NDLI: Robust detection of a set of outliers for image changes based on rerunning the regression

Content Provider	IEEE Xplore Digital Library
Author	Dong Sik Kim
Copyright Year	2009
Description	Author affiliation: School of Electronics and Information Engineering, Hankuk University of Foreign Studies, Gyonggi-do, 449-791, Korea (Dong Sik Kim)
Abstract	By comparing two images, which are captured with the same scene at different times, we can detect the image changes due to moving objects. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, the algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergency of the estimates of the noise variance. Using an empirical compensation constant for the estimate is also proposed. The compensation enables the detection algorithm robust to the choice of thresholds for selecting outliers.
Starting Page	953
Ending Page	956
File Size	549753
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4959743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Polynomials Noise robustness Analysis of variance Detection algorithms Layout Iterative algorithms Algorithm design and analysis Image converters Lighting regression Doubly truncated samples outlier
Content Type	Text
Resource Type	Article

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