Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/46500
Title: A computational model for visual size, location and movement
Authors: Alemán-Flores, Miguel 
Leibovic, K. Nicholas
Moreno-Díaz, Roberto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Computational vision
Motion discrimination
Visual location
Issue Date: 1997
Journal: Lecture Notes in Computer Science 
Conference: 6th International Workshop on Computer Aided Systems Theory, EUROCAST 1997 
Abstract: The ability to detect object size, location and movement is essential for a visual system in either a biological or man made environment. In this paper we present a model for estimating these parameters by using a set of randomly distributed receptive fields on a retina. This approach differs from more conventional ones in which the receptive fields are arranged in a geometric pattern. The simulation of the model has been performed with a software implementation in a layered fashion. From the input level, computations are performed in parallel which are then combined at a subsequent level to yield estimates of the size and center of gravity of an object. Movement discrimination is implemented by a lateral interaction scheme. The randomly generated receptive fields are now divided into eight weighted classes, corresponding one to a different direction, with the same number of receptive fields for each direction. Both, borders and contrast areas of the object, are useful to identify its motion. When one of the receptive fields detects a border, the weights are changed according to its preferred direction, so that it is possible to tbllow the movement of the object if it moves this way. Due to the stochastic nature of the model we can study the effects of receptive field size and density on the results which can be obtained with any desired degree of accuracy. Moreover, since all the parameters are calculated in parallel, based on the same principles and using similar operations, it is possible to have the different parts of the network interact and to make use of results obtained by other subsystems. Finally, in biological systems one also finds some randomness side by side with more deterministic structures. Our model is therefore consistent with this aspect of biological organization
URI: http://hdl.handle.net/10553/46500
ISBN: 978-3-540-63811-7
3540638113
ISSN: 0302-9743
DOI: 10.1007/BFb0025062
Source: Pichler F., Moreno-Díaz R. (eds) Computer Aided Systems Theory — EUROCAST'97. EUROCAST 1997. Lecture Notes in Computer Science, vol 1333. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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