Fast initialization of active contours

The field of robotics is currently undergoing a change toward creation of robots that can naturally interact with humans. For achieving this, interactive robots must be endowed with natural interfaces that can sense and respond in real-time. Vision can provide handy information for this purpose by detecting and tracking human limbs to analyze gestures, actions and even emotions. However, real-time processing of visual information is a challenging bottleneck. In this paper, we introduce a novel method, namely "self-organized contours", that can distinctly accelerate contour initialization, which is the slowest phase in visual tracking. Although the proposed method is general-purpose, it allows immediate initialization of active contours due to its similarity with snake structure. The proposed method is inspired from group behavior in insects and animals, particularly fishes.

[1]  Chris Messom,et al.  Machine Vision for an Intelligent Tutor , 2003 .

[2]  Rainer Stiefelhagen,et al.  Recognition of 3 D-Pointing Gestures for Human-Robot-Interaction , 2003 .

[3]  Demetri Terzopoulos,et al.  Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  B L Partridge,et al.  The structure and function of fish schools. , 1982, Scientific American.

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  J. Deneubourg,et al.  Self-assemblages in insect societies , 2002, Insectes Sociaux.

[7]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[8]  Alexander Zelinsky,et al.  Intuitive Human-Robot Interaction Through Active 3D Gaze Tracking , 2003, ISRR.

[9]  Aggelos K. Katsaggelos,et al.  Lip tracking for MPEG-4 facial animation , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[10]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[11]  B. Tiddeman,et al.  Synthesis and transformation of three-dimensional facial images , 1999, IEEE Engineering in Medicine and Biology Magazine.

[12]  Yuan Yan Tang,et al.  An evolutionary autonomous agents approach to image feature extraction , 1997, IEEE Trans. Evol. Comput..

[13]  W. Herrnkind,et al.  Drag Reduction by Formation Movement in Spiny Lobsters , 1976, Science.

[14]  Brendan J. Frey,et al.  Detection and tracking of faces and facial features , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[15]  Wael Abd-Almageed,et al.  Eye Tracking Using Active Deformable Models , 2002, ICVGIP.

[16]  Roberto Cipolla,et al.  Uncalibrated Stereo Vision with Pointing for a Man-Machine Interface , 1994, MVA.