Optimization of background subtraction for image enhancement

Larry Venetsky; Ross Boczar; Robert Lee-Own

doi:10.1117/12.2014319

28 May 2013 Optimization of background subtraction for image enhancement

Larry Venetsky, Ross Boczar, Robert Lee-Own

Proceedings Volume 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII; 875102 (2013) https://doi.org/10.1117/12.2014319
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Analysis of foreground objects in scenery via image processing often involves a background subtraction process. This process aims to improve blob (connected component) content in the image. Quality blob content is often needed for defining regions of interest for object recognition and tracking. Three techniques are examined which optimize the background to be subtracted - genetic algorithm, an analytic solution based on convex optimization, and a related application of the CVX solver toolbox. These techniques are applied to a set of images and the results are compared. Additionally, a possible implementation architecture that uses multiple optimization techniques with subsequent arbitration to produce the best background subtraction is considered.

Citation Download Citation

Larry Venetsky, Ross Boczar, and Robert Lee-Own "Optimization of background subtraction for image enhancement", Proc. SPIE 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII, 875102 (28 May 2013); https://doi.org/10.1117/12.2014319

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