Abstract
Although interfering (outlying) details complicate image re- cognition and retrieval, ‘soft masking’ of outliers shows considerable promise for robust pixel-by-pixel image matching or reconstruction from principal components (PC). Modeling the differences between two images or between an image and its PC estimate (obtained as a projection onto a subspace of PCs) with a mixed distribution of random noise and outliers, the masks are produced by a simple iterative Expectation-Maximisation based procedure. Experiments with facial images (extracted from the MIT face database) demonstrate the efficiency of this approach.
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Delmas, P., Gimel’farb, G., Shorin, A., Morris, J. (2007). Robust Least-Squares Image Matching in the Presence of Outliers. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_96
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DOI: https://doi.org/10.1007/978-3-540-74272-2_96
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