The REU Site: Research Experience for Undergraduates in Collaborative Data Visualization Applications (VisREU) is a multi-year, interdisciplinary program that provides research experiences for undergraduates with an i...
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ISBN:
(纸本)9781509034208
The REU Site: Research Experience for Undergraduates in Collaborative Data Visualization Applications (VisREU) is a multi-year, interdisciplinary program that provides research experiences for undergraduates with an interest in visualization. The unique feature of the VisREU site is the paring of undergraduate students with research teams which consist of a research faculty member, graduate students (where applicable) and a visualization mentor. Research faculty provide research projects with a visualization component that allow undergraduate students an opportunity to understand the visualization process while doing quality research. Visualization mentors work with student/research mentor teams to create visualizations of research data. In 2015 the program saw an increase in the level of engagement from student participants and research mentors. For two consecutive years (2014/2015) one student from each cohort was selected to present their research results at the annual National Science Foundation (NSF) REU Symposium in Arlington, VA. This talk presents a high-level overview of highlights from the program.
We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations,we inves...
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Today, the need for interaction and visualization techniques to fulfill user requirements for collaborative work is ever increasing. Current approaches do not suffice since they do not consider the simultaneous work o...
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This study examines the content of professional, semi-professional, and user-generated reviews. By comparing these three review types, this study was able to identify characteristics and factors unique to each type of...
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In this contribution, a software system for computer-aided position planning of miniplates to treat facial bone defects is proposed. The intra-operatively used bone plates have to be passively adapted on the underlyin...
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Video key frame extraction is a video summarization technique which is used to recapitulate and describe the most important segments of video sequence. Video clips containing motion information are more likely to draw...
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We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an e...
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ISBN:
(纸本)9781467380270
We present a novel way of odometry estimation from Velodyne LiDAR point cloud scans. The aim of our work is to overcome the most painful issues of Velodyne data - the sparsity and the quantity of data points - in an efficient way, enabling more precise registration. Alignment of the point clouds which yields the final odometry is based on random sampling of the clouds using Collar Line Segments (CLS). The closest line segment pairs are identified in two sets of line segments obtained from two consequent Velodyne scans. From each pair of correspondences, a transformation aligning the matched line segments into a 3D plane is estimated. By this, significant planes (ground, walls, ...) are preserved among aligned point clouds. Evaluation using the KITTI dataset shows that our method outperforms publicly available and commonly used state-of-the-art method GICP for point cloud registration in both accuracy and speed, especially in cases where the scene lacks significant landmarks or in typical urban elements. For such environments, the registration error of our method is reduced by 75% compared to the original GICP error.
This paper presents an approach for automatically aligning the non-overlapping interior and exterior parts of a 3D building model computed from image based 3D reconstructions. We propose a method to align the 3D recon...
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This paper presents an approach for automatically aligning the non-overlapping interior and exterior parts of a 3D building model computed from image based 3D reconstructions. We propose a method to align the 3D reconstructions by identifying corresponding 3D structures that are part of the interior and exterior model (e.g. openings like windows). In this context, we point out the potential of using 3D line segments to enrich the information of point clouds generated by SfMs and show how this can be used for interpreting the scene and matching individual reconstructions.
In this study, we compare the performance of our previously proposed deformable 2D/3D registration approach based on the Levenberg-Marquardt optimization with methods exploiting Covariance Matrix Adaptation (CMA) and ...
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