Evolution of visual resolution constrained by a trade-off
Artificial Life
Animat vision: Active vision in artificial animals
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Empirical Explanation of the Chubb Illusion
Journal of Cognitive Neuroscience
Modular thinking: evolving modular neural networks for visual guidance of agents
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Investigating the emergence of multicellularity using a population of neural network agents
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Analysing the evolvability of neural network agents through structural mutations
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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The fundamental challenge faced by any visual system within natural environments is the ambiguity caused by the fact that light that falls on the system's sensors conflates multiple attributes of the physical world. Understanding the computational principles by which natural systems overcome this challenge and generate useful behaviour remains the key objective in neuroscience and machine vision research. In this paper we introduce Mosaic World, an artificial life model that maintains the essential characteristics of natural visual ecologies, and which is populated by virtual agents that - through 'natural' selection - come to resolve stimulus ambiguity by adapting the functional structure of their visual networks according to the statistical structure of their ecological experience. Mosaic World therefore presents us with an important tool for exploring the computational principles by which vision can overcome stimulus ambiguity and usefully guide behaviour.