袁非牛
(0)
  • 性别：男
  • 聘任技术职务：教授
  • 学历：博士研究生毕业
  • 联系电话：
  • 电子邮箱：yfn@shnu.edu.cn
  • 通讯地址：
  • 部门：信息与机电工程学院
  • 学位：工学博士学位
  • 毕业院校：中国科学技术大学
  • 办公地址：

研究方向：

袁非牛

博士，教授，博士生导师，副院长。

研究方向：人工智能、自然语言处理、数字人文、图像分割、目标检测

博士招生方向：数字人文、算法与计算复杂性(人工智能)

Email: yfn@ustc.edu.cn


个人简介

    袁非牛，男，教授，博士，博士生导师，副院长，校学科建设委员会委员，学科带头人，上海市重点研究基地“上海市中小学在线教育研究基地”执行主任。中国计算机学会(CCF)杰出会员，电气与电子工程师协会(IEEE)高级会员，中国图象图形学会(CSIG)会员，CCF和CSIG多媒体专委会执行委员，上海市图像图形学会理事。担任中科院1区IEEE Transactions on Multimedia副主编(Associate Editor)。获2019年度江西省自然科学奖一等奖、部级优秀毕业生、中国科学院院长优秀奖等。获ESI高被引论文(前1%)、ESI热点论文(前0.1%)等。连续多年同时入选美国斯坦福大学发布的全球前2%顶尖科学家榜单“年度科学影响力榜单”(2020-现在)和“终身科学影响力榜单”(2021-现在)。

    主持国家科技支撑计划子专题、国家自然科学基金项目、中国博士后基金等项目。参加国家重点研发计划等国家级重点、重大项目研究。在IEEE Transactions on Image Processing、IEEE Transactions on Biomedical Engineering、IEEE Transactions on Systems, Man, and Cybernetics: Systems、IEEE Transactions on Multimedia、Pattern Recognition、Medical Image Analysis、Information Sciences、计算机学报等上发表论文100多篇。其中，SCI论文70余篇，中科院1区近20篇。授权国内发明专利6项。

     开展产学研合作。成功为深圳神盾卫民警用设备有限公司、江西科泰华软件公司解决人脸识别、人像抠图、姿态估计和美颜等算法难题，这些算法已经应用在自助拍照机等产品上，并研发出基于Windows、Android和iOS系统的证件照抠图算法模块，相关产品已在在全国各地公安派出所应用，产生很好的社会效应和几千万元的经济效益。采用深度学习算法，提出一种专门的端对端人像抠图网络模型，实现了快速、准确的全自动人像抠图智能算法，并在二代身份证自助拍照系统、驾驶证自助拍照系统等广泛应用。



1. 学习工作经历

    1998和2001年分别获得合肥工业大学学士和硕士学位，2004年获得中国科学技术大学博士学位，2006年从中国科学技术大学火灾科学国家重点实验室博士后流动站出站。2006年进入江西财经大学工作，历任副教授、教授、硕士生导师、博士生导师等。2010至2012年，在新加坡科学技术研究局(A*STAR)任资深研究员(Senior Research Fellow)。2018进入上海师范大学工作，任教授、博士生导师、学科带头人等。

2. 学生科研团队

    指导毕业博士生4人，在读博士生3人，在读硕士生12人。

3. 主讲课程

    本科：深度学习及其编程、数据库应用、计算机基础、Visual C++程序开发、面向对象程序设计C++ (双语)

    硕士：深度学习、计算机视觉、现代信号处理、虚拟现实技术、模式识别、数字图像处理

    博士：人工智能前沿、深度神经网络

4. 发表的部分论文

[1].Feiniu Yuan, Yuhuan Peng, Qinghua Huang, Xuelong Li, A Bi-directionally Fused Boundary Aware Network for Skin Lesion Segmentation,  IEEE Transactions on Image Processing, vol. 33, Nov. 2024, pp.6340-6353. (中科院1区，影响因子13.7，CCF-A类)

[2].  Lin Zhang, Jing Wu, Feiniu Yuan(通讯作者), Yuming Fang,“Smoke-Aware Global-Interactive Non-local Network for Smoke Semantic Segmentation,” IEEE Transactions on Image Processing, vol. 33, Feb. 2024, pp. 1175-1187. (中科院1区，影响因子13.7，CCF-A类)

