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- W2020163055 abstract "This paper addresses the problem of generating models of 3D objects automatically from exploratory view-sequences of the objects. Neural network techniques are described which cluster the frames of video-sequences into view-categories, called aspects, representing the 2D characteristic views. Feedforward processes insure that each aspect is invariant to the apparent position, size, orientation, and foreshortening of an object in the scene. The aspects are processed in conjunction with their associated aspect-transitions by the Aspect Network to learn and refine the 3D object representations on-the-fly. Recognition is indicated by the object-hypothesis which has accumulated the maximum evidence. The object-hypothesis must be'consistent with the current view, as well as the recent history of view transitions stored in the Aspect Network. The “winning” object refines its representation until either the attention of the camera is redirected or another hypothesis accumulates greater evidence." @default.
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- W2020163055 date "1991-04-01" @default.
- W2020163055 modified "2023-09-23" @default.
- W2020163055 title "<title>Aspect networks: using multiple views to learn and recognize 3-D objects</title>" @default.
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- W2020163055 doi "https://doi.org/10.1117/12.25240" @default.
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