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A Probabilistic Intensity Similarity Measure based on Noise Distributions



We derive a probabilistic similarity measure between two observed image intensities that is based on the noise properties of the camera. In many vision algorithms, the effect of camera noise is either neglected or reduced in a preprocessing stage. However, noise reduction cannot be performed with high accuracy due to lack of knowledge about the true intensity signal. Our similarity metric specifically represents the likelihood that two intensity observations correspond to the same unknown noise-free scene radiance. By directly accounting for noise in the evaluation of similarity, the proposed measure makes noise reduction unnecessary and enhances many vision algorithms that involve matching of image intensities. Real-world experiments demonstrate the effectiveness of the proposed similarity measure in comparison to the standard L 2 norm. 1

Year: 2009
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