Kang Xiaomei, Kong Qingqun, Zeng Yi, Xu Bo
Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Front Comput Neurosci. 2018 Apr 30;12:28. doi: 10.3389/fncom.2018.00028. eCollection 2018.
Compared to computer vision systems, the human visual system is more fast and accurate. It is well accepted that V1 neurons can well encode contour information. There are plenty of computational models about contour detection based on the mechanism of the V1 neurons. Multiple-cue inhibition operator is one well-known model, which is based on the mechanism of V1 neurons' non-classical receptive fields. However, this model is time-consuming and noisy. To solve these two problems, we propose an improved model which integrates some additional other mechanisms of the primary vision system. Firstly, based on the knowledge that the salient contours only occupy a small portion of the whole image, the prior filtering is introduced to decrease the running time. Secondly, based on the physiological finding that nearby neurons often have highly correlated responses and thus include redundant information, we adopt the uniform samplings to speed up the algorithm. Thirdly, sparse coding is introduced to suppress the unwanted noises. Finally, to validate the performance, we test it on Berkeley Segmentation Data Set. The results show that the improved model can decrease running time as well as keep the accuracy of the contour detection.
与计算机视觉系统相比，人类视觉系统更快且更准确。人们普遍认为V1神经元能够很好地编码轮廓信息。基于V1神经元的机制，有大量关于轮廓检测的计算模型。多线索抑制算子是一种著名的模型，它基于V1神经元非经典感受野的机制。然而，该模型耗时且有噪声。为了解决这两个问题，我们提出了一种改进模型，该模型整合了初级视觉系统的一些其他机制。首先，基于显著轮廓仅占据整个图像一小部分的知识，引入先验滤波以减少运行时间。其次，基于附近神经元通常具有高度相关响应从而包含冗余信息的生理学发现，我们采用均匀采样来加速算法。第三，引入稀疏编码以抑制不必要的噪声。最后，为了验证性能，我们在伯克利分割数据集上对其进行测试。结果表明，改进后的模型可以减少运行时间并保持轮廓检测的准确性。
