布树辉


的个人主页 http://teacher.nwpu.edu.cn/bushuhui

  被浏览次数:31739

基本信息 The basic information

布树辉

航空学院

博士研究生毕业

博士

教授

交通运输工程

bushuhui@nwpu.edu.cn

综合介绍 General Introduction

布树辉,西北工业大学教授,解放军信息工程大学客座教授,中国航空学会航电与空管分会委员,陕西省自动化学会智能机器人专业委员会委员,军队科学技术奖励评审。2001年毕业于湖南大学,2009年于日本筑波大学获得博士学位。2009至2011年任日本京都大学助理教授。2011年引进加入西北工业大学,并于2012,2016年破格晋升副教授、教授。主要研究方向有:自主无人机与机器人、图形与图像处理、机器学习及其应用等。在同时定位与构图(SLAM)、环境理解等方面取得了较为突出的成果,所研究的无人机实时地图在国际上有较大的影响力。在国外学术期刊、会议发表论文70多篇,申请并获得发明专利25项,主持多项国家、省部级基金项目,获省部级奖2项。


欢迎无人机、机器人、智能感知与控制方向做博士后的学者,联系邮箱 bushuhui@nwpu.edu.cn 。


更多信息请访问:http://www.adv-ci.com

工作经历 Work Experience

2016-至今,西北工业大学航空学院,教授(破格晋升)

2012-2016,西北工业大学航空学院,副教授(破格晋升)

2011-2012,西北工业大学航空学院,讲师

2009-2011,日本京都大学,助理教授

2006-2009,日本筑波大学系统信息工程专业博士学位

2004-2006,日本筑波大学系统信息工程专业硕士学位

2001-2003,TCL深圳多媒体研发中心

1997-2001,湖南大学学士学位


教育教学 Education And Teaching

1、机器学习,本科生课程,32学时  (课程主页:https://gitee.com/pi-lab/machinelearning_notebook

2、智能图像图形处理,研究生课程,40学时  (课程主页:http://www.adv-ci.com/blog/course/intelligent-igp/

3、飞机电子电气系统与维修,本科生课程, 32学时


更多课程介绍: http://www.adv-ci.com/blog/course/

招生信息 Admission Information

我们非常欢迎对无人机智能控制、机器学习、计算机视觉感兴趣,或者有理想、追求的学生加入我们。我们能够提供良好的学习环境和氛围,让您在研究生或博士期间得到最大的提升,从而走向《成功之道》。如果感兴趣请查阅:《实验室的研究方向介绍》《发表论文列表》 ;以及《实验室研究成果视频》。主要的研究方向包括:  

1、无人机智能导航与控制

2、机器学习与计算机视觉 

3、信号与图像处理  


具体要求:

1、 如果您有意向想加入我们,请至少提前2个月联系我们,以方便彼此了解对方;

2、仔细查阅实验室研究课题方向 https://gitee.com/pi-lab/pilab_research_fields ;发表论文列表:http://www.adv-ci.com/blog/publications/ ;以及实验室研究成果视频》,确认自己是否真的喜欢这个研究方向;

3、我们实验室要求比较高的编程能力,因此需要在1-2个月内,完成编程的基础学习和编程练习;我们坚持的理念是:Talk is cheap, show me the code! (空谈误国,实干兴邦);

4、请按照 https://gitee.com/pi-lab/learn_programming 里面的要求完成前4个阶段的编程练习,并将自己编写的程序等提交到gitee上;

5、如果有什么疑问,或者咨询可以直接发邮件到 bushuhui # nwpu.edu.cn,或者 bushuhui # foxmail.com


荣誉获奖 Awards Information

1、2018年全国高校商业精英挑战赛第六届创新创业竞赛全国总决赛一等奖,《通用性机器人开发平台》

2、2018年河南省第四界自然科学学术奖,Place Recognition Based on Deep Feature and Adaptive Weighting of Similarity Matrix

3、2017年教育部自然科学技术进步二等奖,机载系统综合测试与智能故障诊断技术

4、2012年吴亚军优秀青年教师奖 

5、2007年日本学术振兴会博士特别研究员奖励


科学研究 Scientific Research

研究项目:

