This chapter considers some of the implementation issues that are encountered in processing grayscale images. These issues fall into two main areas: grayscale training issues and grayscale hardware implementation.
It was seen in Chapter 4 that training of filters to deal with real-world problems is a difficult task. A balance must be struck between the dimensionality of the training set and the size of the search space. The training task may be simplified by limiting the complexity of the problem through the application of a constraint. This leads to an increase in error due to the addition of a constraint error term. However, this may be more than offset by the reduction in estimation error. Estimation error resulting from inadequate training of filters can be very severe and can even result in filters that actually increase the error.
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