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Optimizing image matches via a verification model
Author(s) -
Lee Jimmy Addison,
Yow KinChoong
Publication year - 2010
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20441
Subject(s) - outlier , computer science , artificial intelligence , image (mathematics) , matching (statistics) , viewpoints , pattern recognition (psychology) , conditional probability , computer vision , boom , data mining , mathematics , statistics , art , environmental engineering , engineering , visual arts
In the literature, we have seen a boom in wide‐baseline matching approaches proposed for locating correspondences between images. However, wrong correspondences or the so‐called outliers are still rather inevitable, especially in urban environments with the presence of repetitive structures, and/or a large dissimilarity in viewpoints. In this paper, we propose a verification model to optimize the image matching results by significantly reducing the number of outliers. Several geometric and appearance‐based measurements are exploited, and conditional probability is used to compute the probability of each true correspondence. The model is validated by extensive experiments on images from the ZuBud database, which are taken in different weather conditions, seasons, and with different cameras. It is also demonstrated on a real‐time application of an image‐based navigation system. © 2010 Wiley Periodicals, Inc.