IEEE Trans Image Process. 2014 Jun;23(6):2732-43. doi: 10.1109/TIP.2014.2317987.
The stimulus response of the classical receptive field (CRF) of neuron in primary visual cortex is affected by its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition, which depends primarily on the correlation of both visual stimulations. The theory of periphery and center interaction with visual characteristics can be applied in night vision information processing. In this paper, a weighted kernel principal component analysis (WKPCA) degree of homogeneity (DH) amended inhibition model inspired by visual perceptual mechanisms is proposed to extract salient contour from complex natural scene in low-light-level image. The core idea is that multifeature analysis can recognize the homogeneity in modulation coverage effectively. Computationally, a novel WKPCA algorithm is presented to eliminate outliers and anomalous distribution in CRF and accomplish principal component analysis precisely. On this basis, a new concept and computational procedure for DH is defined to evaluate the dissimilarity between periphery and center comprehensively. Through amending the inhibition from nCRF to CRF by DH, our model can reduce the interference of noises, suppress details, and textures in homogeneous regions accurately. It helps to further avoid mutual suppression among inhomogeneous regions and contour elements. This paper provides an improved computational visual model with high-performance for contour detection from cluttered natural scene in night vision image.
初级视皮层神经元的经典感受野（CRF）的刺激反应受到其周围区域（即非 CRF（nCRF））的影响。这种调制会产生抑制作用，主要取决于两种视觉刺激的相关性。具有视觉特征的外围和中心相互作用的理论可应用于夜视信息处理。在本文中，我们受视觉感知机制的启发，提出了一种基于加权核主成分分析（WKPCA）的同质性（DH）修正抑制模型，以从低光照级图像的复杂自然场景中提取显著轮廓。其核心思想是多特征分析可以有效地识别调制覆盖中的同质性。在计算上，我们提出了一种新颖的 WKPCA 算法来消除 CRF 中的异常值和异常分布，并精确地完成主成分分析。在此基础上，我们定义了一个新的概念和计算过程 DH，以全面评估外围和中心之间的差异。通过用 DH 修正 nCRF 到 CRF 的抑制作用，我们的模型可以准确地减少同质区域中的噪声、细节和纹理的干扰。它有助于进一步避免非同质区域和轮廓元素之间的相互抑制。本文为从低光照级图像的杂乱自然场景中进行轮廓检测提供了一种具有高性能的改进计算视觉模型。
