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Journal of Vision: Special Issue: "The Modelfest dataset: Analysis and modeling"
The Modelfest dataset: Analysis and modeling
Over the past 40 years psychophysical and physiological studies have
revealed the multi-channel, parallel processing structure of the human
visual system. This enhanced understanding has been accompanied by
development of numerous models of spatial vision.
Unfortunately, direct comparisons of these models on the same data sets
have rarely been made. Instead, researchers have generally tested their
own model with their own data. Alternatively, interested researchers
trying to make comparisons have struggled to reproduce models from
incomplete published descriptions. At the 1997 meeting of the Optical
Society of America, a workshop was organized to address this problem.
This workshop ultimately gave rise to the ModelFest group: an
international consortium of vision researchers focused on the goal of
providing a public database of stimuli and psychophysical thresholds for
testing and developing models of human spatial vision.
Through extensive discussion, this group eventually arrived at consensus
on a set of 43 stimuli, as well as on methods of data collection. The
stimuli were selected to both calibrate candidate models, and to test
them. The first results were submitted to the group in 1999. All the
ModelFest stimuli and data are now available on the internet at
http://neurometrics.com/projects/Modelfest/IndexModelfest.htm and at
http://vision.arc.nasa.gov/modelfest/. The present database includes 43
stimuli, two thirds of which are Gabor patterns, singly or combined in
different ways. The remaining patterns include a line, edge,
checkerboard, sample of spatial noise, natural scene and other patterns.
The Modelfest approach offers a dramatic change from how vision modeling
has proceeded in the past. By using a common database of stimuli and
psychophysical thresholds, researchers have a simple way of comparing
model performance and thereby learning from the innovations and
limitations of each model.
To disseminate recent approaches to vision modeling and to promote the
idea of comparing model performance on a common dataset, we invite
researchers to contribute to a Special Issue of the Journal of Vision.
This special issue will focus on applying vision models to the ModelFest
dataset and include related topics on modeling spatial vision, including
but not limited to:
* Summary and review of the ModelFest dataset
* Statistical analysis of the ModelFest dataset
* Dataset limitations, stimuli that should have been included
* Application of vision models to the Modelfest data set
* Statistical questions regarding comparison of models
* Comparison of the Modelfest data to results from the literature.
Guest Editors:
Thom Carney
University of California at Berkeley, CA, and Neurometrics Institute,
Oakland, CA
thom@neurometrics.com <mailto:thom@neurometrics.com>
Christopher W. Tyler
Smith-Kettlewell Eye Research Institute, San Francisco CA
cwt@mail.ski.org <mailto:cwt@mail.ski.org> www.ski.org/cwt
<http://www.ski.org/cwt>
Deadline for submissions:
December 1, 2004
Target publication date:
May 1, 2004
Journal of Vision encourages the use of images, color, movies,
hyperlinks, and other digital enhancements. To submit a paper to this
special issue please follow the Instructions for Authors.
<http://journalofvision.org/info/info_for_authors.aspx>
Over the past 40 years psychophysical and physiological studies have
revealed the multi-channel, parallel processing structure of the human
visual system. This enhanced understanding has been accompanied by
development of numerous models of spatial vision.
Unfortunately, direct comparisons of these models on the same data sets
have rarely been made. Instead, researchers have generally tested their
own model with their own data. Alternatively, interested researchers
trying to make comparisons have struggled to reproduce models from
incomplete published descriptions. At the 1997 meeting of the Optical
Society of America, a workshop was organized to address this problem.
This workshop ultimately gave rise to the ModelFest group: an
international consortium of vision researchers focused on the goal of
providing a public database of stimuli and psychophysical thresholds for
testing and developing models of human spatial vision.
Through extensive discussion, this group eventually arrived at consensus
on a set of 43 stimuli, as well as on methods of data collection. The
stimuli were selected to both calibrate candidate models, and to test
them. The first results were submitted to the group in 1999. All the
ModelFest stimuli and data are now available on the internet at
http://neurometrics.com/projects/Modelfest/IndexModelfest.htm and at
http://vision.arc.nasa.gov/modelfest/. The present database includes 43
stimuli, two thirds of which are Gabor patterns, singly or combined in
different ways. The remaining patterns include a line, edge,
checkerboard, sample of spatial noise, natural scene and other patterns.
The Modelfest approach offers a dramatic change from how vision modeling
has proceeded in the past. By using a common database of stimuli and
psychophysical thresholds, researchers have a simple way of comparing
model performance and thereby learning from the innovations and
limitations of each model.
To disseminate recent approaches to vision modeling and to promote the
idea of comparing model performance on a common dataset, we invite
researchers to contribute to a Special Issue of the Journal of Vision.
This special issue will focus on applying vision models to the ModelFest
dataset and include related topics on modeling spatial vision, including
but not limited to:
* Summary and review of the ModelFest dataset
* Statistical analysis of the ModelFest dataset
* Dataset limitations, stimuli that should have been included
* Application of vision models to the Modelfest data set
* Statistical questions regarding comparison of models
* Comparison of the Modelfest data to results from the literature.
Guest Editors:
Thom Carney
University of California at Berkeley, CA, and Neurometrics Institute,
Oakland, CA
thom@neurometrics.com <mailto:thom@neurometrics.com>
Christopher W. Tyler
Smith-Kettlewell Eye Research Institute, San Francisco CA
cwt@mail.ski.org <mailto:cwt@mail.ski.org> www.ski.org/cwt
<http://www.ski.org/cwt>
Deadline for submissions:
December 1, 2004
Target publication date:
May 1, 2004
Journal of Vision encourages the use of images, color, movies,
hyperlinks, and other digital enhancements. To submit a paper to this
special issue please follow the Instructions for Authors.
<http://journalofvision.org/info/info_for_authors.aspx>
