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		<title><![CDATA[ Visualization and Computer Graphics, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 2945 </description>
		<year>2013</year>
		<month>May      </month>
		<day>16</day>
		<item>
			<title><![CDATA[Guest Editors' Introduction: Special Section on the IEEE Conference on Visual Analytics Science and Technology (VAST)]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514020]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514020]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1076</startPage>
			<endPage>1077</endPage>
			<fileSize>78</fileSize>
			<authors><![CDATA[Miksch, S.;Ward, M.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Scalable Analysis of Movement Data for Extracting and Exploring Significant Places]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361385]]></link>
			<description><![CDATA[Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361385]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1078</startPage>
			<endPage>1094</endPage>
			<fileSize>1918</fileSize>
			<authors><![CDATA[Andrienko, Gennady;Andrienko, Natalia;Hurter, Christophe;Rinzivillo, Salvatore;Wrobel, Stefan;]]></authors>
		</item>
		<item>
			<title><![CDATA[The Longitudinal Use of SaNDVis: Visual Social Network Analytics in the Enterprise]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381408]]></link>
			<description><![CDATA[As people continue to author and share increasing amounts of information in social media, the opportunity to leverage such information for relationship discovery tasks increases. In this paper, we describe a set of systems that mine, aggregate, and infer a social graph from social media inside an enterprise, resulting in over 73 million relationships between 450,000 people. We then describe SaNDVis, a novel visual analytics tool that supports people-centric tasks like expertise location, team building, and team coordination in the enterprise. We provide details of a 22-month-long, large-scale deployment to over 2,300 users from which we analyze longitudinal usage patterns, classify types of visual analytics queries and users, and extract dominant use cases from log and interview data. By integrating social position, evidence, and facets into SaNDVis, we demonstrate how users can use a visual analytics tool to reflect on existing relationships as well as build new relationships in an enterprise setting.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381408]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1095</startPage>
			<endPage>1108</endPage>
			<fileSize>1193</fileSize>
			<authors><![CDATA[Perer, Adam;Guy, Ido;Uziel, Erel;Ronen, Inbal;Jacovi, Michal;]]></authors>
		</item>
		<item>
			<title><![CDATA[How Visualization Layout Relates to Locus of Control and Other Personality Factors]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6297975]]></link>
			<description><![CDATA[Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. We extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as &#x0022;locus of control&amp;#x201D; (LOC), which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling. We conduct a user study with four visualizations that gradually shift from a list metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control and other personality factors. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. These results provide evidence for the externalization theory of visualization. Finally, we propose applications of these findings to adaptive visual analytics and visualization evaluation.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6297975]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1109</startPage>
			<endPage>1121</endPage>
			<fileSize>1214</fileSize>
			<authors><![CDATA[Ziemkiewicz, Caroline;Ottley, Alvitta;Crouser, R.Jordan;Yauilla, Ashley Rye;Su, Sara L.;Ribarsky, William;Chang, Remco;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Metric for the Evaluation of Dense Vector Field Visualizations]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269874]]></link>
			<description><![CDATA[In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric's applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269874]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1122</startPage>
			<endPage>1132</endPage>
			<fileSize>3052</fileSize>
			<authors><![CDATA[Matvienko, Victor;Kr&#x00FC;ger, Jens;]]></authors>
		</item>
		<item>
			<title><![CDATA[Analytic Double Product Integrals for All-Frequency Relighting]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6244794]]></link>
			<description><![CDATA[This paper presents a new technique for real-time relighting of static scenes with all-frequency shadows from complex lighting and highly specular reflections from spatially varying BRDFs. The key idea is to depict the boundaries of visible regions using piecewise linear functions, and convert the shading computation into double product integrals&amp;#x2014;the integral of the product of lighting and BRDF on visible regions. By representing lighting and BRDF with spherical Gaussians and approximating their product using Legendre polynomials locally in visible regions, we show that such double product integrals can be evaluated in an analytic form. Given the precomputed visibility, our technique computes the visibility boundaries on the fly at each shading point, and performs the analytic integral to evaluate the shading color. The result is a real-time all-frequency relighting technique for static scenes with dynamic, spatially varying BRDFs, which can generate more accurate shadows than the state-of-the-art real-time PRT methods.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6244794]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1133</startPage>
