PERFORM: A Fast Object Recognition Method Using Intersection of Projection Error Regions

This paper describes an object recognition methodology called PERFORM that finds matches by establishing correspondences between model and image features using this formulation. PERFORM evaluates correspondences by intersecting error regions in the image space. The algorithm is analyzed with respect to theoretical complexity as well as actual running times. When a single solution to the matching problem is sought, the time complexity of the sequential matching algorithm for 2D-2D matching using point features is of the order O(l/sup 3/ N/sup 2/), where N is the number of model features and l is the number of image features. When line features are used, the sequential complexity is of the order O(l/sup 2/ N/sup 2/). When a single solution is sought, PERFORM runs faster than the fastest known algorithm to solve the bounded-error matching problem. The PERFORM method is shown to be easily realizable on both SIMD and MIMD architectures.

[1]  Robert C. Bolles,et al.  Locating Partially Visible Objects: The Local Feature Focus Method , 1980, AAAI.

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[4]  Linda G. Shapiro,et al.  Fast parallel object recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[5]  Linda G. Shapiro,et al.  Parallel algorithm for object recognition and its implementation on a MIMD machine , 1995, Proceedings of Conference on Computer Architectures for Machine Perception.

[6]  Jezekiel Ben-Arie The Probabilistic Peaking Effect of Viewed Angles and Distances with Application to 3-D Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Olivier D. Faugeras,et al.  HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Clark F. Olson Fast alignment using probabilistic indexing , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  H. Baird Model-Based Image Matching Using Location , 1985 .

[10]  Robert M. Haralick,et al.  PREMIO: an overview (object recognition) , 1991, [1991 Proceedings] Workshop on Directions in Automated CAD-Based Vision.

[11]  D. W. Thompson,et al.  Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[12]  Thomas M. Breuel,et al.  Fast recognition using adaptive subdivisions of transformation space , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[14]  Linda G. Shapiro,et al.  3D matching using statistically significant groupings , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[15]  W. Eric L. Grimson,et al.  A Study of Affine Matching With Bounded Sensor Error , 1992, ECCV.

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

[17]  Larry S. Davis,et al.  Computer Architectures for Machine Perception , 1993 .

[18]  Bharath Kumar Modayur Efficient parallel object recognition , 1996 .

[19]  Clark F. Olson,et al.  Time and space efficient pose clustering , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  W. Eric L. Grimson,et al.  Localizing Overlapping Parts by Searching the Interpretation Tree , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Robert M. Haralick,et al.  Optimal affine-invariant point matching , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.