It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive par...
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It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive parallel hardware: either resistive networks which are attractive for their technological potential, or neuronal networks related to the ones occurring in the motion pathway in the primate's visual system. If the x and y components of the motion field are coded explicitly as voltages within electrical circuits, simple resistive networks solve for the optical flow in the presence of motion discontinuities. These networks are being implemented into analog, subthreshold CMOS VLSI (complementary metal oxide semiconductor very large-scale integration) circuits. If velocity is represented within a population of direction selective cells, the resulting neuronal network maps onto the primate's striate and extrastriate visual cortex (middle temporal area). The performance of the network mimicks a large number of psychological illusions as well as electrophysical findings.< >
In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron ...
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In this paper we present a new, adaptive spatial-derivative circuit for CMOS image sensors. The circuit removes its offset as a natural part of its operation using a combination of electron tunneling and hot-electron injection to add or remove charge on a floating-gate of an auto-zeroing amplifier. We designed, fabricated and successfully tested a chip with the circuit. Test results show that the circuit reduces the offsets by more than an order of magnitude.
Depth perception via monocular cues was studied with a reach and grasp task in a retinal prosthesis simulator at low resolution. Results indicate that depth perception may be possible with retinal prostheses implanted...
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The accuracy of optical flow estimation depends on the spatio-temporal discretization used in the computation. The authors propose an adaptive multiscale method, where the discretization scale is chosen locally accord...
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The accuracy of optical flow estimation depends on the spatio-temporal discretization used in the computation. The authors propose an adaptive multiscale method, where the discretization scale is chosen locally according to an estimate of the relative error in the velocity measurements. They show that their coarse-to-fine method provides substantially better results of optical flow than conventional algorithms. The authors map this multiscale strategy onto their model of motion computation in primate area MT. The model consists of two stages: (1) local velocities are measured across multiple spatio-temporal channels, while (2) the optical flow field is computed by a network of direction-selective neurons at multiple spatial resolutions. Their model neurons show the same nonclassical receptive field properties as Allman's type I MT neurons and lead to a novel interpretation of some aspect of the motion capture illusion.< >
We present new simulation results, in which a computational model of interacting visual neurons simultaneously predicts the modulation of spatial vision thresholds by focal visual attention, for five dual-task human p...
We present new simulation results, in which a computational model of interacting visual neurons simultaneously predicts the modulation of spatial vision thresholds by focal visual attention, for five dual-task human psychophysics experiments. This new study complements our previous findings that attention activates a winner-take-all competition among early visual neurons within one cortical hypercolumn. This "intensified competition" hypothesis assumed that attention equally affects all neurons, and yielded two single-unit predictions: an increase in gain and a sharpening of tuning with attention. While both effects have been separately observed in electrophysiology, no single-unit study has yet shown them simultaneously. Hence, we here explore whether our model could still predict our data if attention might only modulate neuronal gain, but do so non-uniformly across neurons and tasks. Specifically, we investigate whether modulating the gain of only the neurons that are loudest, best-tuned, or most informative about the stimulus, or of all neurons equally but in a task-dependent manner, may account for the data. We find that none of these hypotheses yields predictions as plausible as the intensified competition hypothesis, hence providing additional support for our original findings.
In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurement...
In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurements based on which the classification should be made. It is helpful to reduce the ambiguity by utilizing extra information referred to as context, which in our case is the identities of the accompanying objects. This technique is applied to white blood cell classification. Comparisons are made against "no context" approach, which demonstrates the superior classification performance achieved by using context. In our particular application, it significantly reduces false alarm rate and thus greatly reduces the cost due to expensive clinical tests.
The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip co...
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The complexity of analog VLSI systems is often limited by the number of pins on a chip rather than by the die area. Currently, many analog parameters and biases are stored off chip. Moving parameter storage on chip could save pins and allow us to create complex programmable analog systems. In this paper, we present a design for an on-chip non-volatile analog memory cell that can be configured in addressable arrays and programmed easily. We use floating-gate MOS transistors to store charge, and we use the processes of tunneling and hot-electron injection to program values. We achieve greater than 13-bit precision with no crosstalk between memory cells.
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