Comparison Of Three One-dimensional Edge Detection Architectures For Analog VLSI Vision Systems
Abstract
A comparison is made between three architectural models used for edge detection in analog VLSI early vision systems. In analog VLSI computational networks, signal strength is a paramount issue due to the need to overcome circuit limitations such as offsets, noise, and finite gain. Therefore algorithms mapped into silicon networks must take full advantage of available signal strengths to maximize signal-to-noise ratios. It will be shown that a discrete Differenced Gaussian algorithm retains a greater amount of the available signal than algorithms using thresholded zero-crossings from the Difference of Gaussian (DoG) or the Laplacian of Gaussian (LoG) functions.
Recommended Citation
M. D. Rowley and J. G. Harris, "Comparison Of Three One-dimensional Edge Detection Architectures For Analog VLSI Vision Systems," Proceedings IEEE International Symposium on Circuits and Systems, vol. 3, pp. 1840 - 1843, Institute of Electrical and Electronics Engineers, Jan 1997.
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
0271-4310
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 1997
