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Artificial Intelligence Approaches to Image Understanding

  • Conference paper
Pattern Recognition Theory and Applications

Part of the book series: NATO Advanced Study Institutes Series ((ASIC,volume 81))

Abstract

The past decade has seen considerable progress in the development of computer vision within Artificial Intelligence. Attention has shifted from restrictions on the domain of application of a vision system to restrictions on the visual abilities studied. Mathematical analyses have been offered for some of the elements of visual perception, such as the relationship between image irradiance and scene radiance, the location of important intensity changes and motion primitives. In each case, it is observed that the information in the image only partially constrains the interpretation of the image, and further constraints are sought. The constraints embody commitments about the way the world is, at least most of the time. For example, the world mostly consists of smooth surfaces, and scenes are mostly viewed from general position, free of accidental alignments. Perceptual abilities such as stereopsis, lightness determination, shape from shading and from texture, require that the appropriate constraints be uncovered and appropriately expressed. Representations have been developed that make explicit the information computed by a perceptual ability. Examples include the Primal Sketch, the Reflectance Map, and object representations based on generalised cones. The isolation of representations has lead to a view of visual perception as the process of constructing instances of a sequence of representations. The input to a particular process is often not the image per se, but a representation of the information computed by a number of processes. It is this observation which most strongly distinguishes image understanding from conventional pattern recognition. A number of sample image understanding systems are described, including edge detection, shape from shading, binocular and photometric stereo, optical flow, directional selectivity, surface reconstruction through interpolation and the representation of objects by primitive volumes. In some cases, it has been possible to directly relate the theory embodied in the program to animate visual systems. In some cases it has been possible to develop important practical applications for example, industrial inspection and bin picking in robotics, and monitoring airfields or terrain for changes in usage. Finally, in some cases it has been possible to construct hardware realizations of theories to achieve real time performance.

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, MIT, USA

    Michael Brady

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  1. Michael Brady
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Editors and Affiliations

  1. Science and Engineering Research Council, Rutherford and Appleton Laboratories, Chilton, Didcot, England

    Josef Kittler

  2. Oxford, UK

    Josef Kittler

  3. Purdue University, West Lafayette, Indiana, USA

    King Sun Fu

  4. Ecole Nationale Superieure des Telecommunications, Paris, France

    Louis-François Pau

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© 1982 D. Reidel Publishing Company

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Brady, M. (1982). Artificial Intelligence Approaches to Image Understanding. In: Kittler, J., Fu, K.S., Pau, LF. (eds) Pattern Recognition Theory and Applications. NATO Advanced Study Institutes Series, vol 81. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-7772-3_15

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  • DOI: https://doi.org/10.1007/978-94-009-7772-3_15

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