INTRODUCTION Global illumination is the physically accurate calculation of lighting in an environment. It is computationally expensive for static environments and even more so for dynamic environments. Not only are many images required for an animation, but the calculation involved increases with the presence of moving objects. In static environments, global illumination algorithms can precompute a lighting solution and reuse it whenever the viewpoint changes, but in dynamic environments, any moving object or light potentially affects the illumination of every other object in a scene. To guarantee accuracy, the algorithm has to recompute the entire lighting solution for each frame. This paper describes a perceptually-based technique that can dramatically reduce this computational load. The technique may also be used in image based rendering, geometry level of detail selection, realistic image synthesis, video telephony and video compression. Perceptually-based rendering operat

The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method. 1.

The perceived quality of computer graphics imagery depends on the accuracy of the rendered frames, as well as the capabilities of the human visual system. Fully detailed, high fidelity frames still take many minutes even hours to render on today's computers. The human eye is physically incapable of capturing a moving scene in full detail. We sense image detail only in a 2 # foveal region, relying on rapid eye movements, or saccades, to jump between points of interest. Our brain then reassembles these glimpses into a coherent, but inevitably imperfect, visual percept of the environment. In the process, we literally lose sight of the unimportant details. In this paper, we demonstrate how properties of the human visual system, in particular inattentional blindness, can be exploited to accelerate the rendering of animated sequences by applying a priori knowledge of a viewer's task focus. We show in a controlled experimental setting how human subjects will consistently fail to notice degradations in the quality of image details unrelated to their assigned task, even when these details fall under the viewers' gaze. We then build on these observations to create a perceptual rendering framework that combines predetermined task maps with spatiotemporal contrast sensitivity to guide a progressive animation system which takes full advantage of image-based rendering techniques. We demonstrate this framework with a Radiance ray-tracing implementation that completes its work in a fraction of the normally required time, with few noticeable artifacts for viewers performing the task.

According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when perfomed by a demonstrator. We subsequently demonstrate that such arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.

A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98 % of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.

in dynamic scenes

Existing models of visual attention have provided plausible explanations for many of the standard percepts and illusions and yet all have defied implementations that have led to generic applications. This paper describes a new measure of visual attention and its application to variable resolution compression mechanisms.

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Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood Human Visual System (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eyetracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model.

Attentional selection of an object for recognition is often modeled using all-or-nothing switching of neuronal connection pathways from the attended region of the retinal input to the recognition units.