Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Neuroimage. 2011 Mar 1;55(1):49-66. doi: 10.1016/j.neuroimage.2010.11.067. Epub 2010 Nov 29.
The broad region outside the classical receptive field (CRF) of a neuron in the primary visual cortex (V1), namely non-CRF (nCRF), exerts robust modulatory effects on the responses to visual stimuli presented within the CRF. This modulating effect is mostly suppressive, which plays important roles in visual information processing. One possible role is to extract object contours from disorderly background textures. In this study, a two-scale based contour extraction model, inspired by the inhibitory interactions between CRF and nCRF of V1 neurons, is presented. The kernel idea is that the side and end subregions of nCRF work in different manners, i.e., while the strength of side inhibition is consistently calculated just based on the local features in the side regions at a fine spatial scale, the strength of end inhibition adaptively varies in accordance with the local features in both end and side regions at both fine and coarse scales. Computationally, the end regions exert weaker inhibition on CRF at the locations where a meaningful contour more likely exists in the local texture and stronger inhibition at the locations where the texture elements are mainly stochastic. Our results demonstrate that by introducing such an adaptive mechanism into the model, the non-meaningful texture elements are removed dramatically, and at the same time, the object contours are extracted effectively. Besides the superior performance in contour detection over other inhibition-based models, our model provides a better understanding of the roles of nCRF and has potential applications in computer vision and pattern recognition.
初级视皮层（V1）神经元经典感受野（CRF）之外的广阔区域，即非 CRF（nCRF），对 CRF 内呈现的视觉刺激的反应产生强大的调制作用。这种调制作用大多是抑制性的，在视觉信息处理中起着重要作用。一个可能的作用是从杂乱的背景纹理中提取物体轮廓。在这项研究中，提出了一种基于两尺度的轮廓提取模型，该模型受到 V1 神经元 CRF 和 nCRF 之间抑制性相互作用的启发。其核心思想是 nCRF 的侧区和端区以不同的方式工作，即侧抑制的强度仅根据精细空间尺度上侧区的局部特征一致计算，而端抑制的强度根据精细和粗糙尺度上端区和侧区的局部特征自适应变化。在计算上，在局部纹理中更有可能存在有意义轮廓的位置，端区对 CRF 的抑制作用较弱，而在纹理元素主要是随机的位置，端区对 CRF 的抑制作用较强。我们的结果表明，通过在模型中引入这种自适应机制，可以显著去除无意义的纹理元素，同时有效地提取物体轮廓。除了在轮廓检测方面优于其他基于抑制的模型的性能外，我们的模型还提供了对 nCRF 作用的更好理解，并在计算机视觉和模式识别中有潜在的应用。
