| The dissertation is the study of Gabor wavelet neural network algorithm and its application in gray image target recognition. It is involved with the neural network theory, the wavelet analysis theory ,the wavelet neural network and their application technology of the target recognition. The key algorithms and technologies in the system realization are studied and discussed from the theory and practice , the aim of the research is the designing and realizing the neural networks target system based on many CPU .This thesis mainly consists of the following parts.the actuality of the BP neural network algorithm, the wavelet transforms theory and Gabor wavelet neural network algorithm and their application of target recognition in the world are introduced. The problems are analyzed. The basic theory and the key technologies are expounded, the main toll route are gray image target recognition.The multiplayer forward neural network and its training algorithm are thorough analyzed, Error back propagation algorithm is derived from the mathematic , the problem of BP algorithm is indicated .The improved BP algorithm with many target recognition is constructed.The principle of Gabor filter is expounded. The expression of Gabor wavelet filter is presented. The multi channel Gabor filter is designed based on theory and practicality, the texture features of gray image target are extracted. The neural network recognizing algorithm based on multi channel Gabor filter feature is presented .The define of phase congruence (PC) invariant is introduced. The mathematic process of deriving phase congruence (PC) invariant is discussed from theory ,the formula of PC invariant is presented.The performance of log gabor wavelet is detailedly analyzed, the modified formula of PC invariant is presented by the log gabor wavelet. Finally.the new algorithm of neural network recognition based on the PC invariant is designed.The three target representations are expatiated, that are the target representation based on the feature , based on the template and based Gabor wavelet neural network, the advantage and disadvantage are discussed. The construct method of Gabor wavelet neural network is expounded. The training algorithm of Gabor wavelet neural network is constructed,the theory analysis and the concrete step of algorithm are presented.The mostly thought t are real time recognizing gray image target with Gabor wavelet neural networks algorithm. The train of thoughts are the forward neural networks (BP net) and Gabor wavelet are organically combined based on they were applied in target feature extraction and recognition. A model of Gabor wavelet neural network is constructed with automatic target recognition, the real time process aim is realized with the automatic target recognizing system applying optimizing theory and self-adapt technique. The neural networks ensemble algorithm is realized with neural network processing system designed based many CPU ,and the good impact is gained when it is applied target recognition .Mostly research findings and innovations are focused on :(1) The improved BP neural networks training algorithm was given out suit to many CPU target recognition system Principally with alter step and input vector , excitation function was adjusted from theory, finally this algorithm was realized in target recognizing system based on many CPU.(2) According to multi resolve principle,a new multi channel filter based Gabor wavelet was designed. It can extract the feature of low quality gray image target, and had good robust. Its center frequency was the range from low frequency to high frequency, its orientation is 6 and scale is 4. Gary image was directly transformed by these wavelet filters, the feature of extracting gray image target was denoted by the coefficients of Gabor wavelet transform and its standard variance, the wavelet feature was input to the improved BP neural networks to classify.(3) The method of image segment and edge feature extraction of low level was put f...
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