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Advances in mathematical models and optimization introduce powerful tools for solving the visual object tracking problem, but this fundamental problem remains unsolved due to various challenges such as illumination variation, deformation and occlusion. Recent progress in correlation filter based models has provided an effective and efficient tracking solution. This type of trackers exploits the property of convolution theorem to significantly speed up their computational processes. However, their scheme of training correlation filters restricts them to use only a few negative examples to train their model, consequently lowers their performance. Therefore, in this paper, we propose to combine the fast calculation advantage of correlation filter with an online discriminative learning model, which can fully exploit the negative examples in the context around the target. The proposed tracker proceeds in a coarse-to-fine scheme: the proposed tracker will first employ a correlation filter to generate a coarse estimation of the location of the target, and then employ a translating model to refine its estimation and calculate the scale variation of the target via a scaling model. Both the translating model and the scaling model are formulated in the framework of our online discriminative model. Besides, we also propose an effective offline representation learning method to generate robust image feature for our correlation filter. Extensive experiments on the online Object Tracking Benchmark against the state-of-the-art methods validate the effectiveness of the proposed tracker. [ABSTRACT FROM AUTHOR]

Theory U, Action Inquiry, Adult Development Theories, etc. are proposed as models for realization of dreams, transformation and reflection for individuals, teams, organizations. However, the relationships among these theories and models have not necessarily been clearly discussed. In this paper, we propose an integrated reference model called IRMDT to comprehensively overlook the relationships of these models. First, we overview the transformation methodologies such as THEORY U, Action Inquiry, adult development theory, methodologies to draw dreams, and methods to realize dreams. Then, we present the requirements that the reference model should have in order to comprehensively understand these methodologies. We propose an integrated reference model called IRMDT that satisfies these requirements. Furthermore, based on this reference model, we propose a visualization model for human thought processes called MVRDT, from multi-layered perspectives of U process, purpose and means system for realizing dream, reflection process, and belief system. By utilizing this model, it becomes possible to visualize, understand and interpret the process of thinking, transformation, introspection, and growth from multiple layered point of view, when people proceed toward the realization of dreams. Finally, we describe the application to artificial consciousness etc. as future development. [ABSTRACT FROM AUTHOR]

In this paper tag cloud construction for web exposition is studied. Construction of a tag cloud must simultaneously solve at least three interdisciplinary engineering problems: modeling and controlling graphics aesthetics, solving discrete two-dimensional layout optimization problem, and all these must be done on computationally constrained browser platform. We analyze the design choices in the earlier tag cloud studies and provide a taxonomy of algorithmic approaches to tag cloud building. Then, the design requirements for tag clouds on websites are defined. We propose to quantify tag cloud aesthetics by use of a novel objective function based on the rules of typography. Tag cloud construction is formalized as a combinatorial optimization problem with an irregular objective function. A set of algorithms is proposed and evaluated on a collection of tag sets from popular web pages. The methods that meet constraints of the browser platform are chosen. [ABSTRACT FROM AUTHOR]

Many applications require manipulation and visualization of complex and highly detailed models at realtime. In this paper, we present a new mesh process and rendering method for realtime high quality rendering. The basic idea is to send a simplified mesh to hardware pipeline, while use the online tessellation on the GPU to facilitate the rendering of complex geometric details. We formulate it into an inverse tessellation problem that first computes the simplified mesh, and then optimizes the tessellated mesh with geometric details to approximate the original mesh. To solve this problem, we propose a two-stage algorithm. In the first stage, we employ an iterative surface simplification technique, where we take the requirement of hardware tessellation into consideration to obtain an optimal simplified mesh. In the second stage, to better utilize the hardware tessellation, we propose a moving vertex strategy to approximate the tessellated mesh to the original mesh. Results show that our method achieves 2–4 times faster at rendering but still retains high quality geometrical details. [ABSTRACT FROM AUTHOR]

Numerous information visualization techniques are available for utilizing and analyzing large data. Among these techniques, network visualization, which employs node-link diagrams, can determine the relationship among multi-dimensional data. However, when data become extremely large, visualization becomes obscure because of visual clutter. To address this problem, many edge bundling techniques have been proposed. However, although graphs present several edge types, previous techniques do not reflect these edges. In this paper, we propose a new edge bundling method for multi-type co-occurring graphs. In this method, electro-static forces work between each pair of edges; however, if the edges are of different types, then repulsion works between pairs. By bundling edges of the same type, a graph can more clearly show relationships among data. Qualitative evaluation through questionnaires lead to useful knowledge, i.e., the proposed method improves bundling performance more extensively than other related work. [ABSTRACT FROM AUTHOR]