MachineVision_Chapter4

# MachineVision_Chapter4 - Chapter 4 Image Filtering When an...

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Chapter 4 Image Filtering When an image is acquired by a camera or other imaging system, often the vision system for which it is intended is unable to use it directly. The image may be corrupted by random variations in intensity, variations in illumination, or poor contrast that must be dealt with in the early stages of vision processing. This chapter discusses methods for image enhancement aimed at elimi- nating these undesirable characteristics. The chapter begins with histogram modification, followed by a brief review of discrete linear systems and fre- quency analysis, and then coverage of various filtering techniques. The Gaus- sian smoothing filter is covered in depth. 4.1 Hi~togram Modification Many images contain unevenly distributed gray values. It is common to find images in which all intensity values lie within a small range, such as the image with poor contrast shown in Figure 4.1. Histogram equalization is a method for stretching the contrast of such images by uniformly redistributing the gray values. This step may make threshold selection approaches more effective. In general, histogram modification enhances the subjective quality of an image and is useful when the image is intended for viewing by a human observer. A simple example of histogram modification is image scaling: the pixels in the range [a,b] are expanded to fill the range [Zb Zk]. The formula for 112

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----- 4.1. HISTOGRAM MODIFICATION 113 Figure 4.1: An image with poor contrast. mapping a pixel value z in the original range into a pixel value z' in the new range IS z' (4.1) - The problem with this scheme is that when the histogram is stretched ac- cording to this formula, the resulting histogram has gaps between bins (see Figure 4.2). Better methods stretch the histogram while filling all bins in the output histogram continuously. If the desired gray value distribution is known a priori, the following method may be used. Suppose that Pi is the number of pixels at level Zi in the original histogram and qi is the number of pixels at level Zi in the desired histogram. Begin at the left end of the original histogram and find the value k1 such that kl -1 kl L Pi ::; q1 < LPi' i=l i=l (4.2) The pixels at levels Zl, Z2, . . . , Zkl-1 map to level Zl in the new image. Next,
114 CHAPrnR4. ~AGEF~TEmNG Figure 4.2: The original image has very poor contrast since the gray values are in a very small range. Histogram scaling improves the contrast but leaves gaps in the final histogram. Top: Original image and histogram. Bottom: Image and resulting histogram after histogram scaling. find the value k2 such that k2-1 k2 .L Pi :::; ql + q2 < .L Pi. i=l i=l (4.3) The next range of pixel values, Zk1, . . . , Zk2-1, maps to level Z2. This procedure is repeated until all gray values in the original histogram have been included. The results of this approach are shown in Figure 4.3.

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## This note was uploaded on 03/16/2011 for the course CSCI 8820 taught by Professor Suchi during the Spring '10 term at UGA.

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MachineVision_Chapter4 - Chapter 4 Image Filtering When an...

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