- author = "Michael Patrick Johnson and Pattie Maes and Trevor Darrell",
- title = "Evolving Visual Routines",
- booktitle = "ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems",
- year = "1994",
- editor = "Rodney A. Brooks and Pattie Maes",
- pages = "198--209",
- address = "MIT, Cambridge, MA, USA",
- month = "6-8 " # jul,
- publisher = "MIT Press",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://pubs.media.mit.edu/pubs/papers/alife-iv.ps.gz",
-
URL = "
http://citeseer.ist.psu.edu/402594.html",
- abstract = "Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [Marr]. Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection of task-specific, task-driven visual routines [Aloimonos, Ullman]. Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [ramachandran]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g. [Chapman]). In this paper we propose an alternative approach in which visual routines for simple tasks are evolved 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 [Koza]. The results obtained are promising: the evolved routines are able to correctly classify up to 93% of the images, which is better than the best algorithm we were able to write by hand.",
-
notes = "alife-4
See also \cite{johnson:1994:EVRAL}",
