T. Peli and D. Malah, "A Study of Edge Detection Algorithms," Computer Graphics and Image Processing, vol. 20, pp. 1-21, 1982.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....[1] 2] 3] Most of the objective evaluation methods assume knowledge of specific features such as known object boundaries in simple synthetic images. In such cases, the edge detection can be quantitatively measured, based on the known ideal detection considered to be the ground truth (GT) [4], 5] 6] In real images, manual specification of the edges was applied to form a GT [7] 8] A method called local edge coherence measures local properties of continuation and thinness by examining local neighborhoods surrounding the detected edge points [9] and was later generalized to ....

T. Peli and D. Malah, "A Study of Edge Detection Algorithms," Computer Graphics and Image Processing, vol. 20, pp. 1-21, 1982.

....based on a goodness function describing the separation of background, skull and brain have been reported [Van88] Cla93] Jac93] Hal92] Lia93] Ger92b] The method is limited, and successful application for clinical use hindered by the variability of anatomy and MR data. Edge detection [Pel82] [Mar80] Bru93] schemes suffer from incorrect detection of edges due to noise, over and under segmentation, and variability in threshold selection in the edge image [Del91a] Sah88] Combination with morphological [Rit96] Rit87a] Rit87b] filtering is also reported [Bom90] Another method is ....

Peli T., Malah, D., "A study of edge detection algorithms," Computer Graphics and Image Processing, V20, p1-21, 1982.

....the point where they are incapable of producing an accurate segmentation. This is precisely the problem when these techniques are applied to ultrasound images. 3.1. 1 Mask Convolutions A very common technique used in edge detection involves the use of convolution of an image with a function (mask) [33, 23, 10, 9, 13, 41]. The purpose of this is to determine local measurements of the edge strength based on a pixel and its surrounding neighbors. A measure for each pixel in the image is obtained by centering the mask over top of the image at each pixel location. Since the mask is a small array of values, typically ....

....is a mask which is not affected by the orientation of a line. This is especially true when curved edges are being detected. The Sample Variance [9] and Laplacian of Gaussian [23] are operators which also detect edges without regard to orientation. Operators such as Roberts, Sobel, and Prewitt [33] respond better to edges or corners in a certain orientation but can not always detect all edges (or curves) with accuracy. Operators which compute direction derivatives of the intensity [10] also fall into this class The mask size is another issue to consider. The larger the mask size the more ....

T. Peli. A study of edge detection algorithms. Computer Graphics, and Image Processing, 20:1--21, 1982.

....matching; statistical techniques, which perform better for noisy images; and surface fitting where the intensity profile of the image is approximated by an analytical 13 function, which is then used to compute the derivatives. A number of these methods have been reviewed in a variety of papers [12, 13, 15, 17, 18, 19, 20, 21]. 2.3 Segmentation using similarity information There is an extensive range of image segmentation techniques based on similarity of gray values or textures. No single technique exists that is effective for all images and not all the techniques are good for one image. According to Pal and Pal [4] ....

Peli, T. and Malah, D. (1982) A Study on Edge Detection Algorithms, Computer Graphics and Image Processing, vol. 20, pp 1--21. 118

....design of edge detectors is summarized in Table 1. Our work deals not with the design of an edge detector, but rather the methodology for comparing edge detectors. One of the earliest comparisons was done by Abdou and Pratt [28] This was followed by work by Fram and Duetsh [29] Peli and Malah [30], and more recently, Ramesh and Haralick [31] The Nature performance real image algorithms Source of the algorithm presented on ground truth compared [3] PAMI, 1995) covariance models 3 real 0 none [4] PAMI, 1994) expansion matching 1 real 0 Canny [5] PAMI, 1993) dispersion of 1 real 0 Sobel ....

T. Peli and D. Malah, "A study of edge detection algorithms," Computer Graphics and Image Processing, vol. 20, pp. 1--21, 1982.

....and John Palmer is with the Department of Psychology, NI 25, Email: jpalmer u.washington.edu) IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. XX, NO. Y, MONTH 1995 2 area with previous work on quantitative performance evaluation is in edge detection and thresholding [1] 2] [3], 4] 5] 6] 7] Most of the papers present an analysis that is specific to edge detection. Furthermore, the performance is finally a number, e.g. percentage of edge points detected, etc. There is little further analysis of the sensitivity of performance to relevant factors such as the ....

T. Peli and D. Malah, "A study of edge detection algorithms", Computer Graphics and Image Processing, vol. 20, pp. 1--21, 1982.

....is a very popular mechanism that is used for both identifying features and also for computing 2 D contours of the objects under observation. Edge extraction strategies and methods to evaluate their performance qualitatively and quantitatively have been presented and discussed in the literature [1,6,7,9,11,12,13]. There are many types of edges, ideal, ramp and noisy edges are only three of them. Different curvatures in the edges also constitute another dimension to be taken into consideration when it comes to asserting the types of edges that exist. The goal of developing the error models for edge ....

T. Peli and D. Malah, "A Study of Edge Detection Algorithms", Computer Graphics and Image Processing, vol. 20, 1982, pp. 1-21.

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T. Peli and D. Malah, "A Study of Edge Detection Algorithms," Computer Graphics and Image Processing, vol. 20, pp. 1-21, 1982.

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T. Peli and D. Malah, "A study of edge detection algorithms", Comp. Graphics and Img. Processing., vol. 20, pp. 1-21, 1982.

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T. Peli and D. Malah, "A Study of Edge Detection Algorithms", Computer Graphics and Image Processing, vol. 20, 1982, pp. 1-21.

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T. Peli and D. Malah, A study of edge detection algorithms, Comput. Graphics Image Process ., vol. 20, pp. 1-21, 1982.

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