Topic
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.
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01 Jan 1997
TL;DR: This tutorial paper describes the log-polar mapping of the eye’s retinal image and its main properties and describes the motivation behind the excitation of the cortex.
Abstract: One interesting feature of the human visual system is the topological transformation of the retinal image into its cortical projection. The excitation of the cortex can be approximated by a log-polar mapping of the eye’s retinal image. In this tutorial paper we describe the log-polar mapping and its main properties.
54 citations
TL;DR: In this article, a spatial filtering method is proposed to project the data into a low-dimensional space in which the trial-to-trial spectral covariance is maximized, and the resulting technique recovers physiologically plausible components (i.e., the recovered topographies match the lead fields of the underlying sources).
54 citations
26 Jul 2010
TL;DR: This work proposes a novel method applied to moving images that takes into account the human visual system and leads to an improved perception of such details and evaluates the resolution enhancement in a perceptual study that shows that significant improvements can be achieved both for computer generated images and photographs.
Abstract: Limited spatial resolution of current displays makes the depiction of very fine spatial details difficult. This work proposes a novel method applied to moving images that takes into account the human visual system and leads to an improved perception of such details. To this end, we display images rapidly varying over time along a given trajectory on a high refresh rate display. Due to the retinal integration time the information is fused and yields apparent super-resolution pixels on a conventional-resolution display. We discuss how to find optimal temporal pixel variations based on linear eye-movement and image content and extend our solution to arbitrary trajectories. This step involves an efficient method to predict and successfully treat potentially visible flickering. Finally, we evaluate the resolution enhancement in a perceptual study that shows that significant improvements can be achieved both for computer generated images and photographs.
54 citations
TL;DR: The visual system combines the light-from-above prior with visual lighting cues using an efficient statistical strategy that assigns a weight to the prior and to the cues and finds a maximum-likelihood lighting direction estimate that is a compromise between the two.
Abstract: Every biological or artificial visual system faces the problem that images are highly ambiguous, in the sense that every image depicts an infinite number of possible 3D arrangements of shapes, surface colors, and light sources. When estimating 3D shape from shading, the human visual system partly resolves this ambiguity by relying on the light-from-above prior, an assumption that light comes from overhead. However, light comes from overhead only on average, and most images contain visual information that contradicts the light-from-above prior, such as shadows indicating oblique lighting. How does the human visual system perceive 3D shape when there are contradictions between what it assumes and what it sees? Here we show that the visual system combines the light-from-above prior with visual lighting cues using an efficient statistical strategy that assigns a weight to the prior and to the cues and finds a maximum-likelihood lighting direction estimate that is a compromise between the two. The prior receives surprisingly little weight and can be overridden by lighting cues that are barely perceptible. Thus, the light-from-above prior plays a much more limited role in shape perception than previously thought, and instead human vision relies heavily on lighting cues to recover 3D shape. These findings also support the notion that the visual system efficiently integrates priors with cues to solve the difficult problem of recovering 3D shape from 2D images.
54 citations
01 Jan 2019
TL;DR: The main purpose of this paper is to review the various existing methods developed for detecting the image forgery, and a categorization of various forgery detection techniques has been presented.
Abstract: In this age of digitization, digital images are used as a prominent carrier of visual information. Images are becoming increasingly ubiquitous in everyday life. Unprecedented involvement of digital images can be seen in various paramount fields like medical science, journalism, sports, criminal investigation, image forensic, etc., where authenticity of image is of vital importance. Various tools are available free of cost or with a negligible amount of cost for manipulating images. Some tools can manipulate images to such an extent that it becomes impossible to discriminate by human visual system that image is forged or genuine. Hence, image forgery detection is a challenging area of research. It is evident that good quality work has been carried out in the past decade in the field of image forgery detection. However, there is still a need to pay much attention in this field, as image manipulation tools are becoming more and more sophisticated. The main purpose of this paper is to review the various existing methods developed for detecting the image forgery. A categorization of various forgery detection techniques has been presented in the paper.
54 citations