%0 Conference Paper %1 johnson:1994:EVR %A Johnson, Michael Patrick %A Maes, Pattie %A Darrell, Trevor %B ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems %C MIT, Cambridge, MA, USA %D 1994 %E Brooks, Rodney A. %E Maes, Pattie %I MIT Press %K algorithms, genetic programming %P 198--209 %T Evolving Visual Routines %U http://citeseer.ist.psu.edu/402594.html %X 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.

@inproceedings{johnson:1994:EVR, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {MIT, Cambridge, MA, USA}, author = {Johnson, Michael Patrick and Maes, Pattie and Darrell, Trevor}, biburl = {https://www.bibsonomy.org/bibtex/2d6cfb6dee1ac4ef65c70bee3079df4e2/brazovayeye}, booktitle = {ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems}, editor = {Brooks, Rodney A. and Maes, Pattie}, interhash = {796f1792a8517c3313186869cedd5d02}, intrahash = {d6cfb6dee1ac4ef65c70bee3079df4e2}, keywords = {algorithms, genetic programming}, month = {6-8 July}, notes = {alife-4 See also \cite{johnson:1994:EVRAL}}, pages = {198--209}, publisher = {MIT Press}, timestamp = {2008-06-19T17:42:30.000+0200}, title = {Evolving Visual Routines}, url = {http://citeseer.ist.psu.edu/402594.html}, year = 1994 }