广告制胜的先决要素——视觉强度

视觉强度是所有诉诸视觉的艺术和设计追求的目的之一。广告是视觉艺术。若要释放这股强劲的潜能,视觉强度是视觉力量的保证。视觉强度,即对于视觉客体来说,是指对视觉主体的刺激或冲击程度;对于视觉主体来说,是指接受视觉对象刺激或冲击的强度,也称视觉感知度。一个人对不同的视觉对象产生不同的视觉感知度,而同一个视觉客体对不同人也可产生不同的冲击或刺激程度。空间与时间的差异也可引起视觉强度的差异。     

Constructing a discriminative visual vocabulary with macro and micro sense of visual words

Visual vocabulary representation approach has been successfully applied to many multimedia and vision applications, including visual recognition, image retrieval, and scene modeling/categorization. The idea behind the visual vocabulary representation is that an image can be represented by visual words, a collection of local features of images. In this work, we will develop a new scheme for the construction of visual vocabulary based on the analysis of visual word contents. By considering the content homogeneity of visual words, we design a visual vocabulary which contains macro-sense and micro-sense visual words. The two types of visual words are appropriately further combined to describe an image effectively. We also apply the visual vocabulary to construct image retrieving and categorization systems. The performance evaluation for the two systems indicates that the proposed visual vocabulary achieves promising results.     

基于协同感知的视觉选择注意计算模型

鉴于在任务相关的视觉注意中,需要建立基于任务的视觉注意显著图来引导视觉注意,为此利用与人认知过程相接近的协同感知理论来研究基于任务的视觉注意计算模型,即首先利用协同识别理论研究二义及多义模式的视觉感知,得到协同视觉感知理论;然后将协同视觉感知中的模式与从视觉注意模型中提取的底层视觉特征相对应,利用偏置矩阵的性质计算底层视觉特征间受任务影响而产生的偏置,再由此偏置和底层视觉特征生成基于任务的视觉注意显著图;最后提出了基于协同感知理论的视觉选择注意计算模型。该算法用于基于任务的视觉搜索的实验结果表明,该算法是有效的,在认知上是合理的。     

可视数据清洗综述

目的 数据清洗是一个长期存在并困扰人们的问题,随着可视化技术的发展,可视数据清洗必将成为数据清洗的重要方法之一.阐述数据的主要质量问题和可视数据清洗的过程,回顾可视数据清洗的研究现状(包括数据质量问题的来源、分类以及可视数据清洗方法),并根据已有文献总结可视数据清洗面临的主要挑战和机遇.方法 由于数据清洗的方法和策略与具体的数据质量问题相关,因此本文以不同的数据质量问题为线索来归纳和评述可视数据清洗的方法和策略.结果 根据数据质量问题的不同,将可视清洗方法归纳为直接可视清洗、可视缺失数据、可视不确定数据、可视数据转换和数据清洗资源共享等,并依据不同的数据质量问题归纳总结出相应问题所面临的挑战和可进一步研究的方向.结论 对可视数据清洗的归纳、总结和展望,并指出在数据清洗领域中可视数据清洗将会是未来最有前景的研究方向之一.     

Age-related changes in visual lobe shape characteristics and their relationship to visual search performance

Visual lobe shape plays an important role in visual search performance, but little is known about the age-related changes in visual lobe shape. The age-related changes in visual lobe shape characteristics and their relationships to visual search performance were investigated in this study. A total of 96 participants aged 15–64 years participated in this study. Their visual lobes were mapped on a uniform 2-D test field composed of 24 regularly spaced meridians passing through the centre of the visual field, and their search performances were also measured. The results showed that in general, age significantly affected visual lobe size, visual lobe shape and search time. As age increased, the visual lobe size decreased; in addition, the roundness, boundary smoothness, symmetry and regularity of the visual lobe deteriorated, and the search time increased. Moreover, significant correlations between visual lobe shape, search time and age were found. Regression analyses indicated that age was important in determining visual lobe shape and search time, suggesting that age differences should be considered when predicting search time and when designing tasks and products that involve visual search in our daily lives and work.     

视觉惯性SLAM综述

对视觉SLAM、惯性导航和视觉惯性SLAM进行了介绍,详细介绍了视觉SLAM的框架结构和细分部分,罗列了近年来出色的视觉SLAM模型。最后在视觉惯性里程计的（visual inertial odometry,VIO）基础上,对现有的不同派别的开源视觉惯性SLAM进行深入分析与比较,探讨视觉惯性SLAM的发展趋势与动向。     

Perceived Visual Complexity and Visual Search Performance of Automotive Instrument Cluster: A Quantitative Measurement Study

Advancements in technology have spurred the development of new in-vehicle applications. Drivers are faced with different driving contexts due to an increase in the number of devices that provide a wealth of diverse information. However, such a scenario can cause drivers to become distracted. Therefore, research on how the presentation of visual information can affect drivers’ performance is important. In this study, an analysis of quantifiable measurements that affect drivers’ perception of visual complexity and visual search performance was conducted. A questionnaire was administered to assess subjective perception of visual complexity, and a user experiment using eye tracking was designed to explore participants’ visual search performance. The results of subjective visual complexity perception and visual search performance suggested that some objective measurement variables were significantly related only to perceived visual complexity, whereas others affected both subjective and behavioral measurements. Thus it is possible to predict which quantifiable measurement variables affect subjective perception of visual complexity and which affect visual search performance. Therefore, this study allows understanding and explaining of perception of visual complexity by quantifiable measurements and the different ways by which these measurements affect visual search performance.     

