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Consistent and robust determination of border ownership based on asymmetric surrounding contrast
- Source :
- Neural networks : the official journal of the International Neural Network Society. 33
- Publication Year :
- 2010
-
Abstract
- Determination of the figure region in an image is a fundamental step toward surface construction, shape coding, and object representation. Localized, asymmetric surround modulation, reported neurophysiologically in early-to-intermediate-level visual areas, has been proposed as a mechanism for figure-ground segregation. We investigated, computationally, whether such surround modulation is capable of yielding consistent and robust determination of figure side for various stimuli. Our surround modulation model showed a surprisingly high consistency among pseudorandom block stimuli, with greater consistency for stimuli that yielded higher accuracy of, and shorter reaction times in, human perception. Our analyses revealed that the localized, asymmetric organization of surrounds is crucial in the detection of the contrast imbalance that leads to the determination of the direction of figure with respect to the border. The model also exhibited robustness for gray-scaled natural images, with a mean correct rate of 67%, which was similar to that of figure-side determination in human perception through a small window and of machine-vision algorithms based on local processing. These results suggest a crucial role of surround modulation in the local processing of figure-ground segregation.
- Subjects :
- business.industry
Cognitive Neuroscience
media_common.quotation_subject
Models, Neurological
Pattern recognition
Pattern Recognition, Automated
Contrast Sensitivity
Random Allocation
Artificial Intelligence
Consistency (statistics)
Robustness (computer science)
Receptive field
Perception
Modulation (music)
Shape coding
Contrast (vision)
Humans
Computer vision
Artificial intelligence
Representation (mathematics)
business
Photic Stimulation
Mathematics
media_common
Subjects
Details
- ISSN :
- 18792782
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Neural networks : the official journal of the International Neural Network Society
- Accession number :
- edsair.doi.dedup.....9b3cf2d70739046c0a82eadc7347b441