1、国防科技创新特区,强干XXX控制,2019年

2、装备预先研究,无人XXX应用技术,2019年

3、中国电科,基于图像XXX研究,2018年

4、 基于混合深度网络的复杂三维场景解析研究,国家自然基金,2016年

5、模式识别国家重点实验室开放课题基金,面向智能设备的场景学习与感知,2014年

6、 基于曲面柔韧度的三维形状局部显著特征描述符研究,国家自然基金,2013年

7、 以局部显著特征为主导的高速图像重建方法研究,陕西省自然科学基金,2012年

8、日本科学研究基金,Research on Spatial-temporal Modulation based Photoacoustic Imaging,2010年

9、日本技术振兴基金,Innovative Techno-Hub for Integrated Medical Bio-imaging,2009年

10、日本文部科学省博士研究员科学基金,Next Generation Myocardial String Imaging: Ultrasound Based Strain Imaging,2007年

11、日本科学研究基金,Modeling Slow Slip Events on Subduction Plate Interfaces,2005年


教材/专著

《无人机测绘技术及应用》


代表论文

[1] Shuhui Bu, Qin Li, Pengcheng Han, Ke Li, “Mask-CDNet: A Mask based pixel change detection network,” Neurocomputing, vol. 378, pp. 166-178, 2020.

[2] Wei Wang, Yong Zhao, Pengcheng Han, Pengcheng Zhao, Shuhui Bu, “TerrainFusion: Real-time digital surface model reconstruction based on monocular SLAM,” IEEE International Conference on Intelligent Robot and System (iROS), 2019.

[3] Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han, “GSLAM: A General SLAM Framework and Benchmark,” ICCV 2019.

[4] Pengcheng Han, Cunbao Ma, Qing Li, Pengyu Leng, Shuhui Bu, Ke Li, “Aerial image change detection using dual regions of interest networks,” Neurocomputing, vol. 349, pp. 190-201, 2019.

[5] Zhizhong Han, Zhenbao Liu, Junwei Han, Chiman Vong, Shuhui Bu, C L Philip Chen, “Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 49, no. 2, pp. 481-494, 2019.

[6] Zhizhong Han, Zhenbao Liu, Chi-Man Vong, Yu-Shen Liu, Shuhui Bu, Junwei Han, CL Philip Chen, "Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network With Coupled Softmax," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 3049-3063, 2018.

[7] Zeyu Wang, Yanxia Wu, Shuhui Bu, Pengcheng Han, Guoyin Zhang, "Structural inference embedded adversarial networks for scene parsing," PloS one, vol. 13, no. 4, 2018. 

[8] Ke Li, Gong Cheng, Shuhui Bu, Xiong You, "Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 4, pp. 2337-2348, 2018.

[9] Ke Li, Changqing Zou, Shuhui Bu, Yun Liang, Jian Zhang, Minglun Gong, "Multi-modal feature fusion for geographic image annotation," Pattern Recognition, vol. 73, pp. 1-14, 2018. 

[10] Shuhui Bu, Lei Wang, Pengcheng Han, Zhenbao Liu, Ke Li, "3D shape recognition and retrieval based on multi-modality deep learning," Neurocomputing, vol. 259, pp. 183-193, 2017. 

[11] Shuhui Bu, Yong Zhao, Gang Wan, and Zhenbao Liu, "Map2DFusion: Real-time Incremental UAV Image Mosaicing based on Monocular SLAM," IEEE International Conference on Intelligent Robot and System (iROS), 2016.

[12] Qing Li, Ke Li, Xiong You, Shuhui Bu, Zhenbao Liu, “Place recognition based on deep feature and adaptive weighting of similarity matrix,” Neurocomputing, vol. 199, pp. 114-127, 2016.

[13] Shuhui Bu, Pengcheng Han, Zhenbao Liu, Junwei Han, “Scene parsing using inference Embedded Deep Networks,” Pattern Recognition, vol. 59, pp. 188-198, 2016. 

[14] Shuhui Bu, Yong Zhao, Gang Wan, Ke Li, Gong Cheng, Zhenbao Liu, “Semi-direct tracking and mapping with RGB-D camera for MAV,” Multimedia Tools and Applications, vol. 76, no. 3, pp. 4445-4469, 2016. 

