Multifeature-Based Surround Inhibition Improves Contour Detection in Natural Images
Multifeature-Based Surround Inhibition Improves Contour Detection in Natural Images
复制标题
基于多特征的环绕抑制改进了自然图像中的轮廓检测
DOI：
10.1109/tip.2014.2361210
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发表时间：
2014-12-01
影响因子：
10.6
通讯作者：
Li, Yong-Jie
中科院分区：
文献类型：
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作者：
Yang, Kai-Fu;Li, Chao-Yi;Li, Yong-Jie
为了有效地执行诸如检测轮廓之类的视觉任务，视觉系统通常需要集成多个视觉特征。大量的生理学研究表明，对于猴子和猫的初级视觉皮质(V1)中的大量神经元，当经典感受野(CRF)与其周围环境(即非CRF)之间存在差异时，对于各种局部特征，由经典感受野(CRF)内的刺激引起的神经元反应基本上是被调制的，通常被抑制。V1神经元对中心-周围刺激结构的精致敏感性被认为具有重要的知觉功能，包括轮廓检测。在本文中，我们提出了一种生物激励模型来提高感知显著轮廓检测的性能。主要贡献是基于多特征的中心-环绕框架，其中单个特征的环绕抑制权重(包括方向、亮度和亮度对比度)根据尺度制导策略被组合，然后组合的权重被用来调节神经元的最终环绕抑制。与基于单线索的模型和其他现有方法(特别是其他生物动机模型)的性能进行了比较。结果表明，与使用单一线索的模型相比，组合多个线索可以显著提高轮廓检测的性能。一般而言，至少在灰度自然图像中，亮度和亮度对比度对轮廓提取的具体任务的贡献远远大于方向。
To effectively perform visual tasks like detecting contours, the visual system normally needs to integrate multiple visual features. Sufficient physiological studies have revealed that for a large number of neurons in the primary visual cortex (V1) of monkeys and cats, neuronal responses elicited by the stimuli placed within the classical receptive field (CRF) are substantially modulated, normally inhibited, when difference exists between the CRF and its surround, namely, non-CRF, for various local features. The exquisite sensitivity of V1 neurons to the center-surround stimulus configuration is thought to serve important perceptual functions, including contour detection. In this paper, we propose a biologically motivated model to improve the performance of perceptually salient contour detection. The main contribution is the multifeature-based center-surround framework, in which the surround inhibition weights of individual features, including orientation, luminance, and luminance contrast, are combined according to a scale-guided strategy, and the combined weights are then used to modulate the final surround inhibition of the neurons. The performance was compared with that of single-cue-based models and other existing methods (especially other biologically motivated ones). The results show that combining multiple cues can substantially improve the performance of contour detection compared with the models using single cue. In general, luminance and luminance contrast contribute much more than orientation to the specific task of contour extraction, at least in gray-scale natural images.