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By John G. Webster (Editor)

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The simplest morphological operations are erosion and dilation, which are, respectively, defined as the intersection and union of the current pixel with the stelt. If the union between the current pixel and a pixel in the stelt evaluates to true, then the dilation is true, and the current pixel is set to one. If the intersection between the current pixel and every pixel defined by the stelt is true, then the erosion evaluates to true. Erosion and dilation are expressed mathematically (2): Dilation A⊕B = Ab ∀b i ∈B Erosion A B= i Abi (12) (13) ∀b i ∈B Dilation as an operation has a tendency to expand (dilate) the foreground of the image, filling in holes and accentuating noise.

Pattern-matching COMPUTER VISION algorithms are the mathematical interface between the numerical feature data and the world representation. Patternmatching algorithms can be based on many different mathematical disciplines, fixed or adaptive, human-derived or numerically derived. Although it is outside the scope of this work to present a full discourse on pattern-matching and recognition algorithms, the general properties of several algorithms will be presented in the context of object recognition for computer vision.

The image shown in Fig. 3, a balanced histogram can be created automatically. This process is known as histogram equalization. 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.

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