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Intelligent Robot Vision Sensors in VLSI

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Abstract

Traditional approaches for solving real-world problems using computer vision have depended heavily on CCD cameras and workstations. As the computation power of workstations doubles every 1.5 years, they are now better able to handle the large amount of data presented by the cameras; yet real-time solutions for physical interaction with the real-world continues to be very hard, and relegated to large and expensive systems. Our approach attempts to solve this problem by using computational sensors and small/inexpensive embedded processors. The computational sensors are custom designed to reduce the amount of data collected, to extract only relevant information and to present this information to the simple processor, microcontrollers (μCs) or DSPs, in a format which reduces post-processing latency. Consequently, the post-processors are required to perform only high level computation on features rather than data. These systems are applied to problems such as target acquisition and tracking for image stabilization and autonomous data driven autonavigation for mobile robots. We present an example of a system that uses a pair of computational sensors and a μC to solve a toy autonavigation problem.

The computational sensors, however, have wide applications in many problems that require image preprocessing such as edge detection, motion detection, centroid localization and other spatiotemporal processing. This paper also presents a general-purpose computational sensor capable of extracting many visual information components at the focal plane.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21218

    Ralph Etienne-Cummings

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  1. Ralph Etienne-Cummings
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Etienne-Cummings, R. Intelligent Robot Vision Sensors in VLSI. Autonomous Robots 7, 225–237 (1999). https://doi.org/10.1023/A:1008968319725

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