Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision
J. Gerard Wolff

TL;DR
The paper explores how the SP theory of intelligence can be applied to natural and computer vision, emphasizing information compression, recognition, learning, and integration with other sensory modalities.
Contribution
It demonstrates the potential of the SP theory to unify concepts in vision, enabling unsupervised learning, recognition robustness, and integration with broader cognitive functions.
Findings
Edges and corners identified via redundancy extraction
Object recognition using multiple alignment and hierarchy
Robust recognition and learning despite errors
Abstract
The SP theory of intelligence aims to simplify and integrate concepts in computing and cognition, with information compression as a unifying theme. This article discusses how it may be applied to the understanding of natural vision and the development of computer vision. The theory, which is described quite fully elsewhere, is described here in outline but with enough detail to ensure that the rest of the article makes sense. Low level perceptual features such as edges or corners may be identified by the extraction of redundancy in uniform areas in a manner that is comparable with the run-length encoding technique for information compression. The concept of multiple alignment in the SP theory may be applied to the recognition of objects, and to scene analysis, with a hierarchy of parts and sub-parts, and at multiple levels of abstraction. The theory has potential for the…
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