Complex preprocessing for pattern recognition
The construction of pattern recognition machines may eventually depend upon the development of highly complex preprocessors. This claim is supported by a discussion of the importance of perceptual grouping. Since complex preprocessing will assess more of the basic structure of a visual scene, internal representations will have to be more descriptive in nature. Two approches to descriptive internal representation are mentioned. Two of the author's programs are reviewed. One plays the Oriental game of GO at a human level and the other can recognize digitized hand printed characters. Both programs use a geometry preserving representation of features, so that calculations involving the features can assess the original geometry of the input. In addition, the GO program calculates groups of stones and performs other types of “complex”processing. Practical and philosophical arguments are given for the use of internal representation by pattern recognition programs.
In the overall ownership cost of enterprise system, the maintenance cost consists of a major percentage. During the lifetime of an enterprise system, process customization is the most frequent maintenance efforts. However, current processing method has limited scalability and efficiency. In this case study, we explained how a scalable and efficient customization processing method was implemented. This method used the carbon emission trading mechanism to facilitate the cost benefit analysis of customization request. It also used distributed processing principle to improve the overall processing efficiency. Feedback from a pilot implementation in a large manufacturer included.