The contour tree has been used to compute the topology of isosurfaces, generate a minimal seed set for accelerated isosurface extraction, and provide a user interface to segment individual contour components in a scalar field. In this paper, we extend the benefits of the contour tree to time-varying data visualization. We define temporal correspondence of contour components and describe an algorithm to compute the correspondence information in time-dependent contour trees. A graph representing the topology changes of time-varying isosurfaces is constructed in real-time for any selected isovalue using the precomputed correspondence information. Quantitative properties, such as surface area and volume of contour components, are computed and labeled on the graph. This topology change graph helps users to detect significant topological and geometric changes in timevarying isosurfaces. The graph is also used as an interactive user interface to segment, track. and visualize the evolution of any selected contour components over time.

Figure 1: Adaptive tetrahedral meshes extracted from UNC Head (CT, 129×129×129). Isovalues (αin, αout) = (1000, 50) in (a)(b), and (1000, 120) in (c)(d); error tolerance εin = 0.0001, εout = (a): 0.0001, (b): 2.856, (c): 2.627, (d): 9.999. in and out represent inner and outer isosurface respectively. The number of elements and the extraction time are listed in Figure 4. ABSTRACTThis paper presents an algorithm to extract adaptive and quality 3D meshes directly from volumetric imaging dataprimarily Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The extracted tetrahedral and hexahedral meshes are extensively used in finite element simulations. Our comprehensive approach combines bilateral and anisotropic (feature specific) diffusion filtering, with contour spectrum based, isosurface and interval volume selection. Next, a top-down octree subdivision coupled with the dual contouring method is used to rapidly extract adaptive 3D finite element meshes from volumetric imaging data. The main contributions are extending the dual contouring method to crack free interval volume tetrahedralization and hexahedralization with feature sensitive adaptation. Compared to other tetrahedral extraction methods from imaging data, our method generates better quality adaptive 3D meshes without hanging nodes. Our method has the properties of crack prevention and feature sensitivity.

This paper presents a novel method for the segmentation of regions of interest in mammograms. The algorithm concurrently delineates the boundaries of the breast boundary, the pectoral muscle, as well as dense regions that include candidate masses. The resulting representation constitutes an analysis of the global structure of the object in the mammogram. We propose a topographic representation called the isocontour map, in which a salient region forms a dense quasi-concentric pattern of contours. The topological and geometrical structure of the image is analyzed using an inclusion tree that is a hierarchical representation of the enclosure relationships between contours. The "saliency" of a region is measured topologically as the minimum nesting depth. Features at various scales are analyzed in multiscale isocontour maps, and we demonstrate that the multiscale scheme provides an efficient way of achieving better delineations. Experimental results demonstrate that the proposed method has potential as the basis for a prompting system in mammogram mass detection.

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

Building Information Modeling (BIM) refers to 3D-based digital modeling of buildings and infrastructure for efficient design, construction, and management. Governments have recognized and encouraged BIM as a primary method for enabling advanced construction technologies. However, BIM is not universally employed in industries, and most designers still use Computer-Aided Design (CAD) drawings, which have been used for several decades. This is because the initial costs for setting up a BIM work environment and the maintenance costs involved in using BIM software are substantially high. With this motivation, we propose a novel software system that automatically generates BIM models from two-dimensional (2D) CAD drawings. This is highly significant because only 2D CAD drawings are available for most of the existing buildings. Notably, such buildings can benefit from the BIM technology using our low-cost conversion system. One of the common problems in existing methods is possible loss of information that may occur during the process of conversion from CAD to BIM because they mainly focus on creating 3D geometric models for BIM by using only floor plans. The proposed method has an advantage of generating BIM that contains property information in addition to the 3D models by analyzing floor plans and other member lists in the input design drawings together. Experimental results show that our method can quickly and accurately generate BIM models from 2D CAD drawings.