1 July 2002 Centroid tracking using a probability map for target segmentation
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Proceedings Volume 4714, Acquisition, Tracking, and Pointing XVI; (2002); doi: 10.1117/12.472592
Event: AeroSense 2002, 2002, Orlando, FL, United States
Abstract
The central problem of image based centroid tracking is that of target segmentation, that is, determining which pixels belong to the target and which belong to the background. Once the target pixels are identified, the centroid (the center of gravity) of these pixels can be used as the estimated target position. The underlying assumption made in centroid tracking is that the target image contains intensity values that are unlikely to occur in the background. Based on this assumption, the centroid tracker uses three concentric gates to determine which pixels are target pixels. The areas bounded by these gates form three disjoint regions; the inner region, the track region, and the outer region. An inner histogram is collected over inner region that should contain mostly target pixels. An outer histogram is collected over the outer region that should contain only background pixels. These histograms are then used to generate a probability map that indicates the probability that a pixel with a given intensity is part of the target. This probability map is then used to segment the target and find its centroid. This paper describes the methods used to generate the probability map and its use in the centroid tracking algorithm. The performance of this algorithm is compared to that of the previously used dual-threshold segmentation algorithm.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John E. Albus, Lloyd J. Lewins, Julie R. Schacht, "Centroid tracking using a probability map for target segmentation", Proc. SPIE 4714, Acquisition, Tracking, and Pointing XVI, (1 July 2002); doi: 10.1117/12.472592; https://doi.org/10.1117/12.472592
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KEYWORDS
Image segmentation

Thallium

Detection and tracking algorithms

Chromium

Image processing

Rubidium

Servomechanisms

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