Geometry-based automatic object localization and 3-D pose detection

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Volumn 2002-January, Issue , 2002, Pages 144-147

  Magnor, M A ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Convolution; Detectors; Geometry; Image edge detection; Layout; Object detection; Pixel; Rendering (computer graphics); Solid modeling

Indexed keywords

COMPUTER GRAPHICS; CONVOLUTION; DETECTORS; EDGE DETECTION; GEOMETRY; IMAGE ANALYSIS; IMAGE MATCHING; OBJECT DETECTION; PIXELS; RENDERING (COMPUTER GRAPHICS); THREE DIMENSIONAL COMPUTER GRAPHICS;

3D POSE ESTIMATION; DEGREE OF PARALLELISM; GRAPHICS HARDWARE; IMAGE COORDINATES; IMAGE EDGE DETECTION; LAYOUT; OBJECT LOCALIZATION; SOLID MODEL;

OBJECT RECOGNITION;

EID: 20444504470     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IAI.2002.999907     Document Type: Conference Paper

References (13)

1. J. Aggarwal, J. Ghosh, D. Nair, and I. Taha. A comparative study of three paradigms for object recognition - bayesian statistics, neural networks and expert systems. In Image Understanding: A Festschrift for Azriel Rosenfeld, pages 241-262. IEEE Computer Society Press, 1996.
2. D. Ballard. Generalized hough transform to detect arbitrary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(2):111-122, 1981.
3. J. Beveridge and E. Riseman. Optimal geometric model-matching under full 3D perspective. Computer Vision, Graphics, and Image Processing (CVGIP): Image Understanding, 61(3):351-364, May 1995.
4. T. M. Breuel. A practical, globally optimal algorithm for geometric matching under uncertainty. Proc. International Workshop on Combinatorial Image Analysis (IWCIA 2001), Philadelphia, USA, Oct. 2001.
5. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986.
6. M. Costabile and G. Pieroni. Detecting shape correspondences by using the generalized hough transform. Proc. International Conference on Pattern Recognition (ICPR'86), Paris, France, pages 589-591, Oct. 1986.
7. E. R. Davies. Reduced parameter spaces for polygon detection using the generalized hough transform. Proc. International Conference on Pattern Recognition (ICPR'86), Paris, France, pages 495-497, Oct. 1986.
8. L. H. Jamieson, E. J. Delp, C.-C. Wang, J. Li, and F. J. Weil. A software environment for parallel computer vision. IEEE Computer, 25(2):73-77, 1992.
9. F. Jurie. Solution of the simultaneous pose and correspondence problem using gaussian error model. Computer Vision and Image Understanding, 73(3):357-373, 1999.
10. H. Kollnig and H. Nagel. Pose estimation by directly matching polyhedral models to gray value gradients. International Journal of Computer Vision, 23(3):283-302, 1997.
11. H. Lensch, W. Heidrich, and H.-P. Seidel.Automated texture registration and stitching for real world models. IEEE Proc. 8th Pacific Conf. Computer Graphics and Applications (PG-00), Hong Kong, China, pages 317-326, October 2000.
12. A. Pope. Model-based object recognition - A survey of recent research. Technical Report TR-94-04, Dept. Computer Science, Univ. British Columbia, Jan. 1994.
13. R. Strzodka and M. Rumpf. Level set segmentation in graphics hardware. Proc. IEEE International Conference on Image Processing (ICIP-2001), Thessaloniki, Greece, 3:1103-1106, Oct. 2001.