Olfaction and Selective Rendering

Accurate simulation of all the senses in virtual environments is a computationally expensive task. Visual saliency models have been used to improve computational performance for rendered content, but this is insufficient for multi‐modal environments. This paper considers cross‐modal perception and, in particular, if and how olfaction affects visual attention. Two experiments are presented in this paper. Firstly, eye tracking is gathered from a number of participants to gain an impression about where and how they view virtual objects when smell is introduced compared to an odourless condition. Based on the results of this experiment a new type of saliency map in a selective‐rendering pipeline is presented. A second experiment validates this approach, and demonstrates that participants rank images as better quality, when compared to a reference, for the same rendering budget.     

Attention as an input modality for Post-WIMP interfaces using the viGaze eye tracking framework

Eye tracking is one of the most prominent modalities to track user attention while interacting with computational devices. Today, most of the current eye tracking frameworks focus on tracking the user gaze during website browsing or while performing other tasks and interactions with a digital device. Most frameworks have in common that they do not exploit gaze as an input modality. In this paper we describe the realization of a framework named viGaze. Its main goal is to provide an easy to use framework to exploit the use of eye gaze as an input modality in various contexts. Therefore it provides features to explore explicit and implicit interactions in complex virtual environments by using the eye gaze of a user for various interactions. The viGaze framework is flexible and can be easily extended to incorporate other input modalities typically used in Post-WIMP interfaces such as gesture or foot input. In this paper we describe the key components of our viGaze framework and additionally describe a user study that was conducted to test the framework. The user study took place in a virtual retail environment, which provides a challenging pervasive environment and contains complex interactions that can be supported by gaze. The participants performed two gaze-based interactions with products on virtual shelves and started an interaction cycle between the products and an advertisement monitor placed on the shelf. We demonstrate how gaze can be used in Post-WIMP interfaces to steer the attention of users to certain components of the system. We conclude by discussing the advantages provided through the viGaze framework and highlighting the potentials of gaze-based interaction.     

Using a Visual Attention Model to Improve Gaze Tracking Systems in Interactive 3D Applications

This paper introduces the use of a visual attention model to improve the accuracy of gaze tracking systems. Visual attention models simulate the selective attention part of the human visual system. For instance, in a bottom‐up approach, a saliency map is defined for the image and gives an attention weight to every pixel of the image as a function of its colour, edge or intensity. Our algorithm uses an uncertainty window, defined by the gaze tracker accuracy, and located around the gaze point given by the tracker. Then, using a visual attention model, it searches for the most salient points, or objects, located inside this uncertainty window, and determines a novel, and hopefully, better gaze point. This combination of a gaze tracker together with a visual attention model is considered as the main contribution of the paper. We demonstrate the promising results of our method by presenting two experiments conducted in two different contexts: (1) a free exploration of a visually rich 3D virtual environment without a specific task, and (2) a video game based on gaze tracking involving a selection task. Our approach can be used to improve real‐time gaze tracking systems in many interactive 3D applications such as video games or virtual reality applications. The use of a visual attention model can be adapted to any gaze tracker and the visual attention model can also be adapted to the application in which it is used.     

IMAF: in situ indoor modeling and annotation framework on mobile phones

The widespread use of smart phones with GPS and orientation sensors opens up new possibilities for location-based annotations in outdoor environments. However, a completely different approach is required for indoors. In this study, we introduce IMAF, a novel indoor modeling and annotation framework on a mobile phone. The framework produces a 3D room model in situ with five selections from user without prior knowledge on actual geometry distance or additional apparatus. Using the framework, non-experts can easily capture room dimensions and annotate locations and objects within the room for linking virtual information to the real space represented by an approximated box. For registering 3D room model to the real space, an hybrid method of visual tracking and device sensors obtains accurate orientation tracking result and still achieves interactive frame-rates for real-time applications on a mobile phone. Once the created room model is registered to the real space, user-generated annotations can be attached and viewed in AR and VR modes. Finally, the framework supports object-based space to space registration for viewing and creating annotations from different views other than the view that generated the annotations. The performance of the proposed framework is demonstrated with achieved model accuracy, modeling time, stability of visual tracking and satisfaction of annotation. In the last section, we present two exemplar applications built on IMAF.     

