AN AUTONOMOUS INTELLIGENT ROBOT VISION COMPUTATIONAL SENSORS IN VLSI
M. Chaitanya Suman
Assistant Professor, Department of ECE. Universal College of Engg.Tech, Guntur District, Andhra Pradesh, India
K. Prathyusha
PG scholar, Department of ECM. K.L.University, Vaddeswaram, Guntur District, Andhra Pradesh, India.
12-22
Vol: 4, Issue: 2, 2014
Receiving Date:
2014-02-12
Acceptance Date:
2014-03-10
Publication Date:
2014-04-10
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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 realtime 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 DSP’s, in a format which minimizes 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 auto navigation for mobile robots. We present an
example a system that uses a pair of computational sensors and a µC to solve a auto navigation 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.
Keywords:
Traditional approaches; workstations; computation power; microcontrollers
References
- Andreou A., Boahen K., Pouliquen P., Pavasovic A., Jenkins R., and Strohbehn K., “CurrentMode Subthreshold MOS Circuit for Analog Neural Systems,” IEEE Transactions on Neural Networks, Vol. 2, No. 2, pp. 205-213, 1991.
- Barlow H., the Senses: Physiology of the Retina, Cambridge University Press, Cambridge, England, 1982. DeWeerth, S. P., “Analog VLSI Circuits for Stimulus Localization and Centroid Computation,” Int’l J. Computer Vision, Vol. 8, No. 2, pp. 191- 202, 1992.
- Etienne-Cummings R., M. Abdel Ghani and V. Gruev, “VLSI Implementation of Centroid Localization for Autonomous Navigation,” excepted for publication in Advances in Neural Information Processing Systems 11, 1998.
- Etienne-Cummings R., J Van der Spiegel and P. Mueller, “Neuromorphic and Digital Hybrid Systems,” Neuromorphic Systems: Engineering Silicon from Neurobiology, L. Smith and A. Hamilton (Eds.), World Scientific, 1998.
- Etienne-Cummings, R. and Donghui Cai, “A General Purpose Image Processing Chip: Orientation Detection,” Advances in Neural Information Processing Systems 10, 1998b.
- Etienne-Cummings R., J. Van der Spiegel and P. Mueller, “VLSI Implementation of Cortical Motion Detection Using an Analog Neural Computer,” Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan and T. Petsche (Eds.), MIT Press, pp. 685-691, 1997.
- Etienne-Cummings R., J. Van der Spiegel and P. Mueller, Mao-zhu Zhang, “A Foveated Tracking Chip,” ISSCC97 Digest of Technical Papers, Vol. 40, pp. 38-39, 1997.
- Horiuchi T., T. Morris, C. Koch and S. P. DeWeerth, “Analog VLSI Circuits for AttentionBased Visual Tracking,” Advances in Neural Information Processing Systems, Vol. 9, Denver, CO, 1996.
- Horn B., Robot Vision, MIT Press, Cambridge MA, 1986. Koch C. and H. Li (Eds.), Vision Chips: Implementing Vision Algorithms with Analog VLSI Circuits, IEEE Computer Press, 1995.
- Kramer, J. and G. Indiveri, “Neuromorphic Vision Sensors and Preprocessors in Systems Application,” Proc. Adv. Focal Plane Arrays Electronics Cameras, May, 1998. Mansfield, P., “Machine Vision Tackles Star Tracking,” Laser Focus World, Vol. 30, No. 26, pp. S21-S24, 1996.
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