视觉跟踪技术综述

视觉跟踪问题是当前计算机视觉领域中的热点问题,本文对这一问题进行了详细的介绍.首先,对视觉跟踪技术在视频监视、图像压缩和三维重构等三个主要方面的应用进行了论述.其次,详细阐述了该技术的研究现状,介绍了其中的一些常用方法,为清楚说明这些方法,先对视觉跟踪问题进行了分类,然后介绍了处理视觉跟踪问题的两种思路即自底向上和自顶向下的思路,最后将具体的视觉跟踪方法分为四类进行了介绍,这四类分别是基于区域的跟踪、基于特征的跟踪、基于变形模板的跟踪和基于模型的跟踪.最后,从控制论角度给出视觉跟踪算法所面临的难点,即算法要满足鲁棒性、准确性和快速性要求时所遇到的困难,并对视觉跟踪问题的研究前景进行了展望.     

混合增强视觉认知架构及其关键技术进展

智能视觉系统虽然在大规模信息的特征检测、提取与匹配等处理上具备一定优势，但是在深层次认知上仍存在不确定性和脆弱性，尤其是针对视觉感知基础上的视觉认知任务，相关数理逻辑和图像处理方法并未实现质的突破，智能算法难以取代人类执行较为复杂的理解、推理、决策和学习等操作。为助力智能视觉感知和认知技术的进一步发展，本文总结了混合增强智能在视觉认知领域的应用现状，给出了混合增强视觉认知的基本架构，并对可纳入该架构下的应用领域及关键技术进行了综述。首先，在分析智能视觉感知内涵和基本范畴的基础上，融合人的视觉感知与心理认知，探讨混合增强视觉认知的定义、范畴及其深化过程，对不同的视觉信息处理阶段进行对比，进而在分析相关认知模型发展现状的基础上，构建混合增强视觉认知的基本框架。该架构不仅可依靠智能算法进行快速地检测、识别、理解等处理，最大限度地挖掘"机"的计算潜能，而且可凭借适时、适当的人工推理、预测和决策有效增强系统认知的准确性和可靠性，最大程度地发挥人的认知优势。其次，分别从混合增强的视觉监测、视觉驾驶、视觉决策以及视觉共享等4个领域探讨可纳入该架构的代表性应用及存在的问题，指出混合增强视觉认知架构是现有技术条件下能够更好地发挥计算机效能、减轻人处理信息压力的方式。最后，基于高、中、低计算机视觉处理技术体系，分析混合增强视觉认知架构中部分中高级视觉处理技术的宏观、微观关系，重点综述可视化分析、视觉增强、视觉注意、视觉理解、视觉推理、交互式学习以及认知评估等关键技术。混合增强视觉认知架构有助于突破当前视觉信息认知"弱人工智能"的瓶颈，将有力促进智能视觉系统向人机深度融合方向发展。下一步，还需在纯粹的基础创新、高效的人机交互、柔性的连接通路等方面开展更加深入的研究。     

Effects of increasing visual load on aurally and visually guided target acquisition in a virtual environment

The aim of the present study is to investigate interactions between vision and audition during a target acquisition task performed in a virtual environment. We measured the time taken to locate a visual target (acquisition time) signalled by auditory and/or visual cues in conditions of variable visual load. Visual load was increased by introducing a secondary visual task. The auditory cue was constructed using virtual three-dimensional (3D) sound techniques. The visual cue was constructed in the form of a 3D updating arrow. The results suggested that both auditory and visual cues reduced acquisition time as compared to an uncued condition. Whereas the visual cue elicited faster acquisition time than the auditory cue, the combination of the two cues produced the fastest acquisition time. The introduction of secondary visual task differentially affected acquisition time depending on cue modality. In conditions of high visual load, acquiring a target signalled by the auditory cue led to slower and more error-prone performance than acquiring a target signalled by either the visual cue alone or by both the visual and auditory cues.     

