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Truc Le - IEEE Xplore Author Profile

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Author details

Truc Le

Publications

6

Citations

1,089

Publications by Year

20162020

Co-Authors:

Abhinav AgarwallaEirikur AgustssonWoong BaeGiang BuiLei Cao

Show All Co-Authors (80)

Truc Le

Affiliation

Department of EECS

University of Missouri

Columbia, USA

Publication Topics

Convolutional Layers,
Convolutional Neural Network,
Deep Learning,
Feature Maps,
Image Edge,
Point Cloud,
Skip Connections,
3D Convolution,
3D Convolutional Network,
3D Point Cloud,
3D Scanning,
3D Segmentation

Biography

Truc Le was born in Ho Chi Minh City, Vietnam. He received the Ph.D. degree in computer science from the University of Missouri, Columbia, in 2018, under Dr. Ye Duan’s supervision. He is currently working with the Google Cloud Healthcare Team, Google, Canada. His research interests include machine learning and computer vision with a focus on deep learning. He received the Vietnam Education Fellowship, a U.S.-funded program for excellent students in Vietnam, in 2012.(Based on document published on 13 May 2020).

Publications

6

Citations

1,089

Publications by Year

20162020

Co-Authors:

Abhinav Agarwalla
Eirikur Agustsson
Woong Bae
Giang Bui
Lei Cao

Show All Co-Authors (80)

Author's Published Works

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Showing 1-6 of 6 results

Conferences (4)

Journals (2)

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Single Year
Ye Duan(6)
Truc Le(6)
Giang Bui(3)
Brittany Morago(2)
Jong Chul Ye(1)
IFP, University of Illinois, Urbana-Champaign, US(1)
Shandong University, China(1)
SenseTime(1)
Sejong University, Seoul, Korea(1)
Korea Ad. Inst. of Science & Technology (KAIST)(1)
2016 IEEE International Conference on Image Processing (ICIP)(1)
2016 IEEE Virtual Reality (VR)(1)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(1)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(1)
IEEE Access(1)
IEEE(6)
Greenville, SC, USA(1)
Honolulu, HI, USA(1)
Phoenix, AZ, USA(1)
Salt Lake City, UT, USA(1)
Convolutional Layers(3)
Point Cloud(3)
3D Point Cloud(2)
3D Scanning(2)
Convolutional Neural Network(2)

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REDN: A Recursive Encoder-Decoder Network for Edge Detection

Truc Le;Ye Duan

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (15)

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In this paper, we introduce REDN: A Recursive Encoder-Decoder Network with Skip-Connections for edge detection in natural images. The proposed network is a novel integration of a Recursive Neural Network with an Encoder-Decoder architecture. The recursive network enables iterative refinement of the edges using a single network model. Adding skip-connections between encoder and decoder helps the gr...Show More

REDN: A Recursive Encoder-Decoder Network for Edge Detection

Truc Le;Ye Duan

Year: 2020 | Volume: 8 | Journal Article |

PointGrid: A Deep Network for 3D Shape Understanding

Truc Le;Ye Duan

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Cited by: Papers (274) | Patents (2)

HTML

Volumetric grid is widely used for 3D deep learning due to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally i...Show More

PointGrid: A Deep Network for 3D Shape Understanding

Truc Le;Ye Duan

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Year: 2018 | Conference Paper |

Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement

Brittany Morago;Giang Bui;Truc Le;Norbert H. Maerz;Ye Duan

IEEE Geoscience and Remote Sensing Letters

Year: 2018 | Volume: 15, Issue: 6 | Journal Article |

Cited by: Papers (9)

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Rock detachment events along roadways pose public safety concerns but can be predicted and safely handled using geological measurements of discontinuities. With modern sensing technology, these measurements can be taken on 3-D point clouds and 2-D optical images that provide a high level of structural accuracy and visual detail. Doing so allows engineers to obtain the needed data with relative eas...Show More

