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A few feet away a computer screen displays the brightly colored, video-game-like models of objects he has scanned, which on a recent afternoon included a bronze from Thailand of the plump Buddhist disciple Phra Sankachai.
Early attempts involved building geometric models of objects [3].
The team was first able to create 2D models of objects using this technique but quickly graduated to 3D models.
Also, the capability to produce models of objects in free space would make some of these models more useful.
Then, based on the initial information, they construct color, position, or motion models of objects and the background.
The Gaussian mixture models of objects and background were then built under the Bayesian framework and the prior information on object shapes was introduced.
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They used mathematical models of object responses to indoor-climate variations.
This is contrary to the widespread assumption of translational invariance of inferotemporal cortex responses in computational models of object recognition.
In broader theoretical terms, the present results pose a challenge for image-based models of object recognition.
Despite these species differences, the overall findings are consistent with view-based models of object recognition [10], [15].
Recently, pictorial models of object representation which emphasise the differential contribution of image features at several hierarchically organised stages of classification have emerged [42].
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