Image Object Recognition Algorithm Research Based On Wavelet Transform And Moment Invariants

This paper focuses on the research of wavelet analysis and moment invariants'application in image object recognition. The concept of geometry moment and Hu'smoment, its property and its application in image recognition are described. Thetraditional moment invariants have the defect: these moments are the wholefeatures calculated from the whole image space, which are apt to disturb by noise.Aim at the above defect, a new moment invariant—wavelet moment is presented,which apply wavelet analysis to moment invariant. Thus wavelet moment possessthe image object's invariant to translation, scaling and rotation. By using waveletmoment invariant, not only the local feature of image object is obtained, but alsothe description ability for the fine features of image construct is improved. Thus,the higher recognition rate is obtained, especially similar images. In this paper,wavelet moments are used as the image features, then the features abstracted areoptimized and finally the features optimized are combined with BP neural networkclassifier to make object image recognition. Moreover, in the view of the whole,the method of local maximal modulus of wavelet transform is used for imagepre-process, based on which image region segment is made, to improve theaccuracy of region segment. In order to ensure veracity of the recognition result, this paper use 60 samples of3 classes of planes for training and testing. In the case of free-noise andnoise-added, we use Hu's moment and wavelet moment as features to test therecognition veracity. The experimental results demonstrate that the recognitionaccuracy and anti-noise capability of wavelet moment have been much increasing.