Weighted KPCA degree of homogeneity amended nonclassical receptive field inhibition model for salient contour extraction in low-light-level image.

Abstract

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.