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.

Edge detection of colour image based on quaternion fractional differential

Abstract: According to the development of the real fractional differential and its applications in the modern signal processing, the authors extend it to quaternion body and put forward a new concept: quaternion fractional differential (QFD), and apply it to edge detection of colour image. This method is called edge detection based on QFD. Simulation experiments indicate that this method has special advantages. Furthermore, the authors give an indicator to evaluate the effectiveness of different edge filters. Comparing with Sobel and mix edges of real fractional differential to every channels of colour image, they discover that QFD has fewer false negatives in the textured regions and is also better at detecting edges that are partially defined by texture, which means the authors can obtain much better results in the interesting regions using QFD and is more consistent with the characteristics of human visual system.

Modeling human color perception under extended luminance levels

Abstract: Display technology is advancing quickly with peak luminance increasing significantly, enabling high-dynamic-range displays. However, perceptual color appearance under extended luminance levels has not been studied, mainly due to the unavailability of psychophysical data. Therefore, we conduct a psychophysical study in order to acquire appearance data for many different luminance levels (up to 16,860 cd/m2) covering most of the dynamic range of the human visual system. These experimental data allow us to quantify human color perception under extended luminance levels, yielding a generalized color appearance model. Our proposed appearance model is efficient, accurate and invertible. It can be used to adapt the tone and color of images to different dynamic ranges for cross-media reproduction while maintaining appearance that is close to human perception.

Steady‐state visual evoked potentials as a research tool in social affective neuroscience

Abstract: Like many other primates, humans place a high premium on social information transmission and processing. One important aspect of this information concerns the emotional state of other individuals, conveyed by distinct visual cues such as facial expressions, overt actions, or by cues extracted from the situational context. A rich body of theoretical and empirical work has demonstrated that these socioemotional cues are processed by the human visual system in a prioritized fashion, in the service of optimizing social behavior. Furthermore, socioemotional perception is highly dependent on situational contexts and previous experience. Here, we review current issues in this area of research and discuss the utility of the steady-state visual evoked potential (ssVEP) technique for addressing key empirical questions. Methodological advantages and caveats are discussed with particular regard to quantifying time-varying competition among multiple perceptual objects, trial-by-trial analysis of visual cortical activation, functional connectivity, and the control of low-level stimulus features. Studies on facial expression and emotional scene processing are summarized, with an emphasis on viewing faces and other social cues in emotional contexts, or when competing with each other. Further, because the ssVEP technique can be readily accommodated to studying the viewing of complex scenes with multiple elements, it enables researchers to advance theoretical models of socioemotional perception, based on complex, quasinaturalistic viewing situations.

Structural Similarity Based Image Quality Assessment

Abstract: This chapter presents structural similarity as an alternative design philosophy for objective image quality assessment methods. It discusses the motivation, the general idea, and a specific structural similarity (SSIM) index algorithm of the structural similarity-based image quality assessment method. Many image quality assessment algorithms have been shown to behave consistently when applied to distorted images created from the same original image, using the same type of distortions. The SSIM indexing algorithm is quite encouraging not only because it achieves good quality prediction accuracy in the current tests, but also because of its simple formulation and low complexity implementation. The principal hypothesis of structural similarity based image quality assessment is that the human visual system is highly adapted to extract structural information from the visual field, and therefore a measurement of structural similarity should provide a good approximation to perceived image quality.

How sensitive is the human visual system to the local statistics of natural images

Abstract: A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.