[3].  Feiniu Yuan, Lin Zhang, Xue Xia, Qinghua Huang, Xuelong Li, A Gated Recurrent Network with Dual Classification Assistance for Smoke Semantic Segmentation, IEEE Transactions on Image Processing, vol. 30, 2021, pp. 4409-4422. (中科院1区，影响因子13.7，CCF-A类)

[4].  Feiniu Yuan, Lin Zhang, Xue Xia, Qinghua Huang, Xuelong Li,  A Wave-shaped Deep Neural Network for Smoke Density Estimation,IEEE Transactions on Image Processing, vol. 29, 2020, pp. 2301-2313. (中科院1区，影响因子13.7，CCF-A类)

[5].  Chunlin Wen, Hui Huang, Yan Ma, Feiniu Yuan, Hongqing Zhu, “Dual-Guided Frequency Prototype Network for Few-Shot Semantic Segmentation,”IEEE Transactions on Multimedia, vol. 26, 2024, pp.8874-8888. (中科院1区，影响因子9.7，CCF-B类)

[6].  Feiniu Yuan, Changhong Xie, Biao Xiang, “A Comprehensive Feature Aggregation Network for Efficient Image Super-Resolution, ”  IEEE Transactions on Consumer Electronics, early access, online. (中科院2区，影响因子10.9)

[7].  Kang Li, Feiniu Yuan (通讯作者), Chunmei Wang,  Frequency-Space Interaction with Hierarchical Aggregation Network for Lightweight Smoke Image Segmentation,  IEEE Transactions on Consumer Electronics, vol. 7, no. 2, pp. 2632-2643. (中科院2区，影响因子10.9)

[8].  Yuming Fang, Guanqun Ding, Wenying Wen, Feiniu Yuan, Yong Yang, Zhijun Fang, Weisi Lin, Salient Object Detection by Spatiotemporal and Semantic Features in Real-Time Video Processing Systems,IEEE Transactions on Industrial Electronics, vol. 67, no. 11, 2020, pp. 9893-9903. (中科院1区，影响因子7.515)

[9].  Yuming Fang, Zhijun Fang, Feiniu Yuan, Yong Yang, Shouyuan Yang, Neal N. Xiong, “Optimized Multi-operator Image Retargeting Based on Perceptual Similarity Measure, ” IEEE Transactions on Systems, Man, and Cybernetics: Systems, Volume 47, Issue 11，pp. 2956-2966, Oct. 2017. (中科院1区，影响因子9.309)

[10].  Feiniu Yuan, Kai-Hsiang Chuang, and Jimin Liu, “A Variational Surface Deformation and Subdivision Based Modeling Framework for Noisy and Small n-Furcated Tube-Like Structures, ” IEEE Transactions on Biomedical Engineering, vol. 60, no. 6, pp. 1589-1598, Jun. 2013. (中科院2区，影响因子4.424)

[11]. Feiniu Yuan, Yanling Chi, Su Huang, and Jimin Liu, “Modeling n-Furcated Liver vessels From a 3D Segmented Volume Using Hole Making and Subdivision Methods,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 2, pp. 552-561, Feb. 2012. (中科院2区，影响因子4.424)

[12].Qinghua Huang, Yonghao Huang, Yaozhong Luo, Feiniu Yuan, Xuelong Li, Segmentation of breast ultrasound image with semantic classification of superpixels, Medical Image Analysis, vol. 61, 2020, pp. 1-13. (中科院1区，影响因子11.148)

[13].Chunmei Wang, Yuan Luo, Chunli Meng, Feiniu Yuan(通讯作者), An adaptive dual graph convolution fusion network for aspect-based sentiment analysis,  ACM Transactions on Asian and Low-Resource Language Information Processing, volume 23, issue 6, Jun 2024.

[14].Yuming Fang, Xiaoqiang Zhang, Feiniu Yuan (通讯作者), Nevrez Imamoglu, Haiwen Liu,  Video Saliency Detection by Gestalt Theory,  Pattern Recognition, vol. 96, no. 12, Dec. 2019, pp. 1-11. (中科院1区，影响因子7.6，CCF-B类)

[15].Feiniu Yuan, Zhengxiao Zhang, and Zhijun Fang, An Effective CNN and Transformer Complementary Network for Medical Image Segmentation, Pattern Recognition, vol. 136, Apr. 2023, 109228, pp. 1-12. (中科院1区，影响因子7.6，CCF-B类)