Interactive real-time motion blur

Motion blurring of fast-moving objects is highly desirable for virtual environments and 3D user interfaces. However, all currently known algorithms for generating motion blur are too slow for inclusion in interactive 3D applications. We introduce a new motion-blur algorithm that works in three dimensions on a per object basis. The algorithm operates in real time even for complex objects consisting of several thousand polygons. While it only approximates true motion blur, the generated results are smooth and visually consistent. We achieve this performance break-through by taking advantage of hardware-assisted rendering of semitransparent polygons, a feature commonly available in today's workstations.     

A computational model of perceptual saliency for 3D objects in virtual environments

When giving directions to the location of an object, people typically use other attractive objects as reference, that is, reference objects. With the aim to select proper reference objects, useful for locating a target object within a virtual environment (VE), a computational model to identify perceptual saliency is presented. Based on the object’s features with the major stimulus for the human visual system, three basic features of a 3D object (i.e., color, size, and shape) are individually evaluated and then combined to get a degree of saliency for each 3D object in a virtual scenario. An experiment was conducted to evaluate the extent to which the proposed measure of saliency matches with the people’s subjective perception of saliency; the results showed a good performance of this computational model.     

Towards high-fidelity multi-sensory virtual environments

Virtual environments are playing an increasingly important role for training people about real world situations, especially through the use of serious games. A key concern is thus the level of realism that virtual environments require in order to have an accurate match of what the user can expect in the real world with what they perceive in the virtual one. Failure to achieve the right level of realism runs the real risk that the user may adopt a different reaction strategy in the virtual world than would be desired in reality. High-fidelity, physically-based rendering has the potential to deliver the same perceptual quality of an image as if you were “there” in the real world scene being portrayed. However, our perception of an environment is not only what we see, but may be significantly influenced by other sensory inputs, including sound, smell, feel, and even taste. Computation and delivery of all sensory stimuli at interactive rates is a computationally complex problem. To achieve true physical accuracy for each of the senses individually for any complex scene in real-time is simply beyond the ability of current standard desktop computers. This paper discusses how human perception, and in particular any cross-modal effects in multi-sensory perception, can be exploited to selectively deliver high-fidelity virtual environments. Selective delivery enables those parts of a scene which the user is attending to, to be computed in high quality. The remainder of the scene is delivered in lower quality, at a significantly reduced computational cost, without the user being aware of this quality difference.     

Multi‐Modal Perception for Selective Rendering

A major challenge in generating high‐fidelity virtual environments (VEs) is to be able to provide realism at interactive rates. The high‐fidelity simulation of light and sound is still unachievable in real time as such physical accuracy is very computationally demanding. Only recently has visual perception been used in high‐fidelity rendering to improve performance by a series of novel exploitations; to render parts of the scene that are not currently being attended to by the viewer at a much lower quality without the difference being perceived. This paper investigates the effect spatialized directional sound has on the visual attention of a user towards rendered images. These perceptual artefacts are utilized in selective rendering pipelines via the use of multi‐modal maps. The multi‐modal maps are tested through psychophysical experiments to examine their applicability to selective rendering algorithms, with a series of fixed cost rendering functions, and are found to perform significantly better than only using image saliency maps that are naively applied to multi‐modal VEs.     

Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.     

RGB-D图像的贝叶斯显著性检测

本文提出了一种基于贝叶斯框架融合颜色和深度对比特征的RGB-D图像显著性检测模型.基于空间先验的超像素对比计算得到深度特征,并通过高斯分布近似深度对比特征概率密度建模深度显著图.类似于深度显著性计算,采用高斯分布计算多尺度超像素低层对比特征得到颜色显著图.假设在给定显著类别下颜色和深度对比特征条件独立,依据贝叶斯定理,由深度显著概率和颜色显著概率得到RGB-D图像显著性后验概率,并采用判别混合分量朴素贝叶斯（DMNB）模型进行计算,其中DMNB模型中的高斯分布参数由变分最大期望算法进行估计.在RGB-D图像显著性检测公开数据集的实验结果表明提出的模型优于现有的方法.     

Dynamic interactions in physically realistic collaborative virtual environments

This work describes our efforts in creating a general object interaction framework for dynamic collaborative virtual environments. Furthermore, we increase the realism of the interactive world by using a rigid body simulator to calculate all actor and object movements. The main idea behind our interactive platform is to construct a virtual world using only objects that contain their own interaction information. As a result, the object interactions are application independent and only a single scheme is required to handle all interactions in the virtual world. In order to have more dynamic interactions, we also created a new and efficient way for human users to dynamically interact within virtual worlds through their avatar. In particular, we show how inverse kinematics can be used to increase the interaction possibilities and realism in collaborative virtual environments. This results in a higher feeling of presence for connected users and allows for easy, on-the-fly creation of new interactions. For the distribution of both the interactive objects and the dynamic avatar interactions, we keep the network load as low as possible. To demonstrate the effectiveness of our techniques, we incorporate them into an existing CVE framework.     

