3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
Representation of local geometry in the visual system
Biological Cybernetics
International Journal of Computer Vision
Artificial Intelligence - Special volume on computer vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry and Matching of Curves in Multiple Views
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Automatic line matching across views
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmented shape descriptions from 3-view stereo
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Recognizing 3D objects using photometric invariant
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Detecting Changes in Aerial Views of Man-Made Structures
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust Computation and Parametrization of Multiple View Relations
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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We describe two image matching techniques that owe their success to a combination of geometric and photometric constraints. In the first, images are matched under similarity transformations by using local intensity invariants and semi-local geometric constraints. In the second, 3D curves and lines are matched between images using epipolar geometry and local photometric constraints. Both techniques are illustrated on real images. We show that these two techniques may be combined and are complementary for the application of image retrieval from an image database. Given a query image, local intensity invariants are used to obtain a set of potential candidate matches from the database. This is very efficient as it is implemented as an indexing algorithm. Curve matching is then used to obtain a more significant ranking score. It is shown that for correctly retrieved images many curves are matched, whilst incorrect candidates obtain very low ranking.