Evolving Visual Routines

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@Article{johnson:1994:EVRAL,

• author = "Michael Patrick Johnson and Pattie Maes and Trevor Darrell",
• title = "Evolving Visual Routines",
• journal = "Artificial Life",
• year = "1994",
• volume = "1",
• number = "4",
• pages = "373--389",
• month = "summer",
• keywords = "genetic algorithms, genetic programming, active vision, visual routines",
• DOI = "doi:10.1162/artl.1994.1.4.373",
• size = "17 pages",
• abstract = "Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1,12]. Some of these researchers have argued that in natural living systems these researchers have argued that in natural selection [11]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g.,[3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93percent of the test images, which is better than any algorithm we were able to write by hand.",
• notes = "Extension of \cite{johnson:1994:EVR}",

}

Genetic Programming entries for Michael Patrick Johnson Pattie Maes Trevor Darrell

Citations