[15] Xiwen Yao, Junwei Han, Lei Guo, Shuhui Bu, Zhenbao Liu, “A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF”, Neurocomputing, vol. 164, pp. 162-172, 2015.

[16] Shuhui Bu, Pengcheng Han, Zhenbao Liu, Junwei Han, Hongwei Ling, "Local Deep Feature Learning Framework for 3D Shape", Computer & Graphics, vol. 46, pp. 117-129, 2015.

[17] Gong Cheng, Junwei Han, Lei Guo, Zhenbao Liu, Shuhui Bu, Jinchang Ren, “Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 8, pp. 4238-4249, 2015.

[18] Shuhui Bu, Pengcheng Han, Zhenbao Liu, Junwei Han, Hongwei Ling, "Local Deep Feature Learning Framework for 3D Shape", Shape Modeling International, 2014. 

[19] Shuhui Bu, Zhenbao Liu, Pengcheng Han, Junwei Han, “Shift-Invariant Ring Feature for 3D Shape” Computer Graphics International, 2014.

[20] Shuhui Bu, Zhenbao Liu, Junwei Han, Jun Wu, RongrongJi, “Learning High-level Feature by Deep Belief Networks for 3D Model Retrieval and Recognition,” IEEE Transactions on Multimedia, vol.16, no.8, pp. 2151-2167, 2014. 

[21] Shuhui Bu, Shaoguang Cheng, Zhenbao Liu, Junwei Han, “Multi-modal Feature Fusion for 3D Shape Recognition and Retrieval,” IEEE Multimedia, vol. 21, no. 4, pp. 38-46, 2014.

[22] Shuhui Bu, Zhenbao Liu, Pengcheng Han, Junwei Han, “Shift-Invariant Ring Feature for 3D Shape” The Visual Computer, vol.30, no. 5, pp. 867-876, 2014.

[23] Zhizhong Han, Zhenbao Liu, Junwei Han, Shuhui Bu*, "3D Shape Creation by Style Transfer," The Visual Computer, vol. 30, no. 7, 2014. 

[24] Zhenbao Liu, Sicong Tang, WeiweiXu, Shuhui Bu, Junwei Han, Kun Zhou, “Automatic 3D Indoor Scene Updating with RGBD Cameras,” Computer Graphics Forum, vol. 33, no. 7, 2014. 

[25] Zhenbao Liu, CailiXie, Shuhui Bu, Xiao Wang, Junwei Han, Hongwei Ling, "Indirect Shape Analysis for 3D Shape Retrieval", Computer & Graphics, vol. 46, pp. 110-116, 2014. 

[26] Zhenbao Liu, Shuhui Bu*, Junwei Han, "Locality-constrained sparse patch coding for 3D shape retrieval," Neurocomputing, vol. 151, part 2, pp. 583-592, 2014. 

[27] Zhenbao Liu, Shuhui Bu, Junwei Han, "Human-Centered 3D Home Applications via Low-Cost RGBD Cameras," chapter 6 of book <<Computer Vision and Machine Learning with RGB-D Sensors>>, 2014.

[28] Shuhui Bu, Zhenbao Liu, Junwei Han, Jun Wu, “Superpixel segmentation based structural scene recognition,” Proceedings of the 21st ACM international conference on Multimedia, pp. 681-684, Barcelona, Spain, 2013. 

[29] Shuhui Bu, Zhenbao Liu, Tsuyoshi Shiina, Kazuhiko Fukutani, “Matrix compression and compressed sensing reconstruction for photoacoustic tomography,” Electronics and Electrical Engineering, vol. 18, no. 9, pp.101-104, 2012.

[30] Shuhui Bu, Zhenbao Liu, Tsuyoshi Shiina, Kengo Kondo, Makoto Yamakawa, Kazuhiko Fukutani, Yasuhiro Sommeda, and Yasufumi Asao, “Model-based reconstruction integrated with fluence compensation for photoacoustic tomography,” IEEE Transactions on Biomedical Engineering, vol. 59, no.5, pp.1354-1363, 2012. 

