Aach T, Kaup A, Mester R (1993)
Publication Type: Journal article
Publication year: 1993
Publisher: Elsevier
Book Volume: 31
Pages Range: 165-180
Journal Issue: 2
DOI: 10.1016/0165-1684(93)90063-G
A major issue with change detection in video sequences is to guarantee robust detection results in the presence of noise. In this contribution, we first compare different test statistics in this respect. The distributions of these statistics for the null hypothesis are given, so that significance tests can be carried out. An objective comparison between the different statistics can thus be based on identical false alarm rates. However, it will also be pointed out that the global thresholding methods resulting from the significance approach exhibit certain weaknesses. Their shortcomings can be overcome by the Markov random field based refining method derived in the second part of this paper. This method serves three purposes: it accurately locates boundaries between changed and unchanged areas, it brings to bear a regularizing effect on these boundaries in order to smooth them, and it eliminates small regions if the original data permits this. © 1993.
APA:
Aach, T., Kaup, A., & Mester, R. (1993). Statistical Model-Based Change Detection in Moving Video. Signal Processing, 31(2), 165-180. https://dx.doi.org/10.1016/0165-1684(93)90063-G
MLA:
Aach, T., André Kaup, and R. Mester. "Statistical Model-Based Change Detection in Moving Video." Signal Processing 31.2 (1993): 165-180.
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