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Optimal contour integration: When additive algorithms fail
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DOI：10.1016/j.neucom.2005.12.095.png)
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Contour integration is a fundamental computation during image segmentation. Psychophysical evidence shows that contour integration is performed with high precision in widely differing situations. Therefore, the brain requires a reliable algorithm for extracting contours from stimuli. While according to statistics, contour integration is optimal when using a multiplicative algorithm, realistic neural networks employ additive operations. Here we discuss potential drawbacks of additive models. In particular, additive models require a subtle balance of lateral and afferent input for reliable contour detection. Furthermore, they erroneously detect an element belonging to several jittered contours instead of a perfectly aligned and thus more salient contour. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
visual system
neural networks
illusion
association field
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