional recognition, Fisher discriminant criterion has its limitation, it often encounts "Course Dimensionality"problem. At last, this thesis discusses locally linear embedding algorithm based on manifold learning method. This algorithm is a new reducing dimension method mainly focus on non-linear data. It can both reducing dimensions and maintain the topology structure of original data. As a new reducing dimension method, it has the merit of processing both non-linear and linear data, and is becoming a research hotspot in reducing dimensions of non-linear data, clustering and image division area ,etc. This thesis combines locally linear embedding method with Fisher linear discriminant analysis method to classify and recognize objects. Experiment results demonstrate that the combination method is a new effective recognition method having the merits of quick recognition speed and high recognition ratio.