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The type of representation chosen depends on the target type, the available hardware, and the degree of accuracy required (12). Object-centered models require transforms from model space to feature space. Usually this begins with computing the viewing transform and then calculating the features based on the two-dimensional projection of the model. The computations involve expressing the object not as it is stored but as it appears on the screen. This may require additional interpretation of the model because the projection may cause some parts of the object to appear as holes or unconnected to the main body of the object.

This skeleton is the core most representative of the object. The skeleton can then be used in pattern matching with a much smaller number of pixels required to describe the object. Thinning operations are often used as the first step in character recognition algorithms because different type styles generally alter the weight of the characters, not the general appearance. If the analysis is done on a skeleton of the character instead of the original image, there is a better chance of recognizing a wider array of type styles.

The type of representation is generally not important as long as the expressions are consistent, and the type of representation is taken into account in the pattern-matching algorithm. Feature selection is an important step in object recognition. There are several constraints and requirements for good features: features must be simple to extract given the hardware and target objects; features must describe the object reliably; and features must differentiate the object from other possible objects and the background of the image.

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