Local disparity information is often sparse and noisy, which creates two conflicting demands when estimating disparity in an image region: the need to spatially average to get an accurate estimate, and the problem of not averaging over discontinuities. We have developed a network model of disparity estimation based on disparityselective neurons, such as those found in the early stages of processing in visual cortex. The model can accurately estimate multiple disparities in a region, which may be caused by transparency or occlusion, in real images and random-dot stereograms. The use of a selection mechanism to selectively integrate reliable local disparity estimates results in superior performance compared to standard back-propagation and cross-correlation approaches. In addition, the representations learned with this selection mechanism are consistent with recent neurophysiological results of von der Heydt, Zhou, Friedman, and Poggio [8] for cells in cortical visual area V2. Combining multi-scale biologically-plausible image processing with the power of the mixture-of-experts learning algorithm represents a promising approach that yields both high performance and new insights into visual system function.

Local disparity information is often sparse and noisy, which creates two conflicting demands when estimating disparity in an image region: theneed to spatially average to get an accurate estimate, and the problem of not averaging over discontinuities. We have developed anetwork model of disparity estimation based on disparityselective neurons,such as those found in the early stages of processing in visual cortex. The model can accurately estimate multiple disparities in a region, which may be caused by transparency or occlusion, inreal images and random-dot stereograms. The use of a selection mechanism to selectively integrate reliable local disparity estimates results in superior performance compared to standard back-propagation and cross-correlation approaches. In addition, the representations learned with this selection mechanism are consistent withrecent neurophysiological results of von der Heydt, Zhou, Friedman, and Poggio [8] for cells in cortical visual area V2. Combining multi-scale biologically-plausible image processing with the power of the mixture-of-experts learning algorithm represents a promising approach that yields both high performance and new insights into visual system function.

We describe a system that can track a hand in a sequence of video frames and recognize hand gestures in a user-independent manner. The system locates the hand in each video frame and determines if the hand is open or closed. The tracking system is able to track the hand to within 10 pixels of its correct location in 99.7% of the frames from a test set containing video sequences from 18 different individualscaptured in 18 different room environments. The gesture recognition network correctly determines if the hand being tracked is open or closed in 99.1 % of the frames in this test set. The system has been designed to operate in real time with existing hardware.

We describe a system that can track a hand in a sequence of video frames and recognize hand gestures in a user-independent manner. The system locates the hand in each video frame and determines if the hand is open or closed. The tracking system is able to track the hand to within 10 pixels of its correct location in 99.7% of the frames from a test set containing video sequences from 18 different individuals captured in 18 different room environments. The gesture recognition network correctly determines if the hand being tracked is open or closed in 99.1 % of the frames in this test set. The system has been designed to operate in real time with existing hardware.

To control such complex systems it is computationally moreefficient to decompose the problem into smaller subtasks, with different control strategies for different operating points. When detailed information about the plant is available, gain scheduling has proven a successful method for designing a global control (Shamma and Athans, 1992). The system is partitioned by choosing several operating points and a linear model for each operating point. A controller is designed for each linear model and a method for interpolating or'scheduling' the gains of the controllers is chosen. The control problem becomes even more challenging when the system to be controlled isnon-stationary, and the mode of the system is not explicitly observable.

To control such complex systems it is computationally more efficient to decompose the problem into smaller subtasks, with different control strategies for different operating points. When detailed information about the plant is available, gain scheduling has proven a successful method for designing a global control (Shamma and Athans, 1992). The system is partitioned by choosing several operating points and a linear model for each operating point. A controller is designed for each linear model and a method for interpolating or'scheduling' the gains of the controllers is chosen. The control problem becomes even more challenging when the system to be controlled is non-stationary, and the mode of the system is not explicitly observable.

We present a model of how MT cells aggregate responses from VI to form such a velocity representation. Two different sets of units, with local receptive fields, receive inputs from motion energy filters. One set of units forms estimates of local motion, while the second set computes the utility of these estimates. Outputs from this second set of units "gate" the outputs from the first set through a gain control mechanism. This active process for selecting only a subset of local motion responses to integrate into more global responses distinguishes our model from previous models of velocity estimation.

We present a model of how MT cells aggregate responses from VI to form such a velocity representation. Two different sets of units, with local receptive fields, receive inputs from motion energy filters. One set of units forms estimates of local motion, while the second set computes the utility of these estimates. Outputs from this second set of units "gate" the outputs from the first set through a gain control mechanism. This active process for selecting only a subset of local motion responses to integrate into more global responses distinguishes our model from previous models of velocity estimation.

One way of simplifying neural networks so they generalize better is to add an extra t.erm

One way of simplifying neural networks so they generalize better is to add an extra t.erm