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Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual System (Artech House Series Bioinformatics & Biomedical Imaging)

Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual System (Artech House Series Bioinformatics & Biomedical Imaging)

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Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual System (Artech House Series Bioinformatics & Biomedical Imaging)

Next Generation Artificial Vision Systems: Reverse Engineering the Human Visual System (Artech House Series Bioinformatics & Biomedical Imaging) Summary:

 
By Maria Petrou, Anil Bharath
  • Publisher:   Artech House Publishers
  • Number Of Pages:   438
  • Publication Date:   2008-06-30
  • ISBN-10 / ASIN:   1596932244
  • ISBN-13 / EAN:   9781596932241
Product Description:

This milestone interdisciplinary work brings you to the cutting edge of emerging technologies inspired by human sight, ranging from semiconductor photoreceptors based on novel organic polymers and retinomorphic processing circuitry to low-powered devices that replicate spatial and temporal processing in the brain. Moreover, it is the first work of its kind that integrates the full range of physiological, engineering, and mathematical issues and advances together in a single source.

Emphasizing both the devices and the software simulation point of view, this definitive book provides state-of-the-art retinal cell and primary visual cortex (V1) models that reflect our rapidly advancing understanding of human visual signal communication networks. It explores design and fabrication considerations behind real-world implementations, including organic light sensors that mimic human rods and cones, analog circuitry to perform retinal processing, algorithm design for motion detection and tracking, wavelet-based visual detection systems, and interest point detectors. You get the latest techniques for resolution and motion detection enhancement, including both the design and applications of biologically motivated spatio-temporal filtering of visual data, as well as a statistical framework for studying object detection in a phase-invariant manner and tools for describing local object invariants. Moreover, this trail-blazing work includes insight into the challenges that lie ahead in this cutting-edge field.

 
 
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