Saliency detection via image sparse representation and color features combination

Saliency detection is a technique to analyze image surroundings to extract relevant regions from the background. In this paper, we propose a simple and effective saliency detection method based on image sparse representation and color features combination. First, the input image is segmented into non-overlapping super-pixels, so as to perform the saliency detection at the region level to reduce computational complexity. Then, a background optimization selection scheme is used to construct an appropriate background template. Based on this, a primary saliency map is obtained by using image sparse representation. Next, through the linear combination of color coefficients we generate an improved saliency map with more prominent salient regions. Finally, the two saliency maps are integrated within Bayesian framework to obtain the final saliency map. Experimental results show that the proposed method has desirable detection performance in terms of detection accuracy and running time.

     

VR 系统信息可视化模型

针对虚拟环境中用户认知负荷较重和资源分配不协调问题,综合分析了人脑认知 活动中信息的显性化表达,提出一种基于分布式认知的信息可视化资源模型。通过计算机感知 虚拟环境中用户动作、行为、任务等信息,依据资源分配方案确定资源和信息之间映射关系, 并以信息表象的形式贮存;通过对信息表象进一步精致化,实现交互界面视觉元素的优化布局; 本文结合眼动追踪设备对VR 系统原型进行可用性评估实验,实验结果表明该可视化模型能够 降低用户认知负荷,改善用户体验。     

A Gaze‐enabled Graph Visualization to Improve Graph Reading Tasks

Performing typical network tasks such as node scanning and path tracing can be difficult in large and dense graphs. To alleviate this problem we use eye‐tracking as an interactive input to detect tasks that users intend to perform and then produce unobtrusive visual changes that support these tasks. First, we introduce a novel fovea based filtering that dims out edges with endpoints far removed from a user's view focus. Second, we highlight edges that are being traced at any given moment or have been the focus of recent attention. Third, we track recently viewed nodes and increase the saliency of their neighborhoods. All visual responses are unobtrusive and easily ignored to avoid unintentional distraction and to account for the imprecise and low‐resolution nature of eye‐tracking. We also introduce a novel gaze‐correction approach that relies on knowledge about the network layout to reduce eye‐tracking error. Finally, we present results from a controlled user study showing that our methods led to a statistically significant accuracy improvement in one of two network tasks and that our gaze‐correction algorithm enables more accurate eye‐tracking interaction.     

Gesture-based interactive augmented reality content authoring system using HMD

This paper proposes an augmented reality content authoring system that enables ordinary users who do not have programming capabilities to easily apply interactive features to virtual objects on a marker via gestures. The purpose of this system is to simplify augmented reality (AR) technology usage for ordinary users, especially parents and preschool children who are unfamiliar with AR technology. The system provides an immersive AR environment with a head-mounted display and recognizes users’ gestures via an RGB-D camera. Users can freely create the AR content that they will be using without any special programming ability simply by connecting virtual objects stored in a database to the system. Following recognition of the marker via the system’s RGB-D camera worn by the user, he/she can apply various interactive features to the marker-based AR content using simple gestures. Interactive features applied to AR content can enlarge, shrink, rotate, and transfer virtual objects with hand gestures. In addition to this gesture-interactive feature, the proposed system also allows for tangible interaction using markers. The AR content that the user edits is stored in a database, and is retrieved whenever the markers are recognized. The results of comparative experiments conducted indicate that the proposed system is easier to use and has a higher interaction satisfaction level than AR environments such as fixed-monitor and touch-based interaction on mobile screens.     

What Are You Looking at?

In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.     

基于视觉显著性的均值漂移跟踪算法

为解决突变运动下的目标跟踪问题,提出了一种基于视觉显著性的均值漂移跟踪算法,将视觉注意机制运用到均值漂移跟踪框架中,利用时空显著性算法对视频序列进行检测,生成视觉显著图,从视觉显著图对应的显著性区域中建立目标的颜色特征表示模型来实现运动目标跟踪.实验结果表明:该算法在摄像机摇晃等动态场景下可以较准确检测出时空均显著的目标,有效克服了在运动目标发生丢失和遮挡等情况下跟踪不稳定的问题,具有较强的鲁棒性,从而实现复杂场景下目标较准确的跟踪.     