Identification of human implicit visual search intention based on eye movement and pupillary analysis

We propose a novel approach for the identification of human implicit visual search intention based on eye movement patterns and pupillary analysis, in general, as well as pupil size, gradient of pupil size variation, fixation length and fixation count corresponding to areas of interest, and fixation count corresponding to non-areas of interest, in particular. The proposed model identifies human implicit visual search intention as task-free visual browsing or task-oriented visual search. Task-oriented visual search is further identified as task-oriented visual search intent generation, task-oriented visual search intent maintenance, or task-oriented visual search intent disappearance. During a visual search, measurement of the pupillary response is greatly influenced by external factors such the intensity and size of the visual stimulus. To alleviate the effects of external factors, we propose a robust baseline model that can accurately measure the pupillary response. Graphical representation of the measured parameter values shows significant differences among the different intent conditions, which can then be used as features for identification. By using the eye movement patterns and pupillary analysis, we can detect the transitions between different implicit intentions—task-free visual browsing intent to task-oriented visual search intent and task-oriented visual search intent maintenance to task-oriented visual search intent disappearance—using a hierarchical support vector machine. In the proposed model, the hierarchical support vector machine is able to identify the transitions between different intent conditions with greater than 90 % accuracy.     

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

The visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.     

Attention and visual memory in visualization and computer graphics

A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see" details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.     

Supporting multimedia learning with visual signalling and animated pedagogical agent: moderating effects of prior knowledge

An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer‐based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The experiment was a 2 (visual signalling, no visual signalling) × 2 (visual APA presence, no visual APA presence) between‐subjects design, resulting in four experimental conditions: visual signalling with APA presence (APA + S), visual signalling without APA presence (S), no visual signalling with APA presence (APA) and no visual signalling without APA presence (C). Signalling was provided via gestures of the APA in the APA + S condition and via dynamic arrows in the S condition. To investigate potential moderating effects of prior knowledge on APA presence and visual signalling factors, middle school students were grouped into low prior knowledge (LPK) and high prior knowledge (HPK) groups using scores on a domain pre‐test. Results revealed that LPK students had higher post‐test scores after learning with visual signalling, resulting in equivalent post‐test performance to their HPK counterparts. LPK students also had higher post‐test scores, higher ratings of graphics understanding and lower perceived difficulty ratings in conditions that included the visual image of the APA. Conversely, HPK students had better post‐test scores after learning without the APA. These results indicate that the effectiveness of visual signalling techniques and the visual presence of an APA is dependent on learner characteristics, including prior domain knowledge.     

Defining visual languages for interactive computing

A novel definition of visual languages allows a uniform approach to satisfying the needs of visual reasoning faced in visual human-computer interaction. The way the machine associates a computational meaning with an image, and conversely, the way it generates an image on the screen from a computation are formally described. A definition of a visual sentence and of a visual language as a set of visual sentences is discussed. A hierarchy of visual languages is derived in relation to the requirements for intelligible, manageable and trustable interaction between humans and computers     

Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?

IntroductionThe main purpose of this cross-sectional study was to investigate whether visual discomfort acts as a mediating factor between perceived visual ergonomic working conditions and self-rated visual performance among office workers who carry out administrative tasks and computer-based work at the Swedish Tax Agency.MethodsA questionnaire was sent to 94 office workers addressing: 1) perceived visual quality of the visual display units; 2) prevalence of eye symptoms; and 3) self-rated visual performance. Eighty-six persons (54 women (63%), 31 men (36%), and 1 of unspecified sex) answered the questionnaire. Multiple regression analysis investigated the association between visual ergonomic working conditions and visual performance, both with and without visual discomfort as a mediator.ResultsThe group mean of the Indexed survey questions indicated a reasonably good quality of visual ergonomic working conditions, a relative absence of eye symptoms, and acceptable self-rated visual performance. Results from multiple regression analysis showed a significant association between perceived visual ergonomic working conditions and self-rated visual performance (r² = 0.30, β = 0.327, p < 0.01). When visual discomfort was used as a mediator, the association between perceived visual ergonomic working conditions and self-rated visual performance remained the same (r² = 0.32, β = 0.315, p < 0.01).DiscussionIt was remarkable to discover that self-rated visual performance was independent of visual discomfort. Possible explanations include exposure factors not included in the current study, such as dry air and sensory irritation in the eyes, psychosocial stress, time spent performing near work activities, or time exposed to visually deficient working conditions.Relevance to industryThe strong connection between satisfaction with visual ergonomic working conditions and productivity in this study has implications for workplace profitability and staff satisfaction. If productivity is enhanced by better visual ergonomic working conditions, then managers of workplaces may be able to improve work outcomes by optimizing the physical work environment.     

Automated Visual Presentation: From Heterogeneous Information to Coherent Visual Discourse

Automated visual presentation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information. They should also be able to work in static or interactive environments and capable of employing a wide range of visual media and visual techniques. In this paper, we focus on three tasks in building visual production systems: establishing a thorough understanding of the presentation-related characteristics of domain-specific information; classifying several types of visual information and capturing their distinct syntactic, semantic, and pragmatic features; and formulating a set of design principles.We define a data-analysis taxonomy to characterize heterogeneous information. In addition, we have modeled presentation context information such as audience identity to produce user-centered visual design. To utilize and manipulate visual information, we have classified it into visual objects and visual tools based on its role in the visual production process. To guide the visual design process, we have formulated a set of design principles that ensure the expressiveness and effectiveness of a design. To test and evaluate our work, we have developed a prototype system called IMPROVISE based on the research results. We use examples generated by IMPROVISE to illustrate how it constructs visual presentations.