Adaptive computer vision: online learning for object recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3175, Issue , 2004, Pages 447-454

  Bekel, Holger ^a   Bax, Ingo ^a   Heidemann, Gunther ^a   Ritter, Helge ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; NEURAL NETWORKS; PATTERN RECOGNITION;

APPEARANCE-BASED VISION; CLASSIFICATION PERFORMANCE; CLASSIFICATION SYSTEM; NEURAL CLASSIFIERS; ONLINE LEARNING; PARALLEL PROCESSING; RUNNING SYSTEMS; VISUAL KNOWLEDGE;

OBJECT RECOGNITION;

EID: 35048837648     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28649-3_55     Document Type: Article

References (14)

1. G. Heidemann, R. Rae, H. Bekel, I. Bax, and H. Ritter. Integrating context-free and context-dependent attentional mechanisms for gestural object reference. In Proc. Int'l Conf. Cognitive Vision Systems, pages 22-33, Graz, Austria, 2003.
2. G. Heidemann and H. Ritter. Efficient Vector Quantization Using the WTA-rule with Activity Equalization. Neural Processing Letters, 13(1):17-30, 2001.
3. Roger E. Kahn, Michael J. Swain, Peter N. Prokopowicz, and R. James Firby. Gesture recognition using the perseus architecture. Technical Report TR-96-04, 19, 1996.
4. T. Kalinke and W. von Seelen. Entropie als Maß des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmerksamkeitssteuerung. In B. Jähne, P. Geißler, H. Haußecker, and F. Hering, editors, Mustererkennung 1996, pages 627-634. Springer Verlag Heidelberg, 1996.
5. T. Kohonen. Self-Organizing Maps. Springer Verlag, 1995.
6. A. Leonardis, H. Bischof, and J. Maver. Multiple eigenspaces. Pattern Recognition, 35(ll):2613-2627, 2002.
7. H. J. Andersen, M. Stoerring and E. Granum. Physics-based modelling of human Skin colour under mixed illuminants. Robotics and Autonomous Systems, 35(34):131-142, 2001.
8. B. W. Mel. SEEMORE: Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation, 9: 777-804, 1997.
9. II. Murase and S. K. Nayar. Visual Learning and Recognition of 3-D Objects from Appearance. Int'l J. of Computer Vision, 14:5-24, 1995.
10. J. C. Ossola, F. Bremond, and M. Thonnat. A communication level in a distributed architecture for object recognition. In 8th International Conference on Systems Research Informatics and Cybernetics, Aug 1996.
11. H. J. Ritter, T. M. Martinetz, and K. J. Schulten. Neuronale Netze. AddisonWesley, München, 1992.
12. T. D. Sanger. Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network. Neural Networks, 2:459-473, 1989.
13. C. Theis, I. lossifidis, and A. Steinhage. Image Processing Methods for Interactive Robot Control. In Proc. IEEE Roman International Workshop on Robot-Human Interactive Communication, Bordeaux and Paris, France, 2001.
14. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, 1999.