Computer graphics
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On improving the accuracy of the Hough transform
Machine Vision and Applications
Model-based object recognition by geometric hashing
ECCV 90 Proceedings of the first european conference on Computer vision
3D object recognition from 2D images using geometric hashing
Pattern Recognition Letters
3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
A Bayesian approach to model matching with geometric hashing
Computer Vision and Image Understanding
Object recognition with stereo vision and geometric hashing
Pattern Recognition Letters
Object recognition with stereo vision and geometric hashing
Pattern Recognition Letters
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In this paper we demonstrate a method to recognize 3D objects and to estimate their pose. For that purpose we use a combination of stereo vision and geometric hashing. Stereo vision is used to generate a large number of 3D low level features, of which many are spurious because at that stage of the process the correspondence problem is not solved as yet. However, geometric hashing is used to discriminate the true features from the spurious one. Geometric hashing is also the basis of a voting mechanism for the recognition of the objects in the scene. The speed of the geometric hashing algorithm helps to overcome the computational burden imposed by the correspondence problem in stereo vision. We look at different hash strategies using both points and lines features and compare our 3D approach to a recognition system based on 2D features. Experiments show that, although our 3D approach generates much more spurious scene features, it is just as fast and more reliable than the 2D system.