Visual detection of lintel-occluded doors from a single image

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Volumn , Issue , 2008, Pages

  Chen, Zhichao ^a   Birchfield, Stanley T ^a  

^a AMRL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; DOOR DETECTION; FALSE POSITIVE RATE; GEOMETRIC FEATURES; INTENSITY PROFILES; LARGE DATABASES; MOBILE ROBOT NAVIGATION; RANGE SENSORS; REAL-TIME APPLICATIONS; SINGLE IMAGES; VISION-BASED; VISUAL DETECTION;

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; DOORS; FACE RECOGNITION; FEATURE EXTRACTION; IMAGE PROCESSING; LAPTOP COMPUTERS; MOBILE ROBOTS; PATTERN RECOGNITION; ROBOTICS; ROBOTS;

EDGE DETECTION;

EID: 51849152434     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2008.4563142     Document Type: Conference Paper

References (13)

1. D. Anguelov, D. Koller, E. Parker, and S. Thrun. Detecting and modeling doors with mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation, 2004.
2. S. Birchfield. Elliptical head tracking using intensity gradients and color histograms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 232-237, June 1998.
3. J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698. 1986.
4. G. Cicirelli, T. D'Orazio, and A. Distante. Target recognition by components for mobile robot navigation. Journal of Experimental & Theoretical Artificial Intelligence, 15(3):281-297, 2003.
5. P. Felzenszwalb and D. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2):167-181, 2004.
6. Y. Freund and R. E. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, Sept. 1999.
7. D. Kim and R. Nevatia. A method for recognition and localization of generic objects for indoor navigation. In AREA Image Understanding Workshop, 1994.
8. P. D. Kovesi. MATLAB and Octave functions for computer vision and image processing. School of Computer Science & Software Engineering, The University of Western Australia. Available from: .
9. I. Monasterio, E. Lazkano, I. Rano, and B. Sierra. Learning to traverse doors using visual information. Mathematics and Computers in Simulation, 60(3):347-356, Sept. 2002.
10. R. Munoz-Salinas, E. Aguirre, M. Garcia-Silvente. and A. Gonzalez. Door-detection using computer vision and fuzzy logic. In Proceedings of the 6th WSEAS International Conference on Mathematical Methods & Computational Techniques in Electrical Engineering, 2004.
11. I. Nourbakhsh, R. Powers, and S. BircMeld. Dervish: An office-navigating robot. AI Magazine, 16(2):53-60, 1995.
12. M. Rous, H. Lupschen, and K.-F. Kraiss. Vision-based indoor scene analysis for natural landmark detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2005.
13. S. A. Stoeter, F. L. Mauff. and N. P. Papanikolopoulos. Realtime door detection in cluttered environments. In Proceedings of the 15th IEEE International Symposium on Intelligent Control, 2000.