[16].Feiniu Yuan, Zeshu Dong, Lin Zhang, Xue Xia, Jinting Shi, “Cubic-cross convolutional attention and count prior embedding for smoke segmentation,” Pattern Recognition, vol. 131, Nov. 2022, 108902. (中科院1区，影响因子7.6，CCF-B类)

[17].Feiniu Yuan, Yu Zhou, Xue Xia, Xueming Qian, Jian Huang, “A Confidence Prior for Image Dehazing,” Pattern Recognition, vol. 119, 2021, pp. 1-16. (中科院1区，影响因子7.6，CCF-B类)

6. 主持的部分项目

[1].  融合微观机理和宏观形态信息的浓度场估计，国家自然科学基金，项目负责人，2023-2026。

[2].  基于Gabor卷积网络的多隐层高斯过程烟雾特征建模研究，国家自然科学基金，项目负责人，2019-2022。

[3].  烟雾流形建模及其在视频烟雾事件检测中的应用，国家自然科学基金，项目负责人，2014-2017。

[4].  面向烟雾检测的局部二元模式多级提升方法研究，国家自然科学基金，项目负责人，2011-2013。

（以下信息源于科研管理系统）

学术成果：
论文
  • [1] 袁非牛,Zhengxiao Zhang,Zhijun Fang. An Effective CNN and Transformer Complementary Network for Medical Image Segmentation. PATTERN RECOGNITION,2023,136(1):1-12.
  • [2] 袁非牛,Zhaoda Tang,汪春梅,Qinghua Huang,Jinting Shi. A Multiple Gated Boosting Network for Multi-Organ Medical Image Segmentation. IET Image Processing,2023,17(10):3028-3039.
  • [3] 袁非牛,Zeshu Dong,Lin Zhang,Xue Xia,Jinting Shi. Cubic-cross convolutional attention and count prior embedding for smoke segmentation. PATTERN RECOGNITION,2022,131(1):1-10.
  • [4] 袁非牛,Jinting Shi,Xue Xia,Qinghua Huang,Xuelong Li. Co-occurrence Matching of Local Binary Patterns for Improving Visual Adaption and Its application to Smoke Recognition. IET Computer Vision,2019,13(2):178–187.
  • [5] 袁非牛. Holistic learning-based high-order feature descriptor for smoke recognition. International Journal of Wavelets Multiresolution and Information Processing,2019,91(2):1-18.
  • [6] 袁非牛. Encoding Pairwise Hamming Distances of Local Binary Patterns for Visual Smoke Recognition. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,468(1):193-212.
  • [7] 袁非牛,Zhengxiao Zhang,Zhijun Fang. An Effective CNN and Transformer Complementary Network for Medical Image Segmentation. PATTERN RECOGNITION,2023,136(1):1-12.
  • [8] 袁非牛,Zhaoda Tang,汪春梅,Qinghua Huang,Jinting Shi. A Multiple Gated Boosting Network for Multi-Organ Medical Image Segmentation. IET Image Processing,2023,17(10):3028-3039.
  • [9] 袁非牛. Deep Smoke Segmentation. NEUROCOMPUTING,2019,357(1):248–260.
  • [10] 袁非牛. Convolutional Neural Networks based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition. MACHINE VISION AND APPLICATIONS,2019,30(2):345–358.
  • [11] 袁非牛. A Gated Recurrent Network with Dual Classification Assistance for Smoke Semantic Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30(5):4409-4422.
  • [12] 袁非牛. A Confidence Prior for Image Dehazing. PATTERN RECOGNITION,2021,119(7):1-16.
  • [13] 袁非牛. 自编码神经网络理论及应用综述. 计算机学报,2019,42(1):203-230.
  • [14] 袁非牛,Kang Li,汪春梅,Zhijun Fang. A Lightweight Network for Smoke Semantic Segmentation. PATTERN RECOGNITION,2023,137(1):1-11.
  • [15] 袁非牛. Holistic learning-based high-order feature descriptor for smoke recognition. International Journal of Wavelets Multiresolution and Information Processing,2019,91(2):1-18.
  • [16] 袁非牛,Yu Zhou,Xue Xia,Jinting Shi,Yuming Fang. Image dehazing based on a transmission fusion strategy by automatic image matting. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,194(5):1-11.
  • [17] 袁非牛,Yu Shi,Lin Zhang,Yuming Fang. A Cross-scale Mixed Attention Network for Smoke Segmentation. DIGITAL SIGNAL PROCESSING,2023,134(0):1-11.
  • [18] 袁非牛,Jinting Shi,Xue Xia,Qinghua Huang,Xuelong Li. Co-occurrence Matching of Local Binary Patterns for Improving Visual Adaption and Its application to Smoke Recognition. IET Computer Vision,2019,13(2):178–187.