Visual gesture interfaces for virtual environments

Virtual environments provide a whole new way of viewing and manipulating 3D data. Current technology moves the images out of desktop monitors and into the space immediately surrounding the user. Users can literally put their hands on the virtual objects. Unfortunately, techniques for interacting with such environments are yet to mature. Gloves and sensor-based trackers are unwieldy, constraining and uncomfortable to use. A natural, more intuitive method of interaction would be to allow the user to grasp objects with their hands and manipulate them as if they were real objects.We are investigating the use of computer vision in implementing a natural interface based on hand gestures. A framework for a gesture recognition system is introduced along with results of experiments in colour segmentation, feature extraction and template matching for finger and hand tracking, and simple hand pose recognition. Implementation of a gesture interface for navigation and object manipulation in virtual environments is presented.     

时空运动显著性的目标跟踪

目的 在目标跟踪过程中,运动信息可以预测目标位置,忽视目标的运动信息或者对其运动方式的建模与实际差异较大,均可能导致跟踪失败。针对此问题,考虑到视觉显著性具有将注意快速指向感兴趣目标的特点,将其引入目标跟踪中,提出一种基于时空运动显著性的目标跟踪算法。方法 首先,依据大脑视皮层对运动信息的层次处理机制,建立一种自底向上的时空运动显著性计算模型,即通过3D时空滤波器完成对运动信号的底层编码、最大化汇集算子完成运动特征的局部编码;利用视频前后帧之间的时间关联性,通过时空运动特征的差分完成运动信息的显著性度量,形成时空运动显著图。其次,在粒子滤波基本框架之下,将时空运动显著图与颜色直方图相结合,来衡量不同预测状态与观测状态之间的相关性,从而确定目标的状态,实现目标跟踪。结果 与其他跟踪方法相比,本文方法能够提高目标跟踪的中心位置误差、精度和成功率等指标;在光照变化、背景杂乱、运动模糊、部分遮挡及形变等干扰因素下,仍能够稳定地跟踪目标。此外,将时空运动显著性融入其他跟踪方法,能够改善跟踪效果,进一步验证了运动显著性对于运动目标跟踪的有效性。结论 时空运动显著性可以有效度量目标的运动信息,增强运动显著的目标区域,抑制干扰区域,从而提升跟踪性能。     

视觉感知正反馈的显著性检测

目的 人类视觉系统性能远超当前机器视觉,模拟人类视觉机制改进当前算法是有效研究途径,为此提出一种视觉感知正反馈模型,通过循环迭代、重复叠加视觉刺激生成更符合人类感知的视觉显著性图。方法 首先用多种常规方法检测图像显著度,模拟人类视觉多通道特性,再组合这些显著图为综合显著图;利用显著度大的像素构建初始注视区。其次借助集成RVFL（随机向量功能网络）模拟人脑神经网络产生视觉刺激,对注视与非注视区内像素在线“随机采样—学习建模”,图像像素经模型分类获得新注视区。对新注视区与非注视区,可重复迭代进行“随机采样—学习建模—像素分类”;迭代中若注视区连续相同,则表明感知饱和,迭代终止。若将每次像素分类结果看做是一种视觉刺激,则多次视觉刺激输出叠加,可生成新的图像显著性图。最终的像素分类结果就是图像分割目标。结果 将本文算法与现有方法在标准图像数据库上进行对比评测,包括通过对6种算法在ECSSD、SED2和MSRA10K 3个图像数据库上的P-R曲线,F-measure值和平均绝对误差（MAE）值上进行定量分析,对6种模型生成的显著性图作定性比较。数据表明,本文算法在SED2和MSRA10K图象数据库中性能最好,在ECSSD图象数据库中稍低于BL（bootstrap learning）和RBD（robust background detection）算法。本文算法的显著图与人类视觉感知更接近。且算法的正反馈迭代过程一般可迅速饱和,并未显著增加算法负担。实验结果表明,本文方法可作为一种有效的后处理手段,显著提升常规显著性检测算法的性能。结论 提出了一种模拟人类视觉机制的数据驱动显著性检测算法,无需图像先验知识和事先的标记样本。面对多目标,背景复杂等情况,本文方法具有相对好的鲁棒性和适用性,并且能够较好解决现实环境中图像处理算法的通用性、可靠性和准确性问题。