A Probabilistic Intensity Similarity Measure based on Noise Distributions
- 1 June 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
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 L2 norm.Keywords
This publication has 8 references indexed in Scilit:
- The Bottleneck Geodesic: Computing Pixel AffinityPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Image Specific Feature SimilaritiesLecture Notes in Computer Science, 2006
- Color lines: image specific color representation.Published by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Learning affinity functions for image segmentation: combining patch-based and gradient-based approachesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Confidence measures for block matching motion estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Robust analysis of feature spaces: color image segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A model-based distance for clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2000
- Radiometric CCD camera calibration and noise estimationIeee Transactions On Pattern Analysis and Machine Intelligence, 1994