====== Significantly different textures: A computational model of pre-attentive texture segmentation ======
==== Ruth Rosenholtz ====
----

===== Abstract =====
Recent human vision research suggests modelling preattentive texture segmentation by taking a set of feature samples from a 
local region on each side of a hypothesized edge, and then performing 
standard statistical tests to determine if the two samples differ significantly 
in their mean or variance.  If the difference is significant at a specified level 
of confidence, a human observer will tend to pre-attentively see a texture 
edge at that location.  I present an algorithm based upon these results, with a 
well specified decision stage and intuitive, easily fit parameters.  Previous 
models of pre-attentive texture segmentation have poorly specified decision 
stages, more unknown free parameters, and in some cases incorrectly model 
human performance.  The algorithm uses heuristics for guessing the 
orientation of a texture edge at a given location, thus improving 
computational efficiency by performing the statistical tests at only one 
orientation for each spatial location.

===== Information =====

---- dataentry pubitem ----
title        : Significantly different textures: A computational model of pre-attentive texture segmentation
authors      : R. Rosenholtz
citation     : Proc. European Conference on Computer Vision, D. Vernon (Ed.), Springer Ver lag, LNCS 1843, Dublin, Ireland, pp. 197-211
shortcite    : ECCV
year         : 2000
created_dt   : 2000-01-01
summary_page : publications:textureeccv00
keyword_tags : rosenholtz
www_url      :
pdf_url      : http://web.mit.edu/rruth/www/Papers/TextureSegmentation.pdf
pageid       : textureeccv00
type         : publication
---