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.

Information-processing approaches to visual perception

Abstract: The best ebooks about Information Processing Approaches To Visual Perception that you can get for free here by download this Information Processing Approaches To Visual Perception and save to your desktop. This ebooks is under topic such as information processing and memory: theory and applications art, perception and information processing: an perception through anticipation a behaviour-based perception through anticipation a behaviour-based information processing models: microscopes of the mind 7 perception and cognitive aspects visual analytics two visual systems and two theories of perception: an 3 perception sage pub teaching implications of information processing theory and chapter 8 information processing theory huntsville, tx a survey of perceptual image processing methods human visual and color information processing investigated perception as information-processing springer visual perception university of leicester chapter3 – perception approaches to perception guidelines for identifying visual perceptual problems in time and mind ii information processing perspectives ebook visual perception of objects: an approach to assessment neural processing of visual information under interocular human color and visual information processing investigated capacity limits of information processing in the brain theoretical approaches to vision ՕՑգ chapter 7 outline i studying cognitive development: six a developmental approach: a framework for the development the complexity of information-processing tasks in vision cognitive science: emerging perspectives and approaches unià ̄¥ed visual perception model for context-aware wearable ar preperceptual images, processing time, and perceptual perception and action washington university in st louis a vestibular sensation: probabilistic approaches to bottom-up and top-down processing in visual perception i can a long term perceptual hypothesis affect visual a century of gestalt psychology in visual perception: ii perception and visual cognition university of glasgow modelling perception using image processing algorithms bcs

A Multiscale Retinex for Color Rendition and Dynamic Range Compression

Abstract: The human vision system performs the tasks of dynamic range compression and color constancy almost effortlessly. The same tasks pose a very challenging problem for imaging systems whose dynamic range is restricted by either the dynamic response of film, in case of analog cameras, or by the analog-to-digital converters, in the case of digital cameras. The images thus formed are unable to encompass the wide dynamic range present in most natural scenes (often < 500:1). Whereas the human visual system is quite tolerant to spectral changes in lighting conditions, these strongly affect both the film response for analog cameras and the filter responses for digital cameras, leading to incorrect color formulation in the acquired image. Our multiscale retinex, based in part on Edwin Land''s work on color constancy, provides a fast, simple, and automatic technique for simultaneous dynamic range compression and accurate color rendition. The retinex algorithm is non-linear, and global---output at a point is also a function of its surround---in extent. A comparison with conventional dynamic range compression techniques such as the application of point non-linearities, e.g. log(x,y), and global histogram equalization and/or modification shows that the multiscale retinex simultaneously provides the best dynamic range compression and color rendition. The applications of such an algorithm are many; from medical imaging to remote sensing; and from commercial photography to color transmission.

Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Abstract: Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.

Parallel detection of Kanizsa subjective figures in the human visual system

Abstract: Subjective figures, seen in the absence of luminance gradients (Fig. 1), provide a phenomenal illusion that can be related to the properties of single cells in the visual cortex, offering a rare bridge between brain function and visual awareness. It remains controversial whether subjective figures arise from intelligent cognitive mechanisms, or from lower-level processes in early vision. The cognitive account implies that the perception of subjective figures may require serial attentive processing, whereas on the low-level account they should arise in parallel at earlier visual stages. Physiological evidence apparently fits a low-level account and indicates that some types of subjective contour may be detected earlier than the conventional Kanizsa type. Here we report that even Kanizsa subjective figures can be detected without focal attention at parallel stages of the human visual system.