The Research On Object Recognition Base On Modified BP Neural Network

Computer vision finds wide application in multiple areas such as household intelligence robots, automatic inspection of instruments, automonous navigation of automobiles and object recognition of aero-sensing images, and sees a brighter prospect in the bloom of a variety of related techniques, Yet object recognition is one of the most important problems in computer vision.Furthermore,to object recognition, feature extraction and classification are pivotal.Object recognition faces a great many challenges in method.One problem is how to identify the object rapidly and accurately from two dimensional images.This task could easily resolved by human visual system which infers from an image of two dimensional and apriori assumptions. This paper puts forward an algorithm of object recognition based on modified BP Neural Network from the shape information of the object.About feature extraction,invariant feature is extracted by moments.Hu moments and the modified algorithm are dicussed amply in the paper.Invariant moments can reflect the shape of object,possess the capability of resisting noise,and is not influenced by the size, position and orientation.Therefore, invariant moments are widely applied on object recognition, scenery matching,image analysis, character recognition,and so on. The modified Hu invariant moments are invariant to the translation , rotating and scale of object,when object is in sequential state or discrete state.Moreover they have small time complexity.So they can recognize object efficiently. This method is examined in the MATLAB laboratory environment.About classification and recognition,the structure,algorithms and shortcomings of BP neural network are introduced.Moreover,adding momentum,conjugate grads, regularization, stretch BP algorithm, back propagation algorithm based on self adapting learning rate with momentum gradient reduction are presented in the paper to meet different problems. Especially back propagation algorithm based on self adapting learning rate with momentum gradient reduction can avoid efficiently the slow convergence and the problem of'local infinitesimal value'of BP network. The modified BP algorithm is examined in the MATLAB laboratory environment, to recognize objects in Coil-20.The experiments are performed in the noise-free environment,as well as in the noisy environment.Compared with object recognition based on traditional BP algorithm,the modified BP algorithm improves the capability of processing nonlinear and uncertain problems.and it has higher recognition rate and quicker convergence in the noise-free environment as well as in the noisy environment. Consequently the feasibility, robustness, efficiency are proved in the paper.