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Local structural information is crucially important for human visual system to perceive natural scenes. Recent years, a variety of local image structure description methods have been proposed for the artificial modeling of visual perception. Although existing local image structure descriptors have shown successful performances, one general limitation is their numerical instability caused by ignoring the information of the spatial correlation of local orientation. In this paper, we propose a local image structure descriptor by modeling the anisotropic mechanism in the primary visual cortex. In particular, the pixel-wise anisotropy values of a given image are calculated by pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy. Then the excitatory/inhibitory interactions among visual neurons in the local receptive field are modeled by measuring the similarities between their anisotropies. By mapping visual neurons to image pixels, the correlation between a central pixel and its local neighbors can be represented by a binary pattern which is named as local anisotropic pattern (LAP). Experimental results on texture classification verified that the proposed LAP has satisfactory texture classification accuracy, rotation invariance, and noise robustness; experimental results on no-reference image quality assessment demonstrated that the proposed LAP achieves state-of-the-art performance in objective evaluation of the perceptual quality of natural image, and this reflects that LAP can accurately represent the degradation of local image structure. [ABSTRACT FROM AUTHOR]

In this paper, a high sensitivity optical pressure sensor based on a graphene/molybdenum disulfide (MoS 2 ) composite film is proposed. The sensor is composed of a polydimethylsiloxane (PDMS) pyramid structure, graphene/MoS 2 composite film, and lithium niobate waveguide. The pressure deforms the PDMS pyramid structure, which leads to the change of the refractive index of the graphene/MoS 2 composite film, and finally be detectable sensitively by the variation of the interference spectrum. Experiments have been carried out using our sensor prototype, and the sensitivity is up to 575.233 nm/kPa in the pressure range of 0 kPa-0.123 kPa, which is much higher than that of typical optical pressure sensors. This shows the advantages of high sensitivity optical pressure sensors based on the graphene/MoS 2 composite film, which is expected to be applied in highly sensitive pressure detection environments.