Paper
1 April 1990 Obtaining Centroids Of Digitized Regions Using Square And Hexagonal Tilings For Photosensitive Elements
Samir Chettri, Michael Keefe, John Zimmerman
Author Affiliations +
Proceedings Volume 1194, Optics, Illumination, and Image Sensing for Machine Vision IV; (1990) https://doi.org/10.1117/12.969848
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States
Abstract
In computer vision and graphics, the square or rectangular tessellation is most commonly used. The hexagonal lattice has not been studied as frequently. In this paper we project squares and circles on each of these grids, digitize these figures and obtain the error in locating the centroid. We adopt a Monte Carlo approach, with the centroid of the actual figures being chosen randomly. In the case of the circle, we project various sizes onto the respective grids and study the error in obtaining the centroid. In the case of the square we combine different sizes with angles of rotation that vary from 0 to 90 degrees. Theoretical formulae are developed for the circle on square tile case. These symbolic representations are compared to the results from the Monte Carlo simulation and they are found to be quite close. Finally, comparisons are drawn between the two grids. In the case of the circle, there is a definite advantage in using the hexagonal grid. For the square there is no inherent advantage to either. These results are of use if it is decided to build a camera with hexagonal picture elements.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Samir Chettri, Michael Keefe, and John Zimmerman "Obtaining Centroids Of Digitized Regions Using Square And Hexagonal Tilings For Photosensitive Elements", Proc. SPIE 1194, Optics, Illumination, and Image Sensing for Machine Vision IV, (1 April 1990); https://doi.org/10.1117/12.969848
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Cited by 3 scholarly publications.
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KEYWORDS
Monte Carlo methods

Machine vision

Error analysis

Raster graphics

Quantization

Computer vision technology

Cameras

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