From Multidimensional Signals to the Generation of Responses

It has become increasingly apparent that perception cannot be treated in isolation from the response generation, firstly because a very high degree of integration is required between different levels of percepts and corresponding response primitives. Secondly, it turns out that the response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. The state of the system is in consequence the combination of the responses produced and the percepts associated with these responses. Thirdly, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain.

[1]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[2]  G. Granlund,et al.  On Scale and Orientation Adaptive Filtering , 1992 .

[3]  Dennis Gabor,et al.  Theory of communication , 1946 .

[4]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  T. Landelius Behavior Representation by Growing a Learning Tree , 1993 .

[6]  R. Held,et al.  MOVEMENT-PRODUCED STIMULATION IN THE DEVELOPMENT OF VISUALLY GUIDED BEHAVIOR. , 1963, Journal of comparative and physiological psychology.

[7]  Gösta H. Granlund Integrated Analysis-Response Structures for Robotics Systems , 1988 .

[8]  Hans Knutsson,et al.  A Dynamic Tree Structure for Incremental Reinforcement Learning of Good Behavior , 1994 .

[9]  D. Lindsley Physiological psychology. , 1956, Annual review of psychology.

[10]  Hans Knutsson,et al.  Representation and learning of invariance , 1994, Proceedings of 1st International Conference on Image Processing.

[11]  S. Lisberger,et al.  The Cerebellum: A Neuronal Learning Machine? , 1996, Science.

[12]  H. Knutsson,et al.  Behaviorism and Reinforcement Learning , 1995 .

[13]  R. Hughes,et al.  The Structure and Interpretation of Quantum Mechanics , 1989 .

[14]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[15]  J. Bower,et al.  Cerebellum Implicated in Sensory Acquisition and Discrimination Rather Than Motor Control , 1996, Science.

[16]  G. Granlund In search of a general picture processing operator , 1978 .

[17]  Harry Wechsler,et al.  A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images , 1982, Pattern Recognit. Lett..

[18]  K. Kanatani Camera rotation invariance of image characteristics , 1987 .

[19]  Hans Knutsson,et al.  Reinforcement Learning Adaptive Control and Explicit Criterion Maximization , 1996 .

[20]  Tomaso Poggio,et al.  Image Representations for Visual Learning , 1996, Science.

[21]  C. Shatz,et al.  Synaptic Activity and the Construction of Cortical Circuits , 1996, Science.

[22]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

[23]  W. Precht The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.

[24]  Andrea J. van Doorn,et al.  Invariant Properties of the Motion Parallax Field due to the Movement of Rigid Bodies Relative to an Observer , 1975 .

[25]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .