外文翻译--图像分割

外文翻译--图像分割内容摘要：

ough the image in the manner described in Section . For the 3 x 3 mask shown in Fig. , this procedure involves puting the sum of products of the coefficients with the gray levels contained in the region enpassed by the mask. That is. with reference to Eq. (). the response of the mask at anv point in the image is given by 3 9199.. .2211iw iz izwzwzwR（ ） 1W 2W 3W 4W 5W 6W 7W 8W 9W FIGURE A general 3 x 3 mask. where z。 is the gray level of the pixel associated with mask coefficient Wi. As usual, the response of the mask is defined with respect to its center location. The details for implementing mask operations are discussed in Section . Point Detection The detection of isolated points in an image. is straightforward in principle. Using the mask shown in Fig. (a), we say that a point has been detected at the location on which the mask is centered if |R| ≥ T () where T is a nonnegative threshold and R is given by Eq. (). Basically,this formulation measures the weighted differences between the center point and its neighbors. The idea is that an isolated point (a point whose gray level is significantly different from its background and which is located in a homogeneous or nearly homogeneous area) will be quite different from its surroundings, and thus be easily detectable by this type of mask. Note that the mask in Fig. (a) is the same as the mask shown in Fig. (d) in connection with Laplacian operations. However, the emphasis here is strictly on the detection of points. That is, the only differences that are considered of interest are those large enough (as determined by T, to be considered isolated points. Note that the mask coefficients sum to zero, indicating that the mask response will be zero in areas of constant gray level. 4 1 1 1 1 8 1 1 1 1 （ a） （ b） （ c） （ d） FIGURE (a) Pointdetection mask. (b) Xray image of a turbine blade with a porosity. (c) Result of point detection. (d) Result of using Eq. ().(Original image courtesy of XTEK Systems Ltd.) EXAMPLE :Detection of isolated points in an image. We illustrate segmentation of isolated points from an image with the aid of Fig. (6), which shows an Xray image of a jetengine turbine blade with a porosity in the upper, right quadrant of the image. There is a single black pixel embedded within the porosity. Figure (c) is the result of applying the point detector mask to the Xray image, and Fig. (d) shows the result of using Eq. () with T equal to 90% of the highest absolute pixel value of the image in Fig. (c). (Threshold selection is discussed in detail in Section ) The single pixel is clearly visible in this image (the pixel was enlarged manually so that it would be visible after printing). This type of detection process is rather specialized because it is based on singlepixel discontinuities that have a homogeneous background in the area of the detector mask. When this condition is not satisfied, other methods discussed in this chapter are more suitable for detecting graylevel discontinuities. 5 Line Detection The next level of plexity is line detection. Consider the masks shown in Fig. . If the first mask were moved around an image, it would respond more strongly to lines (one pixel thick) oriented horizontally. With a constant background, the maximum response would result when the line passed through the middle row of the mask. This is easily verified by sketching a simple array of 139。 s with a line of a different gray level (say, 539。 s) running horizontally through the array. A similar experiment would reveal that the second mask in Fig. responds best to lines oriented at +450。 the third mask to vertical lines。 and the fourth mask to lines in the 450 direction . These directions can be established also by noting that the preferred direction of each mask is weighted with a larger coefficient (., 2) than other possible directions. Note that the coefficients in each mask sum to zero, indicating a zero response from the masks in areas of constant gray level. Horizontal +45176。 Vertical 45176。 FIGURE Line masks. Let R1, R2, R3, and R4 denote the responses of the masks in Fig. , from left to right, where the R39。 s are given by Eq. (). Suppose that the four masks are run individually through an image. If, at a certain point in the image, |Ri| |Rj|, for all j ≠ i, that point is said to be more likely associated with a line in the direction of mask i. For example, if at a point in the image, |Ri||Rj|, for j = 2, 3. 4, that particular point is said to be more likely associated with a horizontal line. Alternatively, we may be interested in detecting lines in a specified direction. In this case, we would use the mask associated with that direction and threshold its output, as in Eq . (). In other words, if we are interested in detecting all the lines in an image in the direction defined by a given。