Impact of Ultra High Definition on Visual Attention

Ultra high definition (UHD) TV is rapidly replacing high definition (HD) TV but little is known of its effects on human visual attention. However, a clear understanding of this effect is important, since accurate models, evaluation methodologies, and metrics for visual attention are essential in many areas, including image and video compression, camera and displays manufacturing, artistic content creation, and advertisement. In this paper, we address this problem by creating a dataset of UHD resolution images with corresponding eye-tracking data, and we show that there is a statistically significant difference between viewing strategies when watching UHD and HD contents. Furthermore, by evaluating five representative computational models of visual saliency, we demonstrate the decrease in models' accuracies on UHD contents when compared to HD contents. Therefore, to improve the accuracy of computational models for higher resolutions, we propose a segmentation-based resolution-adaptive weighting scheme. Our approach demonstrates that taking into account information about resolution of the images improves the performance of computational models.

Ultra-Eye: UHD and HD images eye tracking dataset

Touradj Ebrahimi, Philippe Hanhart, Hiromi Nemoto

Due to the recent advances in ultra high definition (UHD) displays, UHD TV may replace HD TV in a near future. However, little is known about the effect of UHD content on human visual perception, specifically, on human visual attention, viewing strategies, and visual saliency. To help studying these properties of the human visual system and their dynamics when HD content is replaced with UHD content, a publicly accessible dataset is proposed, which is composed of 41 4K UHD and HD images with corresponding eye tracking information. The eye tracking information includes the fixation points and fixation density maps measured during extensive subjective experiments. In this paper, we describe the dataset in details, including the strategy for content selection, the eye-tracking experiments, and the computation of the fixation density maps.

2014

Visual attention in LDR and HDR images

Touradj Ebrahimi, Philippe Hanhart, Hiromi Nemoto

Recent advances in high dynamic range (HDR) capturing and display technologies attracted a lot of interest to HDR imaging. Many issues that are considered as being resolved for conventional low dynamic range (LDR) images pose new challenges in HDR context. One of such issues is human visual attention, which has important applications in image and video compression, camera and displays manufacturing, artistic content creation, and advertisement. However, the impact of HDR imaging on visual attention and on the performance of saliency models is not well understood. Therefore, in this paper, we address this problem by creating a publicly available dataset of 46 HDR and corresponding LDR images with varying regions of interests, scenes, and dynamic range. We conducted eye tracking experiments and obtained fixation density maps, which demonstrate a significant difference in the way HDR and LDR capture attention of the observers.

2015

Visuomotor Coordination in Reach-To-Grasp Tasks: From Humans to Humanoids and Vice Versa

Luka Lukic

Understanding the principles involved in visually-based coordinated motor control is one of the most fundamental and most intriguing research problems across a number of areas, including psychology, neuroscience, computer vision and robotics. Not very much is known regarding computational functions that the central nervous system performs in order to provide a set of requirements for visually-driven reaching and grasping. Additionally, in spite of several decades of advances in the field, the abilities of humanoids to perform similar tasks are by far modest when needed to operate in unstructured and dynamically changing environments. More specifically, our first focus is understanding the principles involved in human visuomotor coordination. Not many behavioral studies considered visuomotor coordination in natural, unrestricted, head-free movements in complex scenarios such as obstacle avoidance. To fill this gap, we provide an assessment of visuomotor coordination when humans perform prehensile tasks with obstacle avoidance, an issue that has received far less attention. Namely, we quantify the relationships between the gaze and arm-hand systems, so as to inform robotic models, and we investigate how the presence of an obstacle modulates this pattern of correlations. Second, to complement these observations, we provide a robotic model of visuomotor coordination, with and without the presence of obstacles in the workspace. The parameters of the controller are solely estimated by using the human motion capture data from our human study. This controller has a number of interesting properties. It provides an efficient way to control the gaze, arm and hand movements in a stable and coordinated manner. When facing perturbations while reaching and grasping, our controller adapts its behavior almost instantly, while preserving coordination between the gaze, arm, and hand. In the third part of the thesis, we study the neuroscientific literature of the primates. We here stress the view that the cerebellum uses the cortical reference frame representation. The cerebellum by taking into account this representation performs closed-loop programming of multi-joint movements and movement synchronization between the eye-head system, arm and hand. Based on this investigation, we propose a functional architecture of the cerebellar-cortical involvement. We derive a number of improvements of our visuomotor controller for obstacle-free reaching and grasping. Because this model is devised by carefully taking into account the neuroscientific evidence, we are able to provide a number of testable predictions about the functions of the central nervous system in visuomotor coordination. Finally, we tackle the flow of the visuomotor coordination in the direction from the arm-hand system to the visual system. We develop two models of motor-primed attention for humanoid robots. Motor-priming of attention is a mechanism that implements prioritizing of visual processing with respect to motor-relevant parts of the visual field. Recent studies in humans and monkeys have shown that visual attention supporting natural behavior is not exclusively defined in terms of visual saliency in color or texture cues, rather the reachable space and motor plans present the predominant source of this attentional modulation. Here, we show that motor-priming of visual attention can be used to efficiently distribute robot's computational resources devoted to visual processing.

EPFL2015

Visuomotor Coordination in Reach-To-Grasp Tasks: From Humans to Humanoids and Vice Versa

Luka Lukic

EPFL2015