Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

A novel technique for robust image watermarking in the DCT domain

Abstract: In this paper, we present a novel algorithm for embedding a watermark into a still host image in the DCT domain. Unlike the traditional techniques, we embed a binary image as a watermark into the dc components of the DCT coefficients of 8/spl times/8 blocks. In the proposed algorithm, we incorporate the feature of texture masking and luminance masking of the human visual system into watermarking. The algorithm recovers the watermark without any reference to the original image. Experimental results demonstrate that the visible watermarks embedded with the proposed algorithm are robust to various attacks.

Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning

Abstract: Neural decoding, which aims to predict external visual stimuli information from evoked brain activities, plays an important role in understanding human visual system. Many existing methods are based on linear models, and most of them only focus on either the brain activity pattern classification or visual stimuli identification. Accurate reconstruction of the perceived images from the measured human brain activities still remains challenging. In this paper, we propose a novel deep generative multiview model for the accurate visual image reconstruction from the human brain activities measured by functional magnetic resonance imaging (fMRI). Specifically, we model the statistical relationships between the two views (i.e., the visual stimuli and the evoked fMRI) by using two view-specific generators with a shared latent space. On the one hand, we adopt a deep neural network architecture for visual image generation, which mimics the stages of human visual processing. On the other hand, we design a sparse Bayesian linear model for fMRI activity generation, which can effectively capture voxel correlations, suppress data noise, and avoid overfitting. Furthermore, we devise an efficient mean-field variational inference method to train the proposed model. The proposed method can accurately reconstruct visual images via Bayesian inference. In particular, we exploit a posterior regularization technique in the Bayesian inference to regularize the model posterior. The quantitative and qualitative evaluations conducted on multiple fMRI data sets demonstrate the proposed method can reconstruct visual images more accurately than the state of the art.

Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning

Abstract: One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.