邊緣檢測外文翻譯--一個(gè)索貝爾圖像邊緣檢測算法描述（英文）

1、Proceedings of Informing Science & IT Education Conference (InSITE) 2009 A Descriptive Algorithm for Sobel Image Edge Detection O. R. Vincent, Clausthal University of Technology, Germany and University of Agricult

2、ure, Abeokuta, Nigeria vincent.rebecca@gmail.com O. Folorunso Department of Computer Science, University of Agriculture, Abeokuta, Nigeria folorunsolusegun@yahoo.com Abstract Image edge detection is a process of lo

3、cating the edge of an image which is important in finding the approximate absolute gradient magnitude at each point I of an input grayscale image. The problem of getting an appropriate absolute gradient magnitude for e

4、dges lies in the method used. The Sobel operator performs a 2-D spatial gradient measurement on images. Transferring a 2-D pixel array into statistically uncorrelated data set enhances the removal of redundant data, as

5、 a result, reduction of the amount of data is required to represent a digital image. The Sobel edge detector uses a pair of 3 x 3 convolution masks, one estimating gradient in the x-direction and the other estimating

6、gradient in y–direction. The Sobel detector is incredibly sensitive to noise in pic- tures, it effectively highlight them as edges. Hence, Sobel operator is recommended in massive data communication found in data transf

7、er. Keywords: Image Processing, Edge Detection, Sobel Operator, Data Communication and Absolute Gradient Magnitude. Introduction Image processing is important in modern data storage and data transmission especially in

8、progressive transmission of images, video coding (teleconferencing), digital libraries, and image database, remote sensing. It has to do with manipulation of images done by algorithm to produce desired images (Milan et

9、 al., 2003). Digital Signal Processing (DSP) improve the quality of im- ages taken under extremely unfavourable conditions in several ways: brightness and contrast ad- justment, edge detection, noise reduction, focus ad

10、justment, motion blur reduction etc (Gonzalez, 2002). The advantage is that image processing allows much wider range of algorithms to be ap- plied to the input data in order to avoid problems such as the build-up of noi

11、se and signal distor- tion during processing (Baker & Nayar, 1996). Many of the techniques of digital image process- ing were developed in the 1960's at the Jet Propulsion Laboratory, Massachusetts Institute of

12、Technology (MIT), Bell laboratory and few other places. But the cost of proc- essing was fairly high with the comput- ing equipments of that era. With the fast computers and signal processors available in the 2000'

13、s, digi- tal image processing became the most common form of image processing and is general used because it is not only the most versatile method but also the Material published as part of this publication, either on

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17、ibution permission. Vincent & Folorunso 99 resulting edge detection. The two filters highlight areas of high special frequency, which tend to define the edge of an object in an image. The two filters are designed w

18、ith the intention of bring- ing out the diagonal edges within the image. The Gx image will enunciate diagonals that run from thee top-left to the bottom-right where as the Gy image will bring out edges that run top-ri

19、ght to bottom-left. The two individual imagesGx andGy are combined using the approxima-tion equation Gy Gx G + =The Canny edge detection operator was developed by John F. Canny in 1986 and uses a multi- stage algorith

20、m to detect a wide range of edges in images. In addition, canny edge detector is a complex optimal edge detector which takes significantly longer time in result computations. The image is firstly run through a Gaussian

21、 blur to get rid of the noise. When the algorithm is applied, the angle and magnitude is obtained which is used to determine portions of the edges to retain. There are two threshold cut-off points where any value in th

22、e image below the first threshold is dropped to zero and values above the second threshold is raised to one. Canny (1986) considered the mathematical problem of deriving an optimal smoothing filter given the criteria o

23、f detection, localization and minimizing multiple responses to a single edge. He showed that the optimal filter given these assumptions is a sum of four exponential terms. He also showed that this filter can be well ap

24、proximated by first-order derivatives of Gaussians. Canny also introduced the notion of non-maximum suppression, which means that given the pre- smoothing filters, edge points are defined as points where the gradient ma

25、gnitude assumes a local maximum in the gradient direction. Another algorithm used is the Susan edge detector. This edge detection algorithm follows the usual method of taking an image and using a predetermined window c

26、entered on each pixel in the image applying a locally acting set of rules to give an edge response (Vincent, 2006). The response is then processed to give the output as a set of edges. The Susan edge filter has been i

27、mplemented using circular masks (kernel) to give isotopic responses with approximations used either with constant weighting within it or with Gaussian weighting. The usual radius is 3.4 pixels, giving a mask of 37 pixe

28、ls, and the smallest mask considered is the traditional 3×3 mask. The 37 pixels circular mask used in all feature detection experiments is placed at each point in the image and for each point the brightness of eac

29、h pixel within the mask is compared with that of nucleus. The comparison equation is ( ) ? ? ? = 01 , O r r C r rifif ( ) ( )( ) ( ) t r rt r roo> Ι ? Ι≤ Ι ? Ι r rr r(1) where r r is the position of the nucleus in

30、 the dimensional image, o r r is the position of any other point within the mask, Ι( r r ) is the brightness of any pixel, t is the brightness in difference threshold and C is the output of the comparison. This compari