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GPU Accelerated Marine Data Visualization Method 被引量:1
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作者 LI Bo CHEN Ge +2 位作者 TIAN Fenglin SHAO Baomin JI Pengbo 《Journal of Ocean University of China》 SCIE CAS 2014年第6期964-970,共7页
The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean mo... The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering. 展开更多
关键词 marine data visualization techniques and methodologies spherical ray casting line integral convolution multiquadric method VV-Ocean
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Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model 被引量:4
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作者 CAI Yingfeng WANG Hai +2 位作者 CHEN Xiaobo GAO Li CHEN Long 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第4期765-772,共8页
Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high ... Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle. 展开更多
关键词 vehicle detection visual saliency deep model convolution neural network
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