A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships

作者：Dickscheid Timo^*; Foerstner Wolfgang

来源：Photogrammetrie Fernerkundung Geoinformation, 2013, 2013(4): 269-283.

DOI：10.1127/1432-8364/2013/0176

• local features
• feature matching
• wide baseline stereo
• image orientation

Many vision applications rely on local features for image analysis, notably in the areas of object recognition, image registration and camera calibration. One important example in photogrammetry are fully automatic algorithms for relative image orientation. Such applications rely on a matching algorithm to extract a sufficient number of correct feature correspondences at acceptable outlier rates, which is most often based on the similarity of feature descriptions. When the number of detected features is low, it is advisable to use multiple feature detectors with complementary properties. When feature similarity is not sufficient for matching, spatial feature relationships provide valuable information. In this work, a highly generic matching algorithm is proposed which is based on a trainable Markov random field (MRF). It is able to incorporate almost arbitrary combinations of features, similarity measures and pairwise spatial relationships, and has a clear statistical interpretation. A major novelty is its ability to compensate for weaknesses in one information cue by implicitely exploiting the strengths of others.

• 出版日期2013