16 June 1995 Image processing and computer vision algorithm selection and refinement using an operator-assisted meta-algorithm
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Most image processing and feature extraction algorithms consist of a composite sequence of operations to achieve a specific task. Overall algorithm capability depends upon the individual performance of each of these operations. This performance, in turn, is usually controlled by a set of a priori known (or estimated) algorithm parameters. The overall design of an image processing algorithm involves both the selections of the sub-algorithm sequence and the required operating parameters, and is done using the best available knowledge of the problem and the experience of the algorithm designer. This paper presents a dynamic and adaptive image processing algorithm development structure. The implementation of the dynamic algorithm structure requires solving of a classification problem at decision nodes in an algorithm graph, A. The number of required classifiers equals the number of decision nodes. There are several learning techniques that could be used to implement any of these classifiers. Each of these techniques, in turn, requires a training set. This training set could be generated using a modified form of the dynamic algorithm. In this modified form, a human operator interface replaces all of the decision nodes. An optimization procedure (Nelder-Mead) is employed to assist the operator in finding the best parameter values. Examples of the approach using real-world imagery are shown.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Khaled M. Shaaban, Khaled M. Shaaban, Robert J. Schalkoff, Robert J. Schalkoff, } "Image processing and computer vision algorithm selection and refinement using an operator-assisted meta-algorithm", Proc. SPIE 2488, Visual Information Processing IV, (16 June 1995); doi: 10.1117/12.212014; https://doi.org/10.1117/12.212014


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