Abstract
Perceptual abilities enable us to go beyond the data that are in the image, since we can achieve reliable identification from a small subset of the predicted correspondences and then use our knowledge to infer many properties of the scene that may not be directly supported by visual data or solve other complex perceptual problems. Without constraining influence of prior expectations, many perceptual problems would be under constrained to the extent that they could never be solved. This emphasis on world knowledge parallels developments in most other areas of machine perception, in which large amounts of problem-specific knowledge are increasingly being used both to constrain solutions and to speed the process of obtaining the solution. In machine perception MU, the knowledge that is needed during solving perceptual problems is supplied by the contextual information that is coded in the categorical structure of the shape classes, the 3D object classes, the picture classes, and the perceptual and ontological categories of visual objects.
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Les, Z., Les, M. (2020). Machine Perception MU—Picture Classes. In: Machine Understanding. Studies in Computational Intelligence, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-030-24070-7_5
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DOI: https://doi.org/10.1007/978-3-030-24070-7_5
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