On the Evolution of Formal Models and Artificial Neural Architectures for Visual Motion Detection

• Published in: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach pp. 479 - 488
• Main Authors: Moreno-Díaz, R., Quesada-Arencibia, A., Rodríguez-Rodríguez, J. C.
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Gradient Model; Motion Detection; Motion Perception; Neural Architecture; Spatial Integration; Human; Knowledge engineering; Formal method; Probabilistic approach; Motion estimation; Cell biology; Cognition; Selectivity; Neural network; Random distribution; Modeling; Non determinism; Biological system; Formal specification; Homogeneity; Deterministic system; Motion detection; Cost analysis; Reliability; Pattern analysis; Motion analysis; Artificial intelligence; Non deterministic system
• ISBN: 9783540263197; 3540263195
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11499305_49

Motion is a key, basic descriptor of our visual experience of the outside world. The lack of motion perception is a devastating illness that leads to death in animals and seriously impaired behavior in humans. Thus, the study of biological basis of motion detection and analysis and the modelling and artificial implementation of those mechanisms has been a fruitful path of science in the last 60 years. Along this paper, the authors make a review of the main models of motion perception that have emerged since the decade of the 60’s stress-ing the underlying biological concepts that have inspired most of them and the traditional architectural concepts imprinted in their functionality and design: formal mathematical analysis, strict geometric patterns of neuron-like processors, selectivity of stimulate etc. Traditional approaches are, then, questioned to include “messy” characteristics of real biological systems such as random distribution of neuron-like processors, non homogeneity of neural architecture, sudden failure of processing units and, in general, non deterministic behavior of the system. As a result is interesting to show that reliability of motion analysis, computational cost and extraction of pure geometrical visual descriptors (size and position of moving objects) besides motion are improved in an implemented model.