Generalised correlation for multi-feature correspondence

Computing correspondences between pairs of images is fundamental to all structures from motion algorithms. Correlation is a popular method to estimate similarity between patches of images. In the standard formulation, the correlation function uses only one feature such as the gray level values of a small neighbourhood. Research has shown that different features--such as colour, edge strength, corners, texture measures--work better under di erent conditions. We propose a framework of generalized correlation that can compute a real valued similarity measure using a feature vector whose components can be dissimilar. The framework can combine the e ects of di erent image features, such as multi-spectral features, edges, corners, texture measures, etc., into a single similarity measure in a exible manner. Additionally, it can combine results of di erent window sizes used for correlation with proper weighting for each. Relative importances of the features can be estimated from the image its...

• Type: Journal
• Year: 2002
• Where: PR
• Authors: C. V. Jawahar, P. J. Narayanan
• Keywords: Image, PR 2002, Similarity Measure, Texture Measures