Vergence responses were recorded from practised observers viewing narrow-band spatial-frequency-filtered planar random-dot stereograms. It was found that low spatial frequencies of 1.75-3.5 cycles deg-1 could trigger appropriate vergence responses to larger disparities than could the relatively high spatial frequency of 7.0 cycles deg-1. Nevertheless, appropriate vergence shifts were observed reliably for spatial-frequency/disparity combinations well outside the range predicted by Marr and Poggio's (1979) model of stereo vision. It was also found that for large-disparity/high-spatial-frequency combinations which the subjects could not fuse, the vergence system went into oscillation with the eyes diverging and converging at a frequency of about 1.5 Hz and with an amplitude of about 10-20 min arc. Finally, it was demonstrated that when a prominent monocular cue was superimposed upon a large-disparity/high-spatial-frequency stereogram then a speedy vergence response occurred which resulted in successful fusion. This latter finding supports the hypothesis advanced earlier that monocular cues can facilitate stereopsis by triggering appropriate vergence shifts.
SUMMARYThis paper reviews some current research problems in Artificial Intelligence applied to robotics, in particular the processing of sensory information and robot programming. It is perceived that much progress has been made in applying AI techniques to particular isolated tasks, but the important theme at the leading edge of the AI-based robotics technology seems to be the interfacing of the subsystems into an integrated environment. This requires flexibility of the subsystems themselves. But on the other hand it also increases the flexibility of the integrated system in that it broadens the variety of ways for solving problems through functional combination of the subsystems. The presentation in this paper is illustrated by examples from the hardware and software environment of the advanced robotics research at the Turing Institute.
This paper describes an approach to establish the correspondence between a magnetic resonance (MR) image of the brain and a slice through a 3D anatomical model. The model is of voxel structure that symbolically labels primary tissue types such as grey matter, white matter, CSF, etc. In this approach a slice is first searched for in the model to achieve the best general match with the brain MR image in question. The operation involves a minimization of parameters such as position, rotation, slant, tilt and enlargement. Having thus found a globally good registration between the image and the model, local matches that link every pixel in the image through to the model slice are then searched for. This pixel-by-pixel match is expressed within a pair of maps, one for the vertical deformation and the other for the horizontal one. The matching algorithm consists of a series of octave separated blurring convolutions combined with exhaustive grey-valued correlation. Because every pixel in the model slice is labelled in terms of its tissue type, and because every pixel in the image has been matched directly to the model, every pixel in the image is now classified. This classification is used directly to perform segmentation which serves as a basis for the computation of medically relevant indices.
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