21 August 1987 Statistical Segmentation Of Digital Images
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Proceedings Volume 0754, Optical and Digital Pattern Recognition; (1987) https://doi.org/10.1117/12.939988
Event: OE LASE'87 and EO Imaging Symposium, 1987, Los Angeles, CA, United States
Abstract
Statistically based models of digital images are used to locate and segment objects of interest from background scenes. Three models are presented and evaluated. These models are based on a Bayesian cost function, a Neyman-Pearson constant false alarm rate function, and a maximum entropy function. Detailed algorithms are presented for separating object regions from background clutter using each of these statistical methods.
© (1987) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jay B. Jordan, G. M. Flachs, "Statistical Segmentation Of Digital Images", Proc. SPIE 0754, Optical and Digital Pattern Recognition, (21 August 1987); doi: 10.1117/12.939988; https://doi.org/10.1117/12.939988
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