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Proceedings Paper

Camera Edge Response
Author(s): Stanley H. Zisk; Norman Wittels
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Paper Abstract

Edge location is an important machine vision task. Machine vision systems perform mathematical operations on rectangular arrays of numbers that are intended to faithfully represent the spatial distribution of scene luminance. The numbers are produced by periodic sampling and quantization of the camera's video output. This sequence can cause artifacts to appear in the data with a noise spectrum that is high in power at high spatial frequencies. This is a problem because most edge detection algorithms are preferentially sensitive to the high-frequency content in an image. Solid state cameras can introduce errors because of the spatial periodicity of their sensor elements. This can result in problems when image edges are aligned with camera pixel boundaries: (a) some cameras introduce transients into the video signal while switching between sensor elements; (b) most cameras use analog low-pass filters to minimize sampling artifacts and these introduce video phase delays that shift the locations of edges. The problems compound when the vision system samples asynchronously with the camera's pixel rate. Moire patterns (analogous to beat frequencies) can result. In this paper, we examine and model quantization effects in a machine vision system with particular emphasis on edge detection performance. We also compare our models with experimental measurements.

Paper Details

Date Published: 12 March 1988
PDF: 8 pages
Proc. SPIE 0850, Optics, Illumination, and Image Sensing for Machine Vision II, (12 March 1988); doi: 10.1117/12.942853
Show Author Affiliations
Stanley H. Zisk, MIT Haystack Observatory (United States)
Norman Wittels, WPI (United States)

Published in SPIE Proceedings Vol. 0850:
Optics, Illumination, and Image Sensing for Machine Vision II
Donald J. Svetkoff, Editor(s)

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