			<endPage>1142</endPage>
			<fileSize>1481</fileSize>
			<authors><![CDATA[Wang, Rui;Pan, Minghao;Chen, Weifeng;Ren, Zhong;Zhou, Kun;Hua, Wei;Bao, Hujun;]]></authors>
		</item>
		<item>
			<title><![CDATA[Generalized Anisotropic Stratified Surface Sampling]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6378367]]></link>
			<description><![CDATA[We introduce a novel stratified sampling technique for mesh surfaces that gives the user control over sampling density and anisotropy via a tensor field. Our approach is based on sampling space-filling curves mapped onto mesh segments via parametrizations aligned with the tensor field. After a short preprocessing step, samples can be generated in real time. Along with visual examples, we provide rigorous spectral analysis and differential domain analysis of our sampling. The sample distributions are of high quality: they fulfil the blue noise criterion, so have minimal artifacts due to regularity of sampling patterns, and they accurately represent isotropic and anisotropic densities on the plane and on mesh surfaces. They also have low discrepancy, ensuring that the surface is evenly covered.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6378367]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1143</startPage>
			<endPage>1157</endPage>
			<fileSize>3042</fileSize>
			<authors><![CDATA[Quinn, Jonathan A.;Langbein, Frank C.;Lai, Yu-Kun;Martin, Ralph R.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Interactive Applications for Sketch-Based Editable Polycube Map]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361388]]></link>
			<description><![CDATA[In this paper, we propose a sketch-based editable polycube mapping method that, given a general mesh and a simple polycube that coarsely resembles the shape of the object, plus sketched features indicating relevant correspondences between the two, provides a uniform, regular, and user-controllable quads-only mesh that can be used as a basis structure for subdivision. Large scale models with complex geometry and topology can be processed efficiently with simple, intuitive operations. We show that the simple, intuitive nature of the polycube map is a substantial advantage from the point of view of the interface by demonstrating a series of applications, including kit-basing, shape morphing, painting over the parameterization domain, and GPU-friendly tessellated subdivision displacement, where the user is also able to control the number of patches in the base mesh by the construction of the base polycube.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361388]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1158</startPage>
			<endPage>1171</endPage>
			<fileSize>2828</fileSize>
			<authors><![CDATA[Garcia, Ismael;Xia, Jiazhi;He, Ying;Xin, Shi-Qing;Patow, Gustavo;]]></authors>
		</item>
		<item>
			<title><![CDATA[Pairwise Harmonics for Shape Analysis]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361386]]></link>
			<description><![CDATA[This paper introduces a simple yet effective shape analysis mechanism for geometry processing. Unlike traditional shape analysis techniques which compute descriptors per surface point up to certain neighborhoods, we introduce a shape analysis framework in which the descriptors are based on pairs of surface points. Such a pairwise analysis approach leads to a new class of shape descriptors that are more global, discriminative, and can effectively capture the variations in the underlying geometry. Specifically, we introduce new shape descriptors based on the isocurves of harmonic functions whose global maximum and minimum occur at the point pair. We show that these shape descriptors can infer shape structures and consistently lead to simpler and more efficient algorithms than the state-of-the-art methods for three applications: intrinsic reflectional symmetry axis computation, matching shape extremities, and simultaneous surface segmentation and skeletonization.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361386]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1172</startPage>
			<endPage>1184</endPage>
			<fileSize>1219</fileSize>
			<authors><![CDATA[Zheng, Youyi;Tai, Chiew-Lan;Zhang, Eugene;Xu, Pengfei;]]></authors>
		</item>
		<item>
			<title><![CDATA[Parallel Streamline Placement for 2D Flow Fields]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6264048]]></link>