[31] Zhenbao Liu and Shuhui Bu, “A Scheme of Real Time Cloud Rendering on the GPU,” Information Journal, vol. 14, no. 6, pp.1983-1992, 2011. 

[32] Shuhui Bu, Kengo Kondo, Makoto Yamakawa, Tsuyoshi Shiina, Kazuhiko Fukutani, Yasuhiro Someda, Yasufumi Asao, “Adaptive and Quantitative Reconstruction Algorithm for PhotoacousticTomography,”Proceedings of SPIE 7899, 78992G,SPIE Photonics West, BiOS 2011, San Francisco, California, United States, 22 - 27 January 2011. 

[33] Zhenbao Liu, Shuhui Bu, Kun Zhou, Xiaoqian Sun, “Geometrically Attributed Binary Tree for 3D Shape Matching,” 25th IEEE Computer Graphics International Conference, Ottawa, Canada, 2011.

[34] Shuhui Bu, Makoto Yamakawa, and Tsuyoshi Shiina, “Myocardial Strain Imaging with High-Performance Adaptive Dynamic Grid Interpolation Method,” Japanese Journal of Applied Physics, vol. 49, no.7, pp. 07HF25-1--10, July, 2010.

[35] BunichiroShibazaki, Shuhui Bu, Takanori Matsuzawa, and Hitoshi Hirose, “Modeling the Activity of Short-term Slow Slip Events Along Deep Subduction Interfaces Beneath Shikoku, Southwest Japan,” Journal of Geophysical Research, vol. 115, B00A19, Apr. 2010.

[36] Zhenbao Liu, Shuhui Bu, Chao Zhang, Xiaojun Tang, “Filter Fourier Coefficients of Shape Projections for 3D Shape Retrieval,” Information Journal, Vol. 13, No. 4, 2010.

[37] Shuhui Bu, Makoto Yamakawa, Tsuyoshi Shiina, “Adaptive Depth Compensation Algorithm for PhotoacousticTomography,”Proceedings of the 2010 IEEE International Ultrasonics Symposium, San Diego, October 11-14, 2010. 

[38] Shuhui Bu, Tsuyoshi Shiina, Makoto Yamakawa, “A High Performance Spatio-Temporal Displacement Smoothing Method for Myocardial Strain Imaging,”Proceedings of the 2009 IEEE International Ultrasonics Symposium, pp. 1415-1418, Roma, September 20-23, 2009. 

[39] Shuhui Bu, Tsuyoshi Shiina, Makoto Yamakawa, Hotaka Takizawa, “Adaptive Dynamic Grid Interpolation: A Robust, High-Performance Displacement Smoothing Filter for Myocardial Strain Imaging,”Proceedings of the 2008 IEEE International Ultrasonics Symposium, pp. 753-756, Beijing, Nov., 2008.

[40] Makoto Yamakawa, Shuhui Bu, Tsuyoshi Shiina, “Robust Strain Estimation Using Adaptive Dynamic Grid-Interpolation Model,”Proceedings of the 2008 IEEE International Ultrasonics Symposium, pp. 2021-2024, Beijing, Nov., 2008.

[41] Shuhui Bu, Tsuyoshi Shiina, Makoto Yamakawa, Hotaka Takizawa, “3D Myocardial Strain Imaging: Improvement of Accuracy and Contrast by Dynamic Grid Interpolation,”Proceedings of the 2007 IEEE International Ultrasonics Symposium, New York, NY, Oct., 2007.

[42] Shuhui Bu, Tsuyoshi Shiina, Makoto Yamakawa, Hotaka Takizawa,“3D Myocardial Contraction Imaging Based on Dynamic Grid Interpolation: Theory and Simulation Analysis”, IEEJ Transactions on Electronics, Information and Systems, vol.127(c), no.10, 1732--1742, Oct., 2007.


社会兼职 Social Appointments

1、中国航空学会航电与空管分会,委员

2、陕西省自动化学会智能机器人专业委员会,委员

3、 IEEE、SPIE、ACM会员。

4、IEEE Transactions on Image Processing、ACM Multimedia、PR等期刊的审稿人。