Democratic Integration: Self-Organized Integration of Adaptive Cues

Sensory integration or sensor fusionthe integration of information from different modalities, cues, or sensorsis among the most fundamental problems of perception in biological and artificial systems. We propose a new architecture for adaptively integrating different cues in a self-organized manner. In Democratic Integration different cues agree on a result, and each cue adapts toward the result agreed on. In particular, discordant cues are quickly suppressed and recalibrated, while cues having been consistent with the result in the recent past are given a higher weight in the future. The architecture is tested in a face tracking scenario. Experiments show its robustness with respect to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, although all cues may be disrupted at one time or another.

[1]  Moshe Kam,et al.  Sensor Fusion for Mobile Robot Navigation , 1997, Proc. IEEE.

[2]  R. Jacobs,et al.  Experience-dependent visual cue integration based on consistencies between visual and haptic percepts , 2001, Vision Research.

[3]  Jochen Triesch,et al.  Democratic Integration: A Theory of Adaptive Sensory Integration , 2000 .

[4]  Hartmut Neven,et al.  PersonSpotter-fast and robust system for human detection, tracking and recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[5]  Mahmood R. Azimi-Sadjadi,et al.  A study of cloud classification with neural networks using spectral and textural features , 1999, IEEE Trans. Neural Networks.

[6]  Ren C. Luo,et al.  Multisensor integration and fusion in intelligent systems , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Robin R. Murphy,et al.  Biological and cognitive foundations of intelligent sensor fusion , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Belur V. Dasarathy,et al.  Sensor fusion potential exploitation-innovative architectures and illustrative applications , 1997, Proc. IEEE.

[9]  Virginia R. de Sa,et al.  Unsupervised Classification Learning from Cross-Modal Environmental Structure , 1994 .

[10]  Robert A. Jacobs,et al.  Modeling the Combination of Motion, Stereo, and Vergence Angle Cues to Visual Depth , 1999, Neural Computation.

[11]  Jochen Triesch Self-organized integration of adaptive visual cues for face tracking , 2000, SPIE Defense + Commercial Sensing.

[12]  Peter W. Pachowicz,et al.  Expert assisted, adaptive, and robust fusion architecture , 1998 .

[13]  Farid Amoozegar,et al.  Neural network fusion capabilities for efficient implementation of tracking algorithms , 1996, Defense, Security, and Sensing.

[14]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[15]  Horst-Michael Groß,et al.  User localisation for visually-based human-machine-interaction , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[16]  Dana H. Ballard,et al.  Category Learning Through Multimodality Sensing , 1998, Neural Computation.

[17]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[18]  Sauro Longhi,et al.  Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots , 1999, IEEE Trans. Robotics Autom..

[19]  A. Steinhage,et al.  Attractor dynamics to fuse strongly perturbed sensor data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[20]  Jan C. Vorbrüggen Zwei Modelle zur datengetriebenen Segmentierung visueller Daten , 1995 .

[21]  Jochen Triesch,et al.  Vision based robotic gesture recognition , 1999 .

[22]  Jochen Triesch,et al.  Robotic Gesture Recognition , 1997, Gesture Workshop.

[23]  Heinrich H. Bülthoff,et al.  Touch can change visual slant perception , 2000, Nature Neuroscience.

[24]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[25]  Shaogang Gong,et al.  Modelling facial colour and identity with Gaussian mixtures , 1998, Pattern Recognit..