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

By John G. Webster (Editor)

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For example, satellite images are scanned automatically for enemy installations, and medical images can be analyzed for evidence of tumors. In each of these tasks, the sole goal of the system is to identify an object in the image. Even tasks such as robot-guided vision require at least some image recognition to separate the area of interest out from the rest of the image. Object recognition can be divided into several steps as shown in Fig. 11. There are many different approaches for each of these steps, sometimes based on extremely different paradigms (3).

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). The size of ␴ determines the degree of smoothing by changing the shape of the Gaussian generated. A larger ␴ results in a flatter Gaussian with more even weightings.

In general, histogram equalization attempts to fit a constant distribution to the gray-scale image, which means that the total occupancy of the gray-scale values increases linearly. Fitting this form of distribution to the histogram can be expressed by using the following equation: in = IMAGE PROCESSING Image processing is generally the first step of a computer vision algorithm. Image processing performs preprocessing on the image to highlight specific features, eliminate noise, and gain basic pixel-level information about the image.

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

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