Genetic programming based blind image deconvolution for surveillancesystems

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@Article{Mahmood:2013:EAAI,

• author = "Muhammad Tariq Mahmood and Abdul Majid and Jongwoo Han and Young Kyu Choi",
• title = "Genetic programming based blind image deconvolution for surveillancesystems",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "26",
• number = "3",
• pages = "1115--1123",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Surveillance systems, Deconvolution, Image restoration, Deblurring",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2012.08.001",
• URL = "http://www.sciencedirect.com/science/article/pii/S0952197612002023",
• abstract = "Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighbourhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.",

}

Genetic Programming entries for Muhammad Tariq Mahmood Abdul Majid Jongwoo Han Young Kyu Choi

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