Photograph LIDAR Registration Methodology for Rock Discontinuity Measurement

Brittany Morago;Giang Bui;Truc Le;Norbert H. Maerz;Ye Duan

IEEE Geoscience and Remote Sensing Letters

Year: 2018 | Volume: 15, Issue: 6 | Journal Article |

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang;Lei Zhang;Bee Lim;Sanghyun Son;Heewon Kim;Seungjun Nah;Kyoung Mu Lee;Xintao Wang;Yapeng Tian;Ke Yu;Yulun Zhang;Shixiang Wu;Chao Dong;Liang Lin;Yu Qiao;Chen Change Loy;Woong Bae;Jaejun Yoo;Yoseob Han;Jong Chul Ye;Jae-Seok Choi;Munchurl Kim;Yuchen Fan;Jiahui Yu;Wei Han;Ding Liu;Haichao Yu;Zhangyang Wang;Honghui Shi;Xinchao Wang;Thomas S. Huang;Yunjin Chen;Kai Zhang;Wangmeng Zuo;Zhimin Tang;Linkai Luo;Shaohui Li;Min Fu;Lei Cao;Wen Heng;Giang Bui;Truc Le;Ye Duan;Dacheng Tao;Ruxin Wang;Xu Lin;Jianxin Pang;Jinchang Xu;Yu Zhao;Xiangyu Xu;Jinshan Pan;Deqing Sun;Yujin Zhang;Xibin Song;Yuchao Dai;Xueying Qin;Xuan-Phung Huynh;Tiantong Guo;Hojjat Seyed Mousavi;Tiep Huu Vu;Vishal Monga;Cristovao Cruz;Karen Egiazarian;Vladimir Katkovnik;Rakesh Mehta;Arnav Kumar Jain;Abhinav Agarwalla;Ch V. Sai Praveen;Ruofan Zhou;Hongdiao Wen;Che Zhu;Zhiqiang Xia;Zhengtao Wang;Qi Guo

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

Cited by: Papers (771) | Patents (1)

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This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 ...Show More

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang;Lei Zhang;Bee Lim;Sanghyun Son;Heewon Kim;Seungjun Nah;Kyoung Mu Lee;Xintao Wang;Yapeng Tian;Ke Yu;Yulun Zhang;Shixiang Wu;Chao Dong;Liang Lin;Yu Qiao;Chen Change Loy;Woong Bae;Jaejun Yoo;Yoseob Han;Jong Chul Ye;Jae-Seok Choi;Munchurl Kim;Yuchen Fan;Jiahui Yu;Wei Han;Ding Liu;Haichao Yu;Zhangyang Wang;Honghui Shi;Xinchao Wang;Thomas S. Huang;Yunjin Chen;Kai Zhang;Wangmeng Zuo;Zhimin Tang;Linkai Luo;Shaohui Li;Min Fu;Lei Cao;Wen Heng;Giang Bui;Truc Le;Ye Duan;Dacheng Tao;Ruxin Wang;Xu Lin;Jianxin Pang;Jinchang Xu;Yu Zhao;Xiangyu Xu;Jinshan Pan;Deqing Sun;Yujin Zhang;Xibin Song;Yuchao Dai;Xueying Qin;Xuan-Phung Huynh;Tiantong Guo;Hojjat Seyed Mousavi;Tiep Huu Vu;Vishal Monga;Cristovao Cruz;Karen Egiazarian;Vladimir Katkovnik;Rakesh Mehta;Arnav Kumar Jain;Abhinav Agarwalla;Ch V. Sai Praveen;Ruofan Zhou;Hongdiao Wen;Che Zhu;Zhiqiang Xia;Zhengtao Wang;Qi Guo

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2017 | Conference Paper |

Circle detection on images by line segment and circle completeness

Truc Le;Ye Duan

2016 IEEE International Conference on Image Processing (ICIP)

Year: 2016 | Conference Paper |

Cited by: Papers (15) | Patents (3)

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Circle detection from digital images is a necessary operation in many robotics and computer vision tasks to facilitate shape and object recognition. We propose and analyze a novel method, based on line segment detection and circle completeness verification, to detect circles in images. The key idea is to use line segments instead of raw edge pixels to get the circle candidates followed by a verifi...Show More

Circle detection on images by line segment and circle completeness

Truc Le;Ye Duan

2016 IEEE International Conference on Image Processing (ICIP)

Year: 2016 | Conference Paper |

Integrating videos with LIDAR scans for virtual reality

Giang Bui;Brittany Morago;Truc Le;Kevin Karsch;Zheyu Lu;Ye Duan

2016 IEEE Virtual Reality (VR)

Year: 2016 | Conference Paper |

Cited by: Papers (5)

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LIDAR range scans can be used to quickly create accurate 3D models for virtual reality and as a basis to visualize sets of photographs, videos, and virtual objects in a cohesive environment. The number of existing virtual reality programs that use LIDAR data as input has motivated our group to develop methods for fusing images with 3D scans and for augmenting the scans with both dynamic objects pr...Show More

Integrating videos with LIDAR scans for virtual reality

Giang Bui;Brittany Morago;Truc Le;Kevin Karsch;Zheyu Lu;Ye Duan

2016 IEEE Virtual Reality (VR)

Year: 2016 | Conference Paper |

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