16.Control Systems Technology by John G. Webster (Editor)

By John G. Webster (Editor)

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The more noise eliminated, the less distinct the edges will become. See Fig. 6 for examples of edge detection (3). Gradient-Based Edge Detection. The most common method for detecting edges in an intensity map is to use a gradient enhancer. The gradient enhancer is a special filtering process that highlights edges by returning the magnitude of the gradient of the image. Edges tend to be characterized by rapid changes in intensity and have high local gradients. Continu- ous surfaces generally have uniform intensity levels and small local gradients.

Most of the recent work belongs to the third class. Among these, Pavlidis and Liow (8) describe a method to combine segments obtained by using a region-growing approach where edges between the regions are eliminated or modified based on contrast, gradient, and shape of the boundary. Haddon and Boyce (9) generate regions by partitioning the image co-occurrence matrix, and then refining them by relaxation using the edge information. Hedley and Yan (10) have suggested a histogram analysis to segment pixels with low spatial gradients, while edge pixels are later assigned to the nearest class computed from nonedge pixels.

2. Gaussian weighting decreases with distance from the current pixel. 3. The Fourier transform of a Gaussian function is a Gaussian function. 4. The degree of smoothing can be altered with by changing a single parameter. 5. Gaussians are separable and can be decomposed into horizontal and vertical components (6). A discrete two-dimensional Gaussian filter is given by the following equation: g[i, j] = e−(i 2 + j 2 )/2σ 2 (7) where i and j are the coordinates of the current pixel and ␴ is the width of the function (5).

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