			<description><![CDATA[Parallel streamline placement is still an open problem in flow visualization. In this paper, we propose an innovative method to place streamlines in parallel for 2D flow fields. This method is based on our proposed concept of local tracing areas (LTAs). An LTA is defined as a subdomain enclosed by streamlines and/or field borders, where the tracing of streamlines are localized. Given a flow field, it is initialized as an LTA, which is later recursively partitioned into hierarchical LTAs. Streamlines are placed within different LTAs simultaneously and independently. At the same time, to control the density of streamlines, each streamline is associated with an isolation zone and a saturation zone, both of which are center aligned with the streamline but have different widths. None of streamlines can trace into isolation zones of others. And new streamlines are only seeded within valid seeding areas (VSAs) that are enclosed by saturation zones and/or field borders. To implement the parallel strategy and the density control, a cell-based modeling is devised to describe isolation zones and LTAs as well as saturation zones and VSAs. With the help of these cell-based models, a heuristic seeding strategy is proposed to seed streamlines within irregular LTAs, and a cell-marking technique is used to control the seeding and tracing of streamlines. Test results show that the placement method can achieve highly parallel performance on shared memory systems without losing the quality of placements.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6264048]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1185</startPage>
			<endPage>1198</endPage>
			<fileSize>1654</fileSize>
			<authors><![CDATA[Zhang, Wenyao;Wang, Yi;Zhan, Jianfeng;Liu, Beichen;Ning, Jianguo;]]></authors>
		</item>
		<item>
			<title><![CDATA[Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361384]]></link>
			<description><![CDATA[Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361384]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1199</startPage>
			<endPage>1217</endPage>
			<fileSize>894</fileSize>
			<authors><![CDATA[Tam, Gary K.L.;Cheng, Zhi-Quan;Lai, Yu-Kun;Langbein, Frank C.;Liu, Yonghuai;Marshall, David;Martin, Ralph R.;Sun, Xian-Fang;Rosin, Paul L.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Timeline Editing of Objects in Video]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226393]]></link>
			<description><![CDATA[We present a video editing technique based on changing the timelines of individual objects in video, which leaves them in their original places but puts them at different times. This allows the production of object-level slow motion effects, fast motion effects, or even time reversal. This is more flexible than simply applying such effects to whole frames, as new relationships between objects can be created. As we restrict object interactions to the same spatial locations as in the original video, our approach can produce high-quality results using only coarse matting of video objects. Coarse matting can be done efficiently using automatic video object segmentation, avoiding tedious manual matting. To design the output, the user interactively indicates the desired new life spans of objects, and may also change the overall running time of the video. Our method rearranges the timelines of objects in the video whilst applying appropriate object interaction constraints. We demonstrate that, while this editing technique is somewhat restrictive, it still allows many interesting results.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226393]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1218</startPage>
			<endPage>1227</endPage>
			<fileSize>1337</fileSize>
			<authors><![CDATA[Lu, Shao-Ping;Zhang, Song-Hai;Wei, Jin;Hu, Shi-Min;Martin, Ralph R.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Visualizing Natural Image Statistics]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361387]]></link>
			<description><![CDATA[Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution, and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361387]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1228</startPage>
			<endPage>1241</endPage>
			<fileSize>4086</fileSize>
			<authors><![CDATA[Fang, Hui;Tam, Gary Kwok-Leung;Borgo, Rita;Aubrey, Andrew J.;Grant, Philip W.;Rosin, Paul L.;Wallraven, Christian;Cunningham, Douglas;Marshall, David;Chen, Min;]]></authors>
		</item>
		<item>
			<title><![CDATA[Water Surface Modeling from a Single Viewpoint Video]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6338256]]></link>
			<description><![CDATA[We introduce a video-based approach for producing water surface models. Recent advances in this field output high-quality results but require dedicated capturing devices and only work in limited conditions. In contrast, our method achieves a good tradeoff between the visual quality and the production cost: It automatically produces a visually plausible animation using a single viewpoint video as the input. Our approach is based on two discoveries: first, shape from shading (SFS) is adequate to capture the appearance and dynamic behavior of the example water; second, shallow water model can be used to estimate a velocity field that produces complex surface dynamics. We will provide qualitative evaluation of our method and demonstrate its good performance across a wide range of scenes.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6338256]]></guid>
			<volume>19</volume>
			<issue>7</issue>
			<startPage>1242</startPage>
			<endPage>1251</endPage>
			<fileSize>4411</fileSize>
			<authors><![CDATA[Li, Chuan;Pickup, David;Saunders, Thomas;Cosker, Darren;Marshall, David;Hall, Peter;Willis, Philip;]]></authors>
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