Resarch On The Feature Extraction Algorithm Of Image Detection And Recognition

In the process of human cognition and understanding of the surrounding environment,image becomes most intuitive and convenient means for human visual.So,the key of image analysis and understanding is how to get important information from the image.The emergence of image feature extraction solved the critical question effectively.The image information can be described usually by means of a descriptor for target characteristics,in which target feature represent the characteristics of the target area.Different types of image have the different target characteristics,so develop effective feature extraction algorithm which aim at different types of image is very important.Aimed at the feature extraction research and application algorithm of image,several new feature extraction algorithms were proposed,including based on gray statistical properties,signal processing and learning algorithm.The specific contents are as follows:Since the performance of edge detection algorithm is closely related with the quality of the original image,noise is the major interference factor,which means it needs preprocessing before image analysis,this paper introduced filtering method first,in order to get better effect in the chapter three.The second chapter first discusses some basis methods about denosing theory,which mainly includes Wavelet,Contourlet,Non-subsample Contourlet,Non-local means and median filter.And on this basis we combine the spatial domain and frequency domain to propose a NSCT image de-noising method with double filtering,which adopt different method to remove different noise.The simulation results show that,it can highlight the characteristics of image so that getting better results of edge detection.Secondly,for the feature extraction module of image processing,the feature extraction methods based on grey statistical property were proposed.This algorithm is aimed specially at unimodal pattern histogram of image.We combined the Canny edge detection algorithm and Rosin algorithm,then extended the traditional Rosin method to 2-D based on the defect that the traditional one dimensional Rosin algorithm cannot make full use of local structure information.The pixels itself and its neighborhood of local structure information are used to establish the 2-D histogram of image.Using the peak and the bottom point of histogram tostructure the whole angle rotation threshold selection plane and search the maximum value of space distance between histogram and every threshold plane,then got an adaptive threshold selection method.The test result show that the effectiveness of algorithm and expand the application scope of the canny algorithm.Thirdly,the feature extraction algorithm based on morphological component analysis(MCA)was proposed,by the implementation of MCA,the generated two image series of smooth piecewise edges and textures components can be described by suitable dictionaries,and the combination of smooth piecewise part and active contour model is achieved.Experimental results show that it can achieve the separation of texture feature and the purpose of Feature recognition and detection.Finally,Kernel Fisher discriminant analysis(KFDA)method has demonstrated its success in extracting features.However,a single kernel is not always suitable for the different applications which contain data from multiple,heterogeneous sources.To improve the performance of KFDA,a novel algorithm named multiple data-dependent kernel fisher discriminant analysis is proposed in this paper.The constructed multiple data-dependent kernel is a combination of several base kernels with a data-dependent kernel constraint on their weights.By solving the optimization equation,the parameters optimization of data-dependent kernel and multiple base kernels are achieved.Experimental results on the three face databases validate the effectiveness of the proposed algorithm.