Download Analog VLSI Circuits for the Perception of Visual Motion by Alan A. Stocker PDF
By Alan A. Stocker
Even though it is now attainable to combine many thousands of transistors on a unmarried chip, conventional electronic circuit expertise is now attaining its limits, dealing with difficulties of expense and technical potency while scaled all the way down to ever-smaller characteristic sizes. The research of organic neural structures, specially for visible processing, has allowed engineers to raised know the way complicated networks can successfully procedure quite a lot of details, when facing tough computational challenges.Analog and parallel processing are key features of organic neural networks. Analog VLSI circuits utilizing an analogous beneficial properties can as a result be constructed to emulate brain-style processing. utilizing ordinary CMOS know-how, they are often cost effectively synthetic, allowing effective commercial and client functions in robotics and cellular electronics.This booklet explores the idea, layout and implementation of analog VLSI circuits, encouraged by way of visible movement processing in organic neural networks. utilizing a unique process pioneered by means of the writer himself, Stocker explains intimately the development of a sequence of digital chips, offering the reader with a priceless sensible perception into the technology.Analog VLSI Circuits for the belief of visible movement: * analyses the computational difficulties in visible movement conception; * examines the problem of optimization in analog networks via excessive point strategies reminiscent of movement segmentation and selective consciousness; * demonstrates community implementation in analog VLSI CMOS know-how to supply computationally effective units; * units out measurements of ultimate implementation; * illustrates thesimilarities of the provided circuits with the human visible movement notion approach; * contains an accompanying web site with videos of circuits lower than real-time visible stipulations and extra supplementary material.With an entire evaluation of all present neuromorphic analog VLSI platforms for visible movement sensing, Analog VLSI Circuits for the belief of visible movement is a distinct reference for complicated scholars in electric engineering, man made intelligence, robotics and computational neuroscience. it is going to even be helpful for researchers, pros, and electronics engineers operating within the box.
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Additional resources for Analog VLSI Circuits for the Perception of Visual Motion
G. the optical ﬂow vector v). 22 VISUAL MOTION PERCEPTION Motion discontinuities A further hint that motion discontinuities play an important role in visual motion perception is found in biological systems. A general property of biological nervous systems is their ability to discard redundant information as early as possible in the processing stream, and so reduce the huge input information ﬂow. For example, antagonistic center-surroundtype receptive ﬁelds transmit preferentially spatio-temporal discontinuities in visual feature space while regions of uniform visual features are hardly encoded.
Hopﬁeld himself (in collaboration with David Tank) demonstrated the potential of the approach by describing a network solution for the well-known traveling salesman problem (TSP) [Hopﬁeld and Tank 1985]. The TSP is a computationally hard optimization problem. It considers a salesman who must choose the shortest overall path to visit a number of cities only once each. This is a typical combinatorial problem in which the number of possible solutions grows exponentially with the number of cities. Their proposed network could ﬁnd (near-)optimal solutions within only a few characteristic time constants.
1987], and partially to the tracking depending on the level of feature extraction; for example, tracking edges might be ambiguous if there is occlusion. There are two other reasons against having a complex spatial feature extraction stage preceding image motion estimation. First, fault tolerance is decreased, because once a complex feature is misclassiﬁed the motion information of the entire feature is wrong. In contrast, outliers on low-level features can be discarded, usually by some conﬁdence measure, or simply averaged out.