Name
Abstract | ||
|---|---|---|
Artificial neural models suggest that the probability density function (PDF) of available inputs is crucial in determining the distribution of responses shown by a group of pattern-matching cells. This paper therefore describes a Monte-Carlo study of the PDF for linear optic flow components produced by ego-motion in a simulated planar environment. The recent search for deformation-selective cells in the medial superior temporal (MST) area of the cerebral cortex is used to illustrate the biological significance of the optic flow PDF. The simulation results are consistent with the neurophysiological finding that MST cells exhibit a continuum of responses to translation, rotation and divergence. In addition, there are strong negative correlations between deformation and other first-order flow components. The deformation components contain information necessary for recovering shape from flow. Consequently, if cells responsible for shape analysis are present in the MST area they should respond best to combinations of deformation with other flow components, rather than to the pure stimuli used in previous neurophysiological studies |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICPR.1998.711267 | Pattern Recognition, 1998. Proceedings. Fourteenth International Conference |
Keywords | Field | DocType |
Monte Carlo methods,brain models,image reconstruction,image sequences,neural nets,neurophysiology,physiological models,probability,vision,MST area,Monte-Carlo study,artificial neural models,cerebral cortex,deformation,deformation-selective cells,divergence,ego-motion,first-order flow components,joint probability density function,linear optic flow components,medial superior temporal area,neurophysiology,optic flow PDF,pattern-matching cells,response distribution,rotation,shape analysis,strong negative correlations,translation | Computer vision,Monte Carlo method,Neurophysiology,Medial superior temporal area,Flow (psychology),Artificial intelligence,Deformation (mechanics),Artificial neural network,Probability density function,Mathematics,Shape analysis (digital geometry) | Conference |
Volume | ISSN | ISBN |
1 | 1051-4651 | 0-8186-8512-3 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jim Ivins | 1 | 89 | 10.95 |
| John Porrill | 2 | 352 | 85.11 |
| John Frisby | 3 | 0 | 0.34 |
| Orban, G. | 4 | 0 | 0.34 |