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Contour detection improved by context-adaptive surround suppression.

ABSTRACT: Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

SUBMITTER: Sang Q

PROVIDER: S-EPMC5536361 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Contour detection improved by context-adaptive surround suppression.

Sang Qiang Q Cai Biao B Chen Hao H

PloS one 20170731 7

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control ...[more]

PMID: 28759589

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