Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Determination of the performance of a total computer vision algorithm is possible if the performance of each of the sub-algorithm constituents is given. The problem, however, is that for most published algorithms, there is no performance characterization which has been established in the research literature. This is an awful state of affairs for the engineers whose job it is to design and build image analysis or machine vision systems.
This suggests that there has been a cultural deficiency in the computer vision community: computer vision algorithms have been published more on the merit of an experimental or theoretical demonstration suggesting that some task can be done, rather than on an engineering basis. Such a situation was tolerated because the interesting question was whether it was possible at all to accomplish a computer vision task. Performance was a secondary issue.
Now, however, a major interesting question is how to quickly design machine vision systems which work efficiently and which meet requirements. To do this requires an engineering basis which describes precisely what is the task to be done, how this task can be done, what is the error criterion, and what is the performance of the algorithm under various kinds of random degradations of the input data.
In this paper, we discuss the meaning of performance characterization in general, and then discuss the details of an experimental protocol under which an algorithm performance can be characterized.
KeywordsMachine Vision Random Perturbation Error Criterion Data Unit Vision Algorithm
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