Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
J Neurosci Methods. 2022 Mar 1;369:109423. doi: 10.1016/j.jneumeth.2021.109423. Epub 2021 Nov 24.
Given energy metabolism, visual information degradation plays an essential role in the retina- lateral geniculate nucleus (LGN)-primary visual cortex (V1)-secondary visual cortex (V2) pathway, and is a pivotal issue for visual information processing. Degradation helps the visual nervous system conserve brain energy and efficiently perceive the real world even though a small fraction of visual information reaches the early visual areas. The coding of contour features (edge and corner) is achieved in the retina-LGN-V1-V2 pathway. Based on the above, we proposed a contour detection model based on degradation (CDMD).
Inspired by pupillary light reflex regulation, we took into consideration the novel approach of the hue-saturation-value (HSV) module for color encoding to meet the subtle chromaticity change rather than using the traditional red-green-blue (RGB) module, following the mechanisms of dark (DA) and light (LA) adaptation processes in photoreceptors. Meanwhile, the degradation mechanism was introduced as a novel strategy focusing only on the essential information to detect contour features, mimicking contour detection by visual perception under the restriction of axons in each optic nerve biologically. Ultimately, we employed the feedback mechanism achieving the optimal HSV value for each pixel of the experimental datasets.
We used the publicly available Berkeley Segmentation Data Set 500 (BSDS500) to assess the effectiveness of our CDMD model, introduced the F-measure to evaluate the results. The F-measure score was 0.65, achieved by our model. Moreover, CDMD with HSV has a better sensitivity for subtle chromaticity changes than CDMD with RGB.
Experimental results demonstrated that our CDMD model, which functions close to the real visual system, achieved a more competitive performance with low computational cost than some state-of-the-art non-deep-learning and biologically inspired models. Compared with deep-learning-based algorithms, our model contains fewer parameters and computation time, does not require additional visual features, as well as an extra training process.
Our proposed CDMD model is a novel approach for contour detection, which mimics the cognitive function of contour detection in early visual areas, and realizes a competitive performance in image processing. It contributes to bridging the gap between the biological visual system and computer vision.
鉴于能量代谢，视觉信息的退化在视网膜-外侧膝状体核（LGN）-初级视觉皮层（V1）-次级视觉皮层（V2）通路中起着至关重要的作用，是视觉信息处理的关键问题。退化有助于视觉神经系统节省大脑能量，并有效地感知现实世界，尽管只有一小部分视觉信息到达早期视觉区域。轮廓特征（边缘和拐角）的编码是在视网膜-LGN-V1-V2 通路中完成的。基于上述内容，我们提出了一种基于退化的轮廓检测模型（CDMD）。
受瞳孔光反射调节的启发，我们考虑了颜色编码的色调-饱和度-值（HSV）模块的新颖方法，以满足细微的色度变化，而不是使用传统的红绿蓝（RGB）模块，遵循光感受器中暗（DA）和亮（LA）适应过程的机制。同时，引入了退化机制作为一种新策略，仅关注检测轮廓特征的基本信息，模拟视觉感知在视神经中轴突的限制下进行轮廓检测。最终，我们采用反馈机制为实验数据集的每个像素实现最佳的 HSV 值。
我们使用公开可用的伯克利分割数据集 500（BSDS500）来评估我们的 CDMD 模型的有效性，引入了 F 度量来评估结果。我们的模型的 F 度量得分为 0.65。此外，与 RGB 相比，HSV 的 CDMD 对细微的色度变化具有更好的敏感性。
实验结果表明，我们的 CDMD 模型更接近真实视觉系统，与一些最先进的非深度学习和生物启发模型相比，具有更低的计算成本和更具竞争力的性能。与基于深度学习的算法相比，我们的模型包含更少的参数和计算时间，不需要额外的视觉特征，也不需要额外的训练过程。
我们提出的 CDMD 模型是一种新的轮廓检测方法，模拟了早期视觉区域中轮廓检测的认知功能，并在图像处理中实现了具有竞争力的性能。它有助于弥合生物视觉系统和计算机视觉之间的差距。
