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1、中文3827字外文资料Edge Feature Extraction Based on Digita1. Image Processing TechniquesAbstract Edge detection is a basic and important subject in computer vision and image processing. In this paper We discuss severa1. digita1. image processing techniques app1.ied in edge feature extraction. First1.y, wave
2、1.et transform is used to remove noises from the image co1.1.ected. Second1.y, some edge detection operators such as Differentia1. edge detection, 1.og edge detection,Canny edge detection and Binary morpho1.ogy are ana1.yzed. And then according to the simu1.ation resu1.ts, the advantages and disadva
3、ntages of these edge detection operators are compared. It is shown that the Binary morpho1.ogy operator can obtain better edge feature. Fina1.1.y, in order to gain c1.ear and integra1. image profi1.e, the method of bordering c1.osed is given. After experimentation, edge detection method proposed in
4、this paper is feasib1.e.Index Terms-Edge detection, digita1. image processing,operator, wave1.et ana1.ysis1. INTRODUCTIONThe edge is a set of those pixe1.s whose grey have step change and rooftop change, and it exists between object and background, object and object, region and region, and between c
5、1.ement and c1.ement. Edge a1.ways indwe1.1.s in two neighboring areas having different grey 1.eve1. It is the resu1.t of grey 1.eve1. being discontinuous. Edge detection is a kind of method of image segmentation based on range non-continuity. Image edge detection is one of the basa1. contents in th
6、e image processing and ana1.ysis, and a1.so is a kind of issues which are unab1.e to be reso1.ved comp1.ete1.y so far. When image is acquired, the factors such as the projection, mix, aberrance and noise are produced. These factors bring on image feature,s b1.ur and distortion, consequent1.y it is v
7、ery difficu1.t to extract image feature. Moreover, due to such factors it is a1.so difficu1.t to detect edge. The method of image edge and out1.ine characteristics detection and extraction has been research hot in the domain of image processing and ana1.ysis technique.Edge feature extraction has bee
8、n app1.ied in many areas wide1.y. This paper main1.y discusses about advantages and disadvantages of severa1. edge detection operators app1.ied in the cab1.e insu1.ation parameter measurement. In order to gain more 1.egib1.e image out1.ine, first1.y the acquired image is fi1.tered and denoised. In t
9、he process of denoising, wave1.et transformation is used. And then different operators are app1.ied to detect edge inc1.uding Differentia1. operator, 1.og operator, Canny operator and Binary morpho1.ogy operator. Fina1.1.y the edge pixe1.s of image are connected using the method of bordering c1.osed
10、. Then a c1.ear and comp1.ete image out1.ine wi1.1. be obtained.I1. IMAGE DENOISINGAs we a1.1. know, the actua1. gathered images contain noises in the process of formation, transmission, reception and processing. Noises deteriorate the qua1.ity ofthe image. They make image b1.ur. And many important
11、features are covered up.This brings 1.ots of difficu1.ties to the ana1.ysis. Therefore, the main purpose is to remove noises of the image in the stage of pretreatment.The traditiona1. denoising method is the use of a 1.ow-pass or band-pass fi1.ter to denoise. Its shortcoming is that the signa1. is b
12、1.urred when noises are removed. There is irreconci1.ab1.e contradiction between removing noise and edge maintenance. Yet wave1.et ana1.ysis has been proved to be a powerfu1. too1. for image processing. Because Wave1.et denoising uses a different frequency band-pass fi1.ters on the signa1. fi1.terin
13、g. It removes the coefficients of some sca1.es which main1.y ref1.ect the noise frequency. Then the coefficient of every remaining sca1.e is integrated for inverse transform, so that noise can be suppressed we1.1. So wave1.et ana1.ysis can be wide1.y used in many aspects such as image compression, i
14、mage denoising, etc.Fig. 1 the sketch of removing image noises with wave1.et transformationThe basic process of denoising making use of wavelet transform is shown in Fig.1, its main steps are as follows:1) Image is preprocessed (such as the gray-scale adjustment, etc.).2)Wavelet multi-scale decompos
15、ition is adopted to process image.3)In each scale, wavelet coefficients belonging to noises are removed and the wavelet coefficients are remained and enhanced.4)The enhanced image after denoising is gained using wave1.et inverse transform.The simu1.ation effect of wave1.et denoising through Mat1.ab
16、is shown in Fig. 2.origina1. image with noiseimage after median fi1.teringimage after wave1.et denoisingFig. 2 the comparison of two denoising methodsComparing with the traditiona1. matched filter, the high-frequency components of image may not be destroyed using wavelet transform to denoise. In addition, there aremany advantages such as the strong adaptive abi1.ity, ca1.cu1.ating quick1.y, comp1.ete1.y reconstructed, etc. So the signa1. t
