Model indexing and object recognition using 3D viewpoint invariance

Object recognition and location of 3D objects from range data are described, based on invariant representations of characteristic views of single objects. Depth data is processed to remove outliers, then smoothed and segmented into surface patches, fitted by polynomial equations. Mathematical invariants are derived from groups of these surface patches and used for indexing a model database stored in the form of a kd tree. The pose of the known object may then be determined from the Euclidean centre and orientation of the higher order surfaces formed from the feature groups. The approach is complete, scalable and extendable. The robustness of the method is evaluated in terms of the stability and discriminatory power of the representation, and the accuracy of the determined pose.

[1]  Andrew M. Wallace,et al.  Viewer-Centred Representations for Location and Inspection , 1994, BMVC.

[2]  Patrick Mcandrew.  Recognising and locating objects in two-dimensional perspective views (object recognition) , 1990 .

[3]  Anil K. Jain,et al.  Three-Dimensional Object Recognition Systems , 1993 .

[4]  Emanuele Trucco,et al.  Visibility scripts for active feature-based inspection , 1994, Pattern Recognit. Lett..

[5]  R. Brockett.  Least squares matching problems , 1989 .

[6]  ANDREW M. WALLACE,et al.  A comparison of approaches to high-level image interpretation , 1988, Pattern Recognit..

[7]  Marc H. Raibert,et al.  Running With Symmetry , 1986 .

[8]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[9]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[10]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[11]  Anil K. Jain,et al.  BONSAI: 3D Object Recognition Using Constrained Search , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  D. Hilbert,et al.  Theory of algebraic invariants , 1993 .

[13]  Andrew M. Wallace,et al.  Compilation and sufficient representation of object models for visual recognition , 1992, Pattern Recognit. Lett..

[14]  A. Wallace,et al.  Outlier removal and discontinuity preserving smoothing of range data , 1996 .

[15]  Vaughan R. Pratt,et al.  Direct least-squares fitting of algebraic surfaces , 1987, SIGGRAPH.

[16]  Katsushi Ikeuchi,et al.  Appearance-Based Vision and the Automatic Generation of Object Recognition Programs , 1992 .

[17]  Patrick McAndrew.  Recognizing and locating objects in two-dimensional perspective views , 1992 .

[18]  Robert C. Bolles,et al.  3DPO: A Three- Dimensional Part Orientation System , 1986, IJCAI.

[19]  Jake K. Aggarwal,et al.  Model-based object recognition in dense-range images—a review , 1993, CSUR.

[20]  Kevin W. Bowyer,et al.  Aspect graphs: An introduction and survey of recent results , 1990, Int. J. Imaging Syst. Technol..

[21]  Satoru Kawai,et al.  A simple method for computing general position in displaying three-dimensional objects , 1988, Comput. Vis. Graph. Image Process..

[22]  Andrew M. Wallace.  Matching segmented scenes to models using pairwise relationships between features , 1987, Image Vis. Comput..

[23]  Avinash C. Kak,et al.  A robot vision system for recognizing 3D objects in low-order polynomial time , 1989, IEEE Trans. Syst. Man Cybern..

[24]  C. Taubin.  Algebraic Curves and Surfaces in 3D Computer Vision , 1992, IMA Conference on the Mathematics of Surfaces.

[25]  Guanghua Zhang,et al.  Model matching using multiple data sources , 1993, Electronic Imaging.

[26]  C. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[27]  Gene H. Golub,et al.  Matrix computations , 1983 .

[28]  S. Ullman.  Aligning pictorial descriptions: An approach to object recognition , 1989, Cognition.

[29]  Jon Louis Bentley,et al.  Data Structures for Range Searching , 1979, CSUR.