3-D object recognition using projective invariant relationship by single-view

We propose a new method for recognizing three-dimensional objects using a three-dimensional invariant relationship for a special structure and geometric hashing by single-view. We use a special structure consisting of four co-planar points and any two non-coplanar points with respect to the plane. We derive an invariant relationship for the structure, which is represented by a plane equation. For recognition of 3-D objects using geometric hashing, a set of points on the plane is mapped into a set of points intersecting the plane and the unit sphere, thereby satisfying the invariant relationship. Experiments using 3-D polyhedral objects are carried out to demonstrate the feasibility of our method for 3-D object recognition.

[1]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[2]  Eamon B. Barrett,et al.  Closed-form extension of the anharmonic ratio to N-space , 1983, Comput. Vis. Graph. Image Process..

[3]  Frank C. D. Tsai A Probabilistic Approach to Geometric Hashing Using Line Features , 1993, Comput. Vis. Image Underst..

[4]  David A. Forsyth,et al.  Extracting projective structure from single perspective views of 3D point sets , 1993, 1993 (4th) International Conference on Computer Vision.

[5]  Lakmal D. Seneviratne,et al.  A new structure of invariant for 3D point sets from a single view , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[6]  Eamon B. Barrett,et al.  Representation of Three-Dimensional Object Structure as Cross-Ratios of Determinants of Stereo Image Points , 1993, Applications of Invariance in Computer Vision.

[7]  Eamon B. Barrett,et al.  General methods for determining projective invariants in imagery , 1991, CVGIP Image Underst..

[8]  Haim J. Wolfson,et al.  Model-Based Object Recognition by Geometric Hashing , 1990, ECCV.

[9]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Long Quan,et al.  Invariants of Six Points and Projective Reconstruction From Three Uncalibrated Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Andrew Zisserman,et al.  A Case Against Epipolar Geometry , 1993, Applications of Invariance in Computer Vision.

[12]  Edward M. Riseman,et al.  The non-existence of general-case view-invariants , 1992 .

[13]  Long Quan,et al.  Invariants of 6 Points from 3 Uncalibrated Images , 1994, ECCV.

[14]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[16]  Josef Kittler,et al.  Low-level Grouping of Straight Line Segments , 1991 .

[17]  Lakmal D. Seneviratne,et al.  Three dimensional object recognition using invariants , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.