  • [19] 袁非牛. Holistic learning-based high-order feature descriptor for smoke recognition. International Journal of Wavelets Multiresolution and Information Processing,2019,91(2):1-18.
  • [20] 袁非牛. Encoding Pairwise Hamming Distances of Local Binary Patterns for Visual Smoke Recognition. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,468(1):193-212.
  • [21] 袁非牛,Zeshu Dong,Lin Zhang,Xue Xia,Jinting Shi. Cubic-cross convolutional attention and count prior embedding for smoke segmentation. PATTERN RECOGNITION,2022,131(1):1-10.
  • [22] 袁非牛,Yu Zhou,Xue Xia,Jinting Shi,Yuming Fang. Image dehazing based on a transmission fusion strategy by automatic image matting. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,194(5):1-11.
  • [23] 袁非牛,Yu Shi,Lin Zhang,Yuming Fang. A Cross-scale Mixed Attention Network for Smoke Segmentation. DIGITAL SIGNAL PROCESSING,2023,134(0):1-11.
  • [24] 袁非牛,Xue Xia,Jinting Shi,Lin Zhang,黄继风. Learning multi-scale and multi-order features from 3D local differences for visual smoke recognition. INFORMATION SCIENCES,2018,468(1):193-212.
  • [25] 袁非牛,Kang Li,汪春梅,Zhijun Fang. A Lightweight Network for Smoke Semantic Segmentation. PATTERN RECOGNITION,2023,137(1):1-11.
  • [26] 袁非牛,Kang Li,汪春梅,Jinting Shi,Yaowen Zhu. Fully Extracting Feature Correlation Between and Within Stages for Semantic Segmentation. DIGITAL SIGNAL PROCESSING,2022,127(103578):1-11.
  • [27] 袁非牛,Jinting Shi,Xue Xia,Qinghua Huang,Xuelong Li. Co-occurrence Matching of Local Binary Patterns for Improving Visual Adaption and Its application to Smoke Recognition. IET Computer Vision,2019,13(2):178–187.
  • [28] 袁非牛. Holistic learning-based high-order feature descriptor for smoke recognition. International Journal of Wavelets Multiresolution and Information Processing,2019,91(2):1-18.
  • [29] 袁非牛. Encoding Pairwise Hamming Distances of Local Binary Patterns for Visual Smoke Recognition. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,468(1):193-212.
  • [30] 袁非牛. Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition. KSII Transactions on Internet and Information Systems,2019,13(4):2078-2093.
  • [31] 袁非牛. Deep Smoke Segmentation. NEUROCOMPUTING,2019,357(1):248–260.
  • [32] 袁非牛. Convolutional Neural Networks based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition. MACHINE VISION AND APPLICATIONS,2019,30(2):345–358.
  • [33] 袁非牛,Kang Li,汪春梅,Zhijun Fang. A Lightweight Network for Smoke Semantic Segmentation. PATTERN RECOGNITION,2023,137(1):1-11.
  • [34] 袁非牛,Kang Li,汪春梅,Jinting Shi,Yaowen Zhu. Fully Extracting Feature Correlation Between and Within Stages for Semantic Segmentation. DIGITAL SIGNAL PROCESSING,2022,127(103578):1-11.
  • [35] 袁非牛,Jinting Shi,Xue Xia,Qinghua Huang,Xuelong Li. Co-occurrence Matching of Local Binary Patterns for Improving Visual Adaption and Its application to Smoke Recognition. IET Computer Vision,2019,13(2):178–187.
  • [36] 袁非牛. Holistic learning-based high-order feature descriptor for smoke recognition. International Journal of Wavelets Multiresolution and Information Processing,2019,91(2):1-18.
  • [37] 袁非牛,Zeshu Dong,Lin Zhang,Xue Xia,Jinting Shi. Cubic-cross convolutional attention and count prior embedding for smoke segmentation. PATTERN RECOGNITION,2022,131(1):1-10.
  • [38] 袁非牛,Yu Zhou,Xue Xia,Jinting Shi,Yuming Fang. Image dehazing based on a transmission fusion strategy by automatic image matting. COMPUTER VISION AND IMAGE UNDERSTANDING,2020,194(5):1-11.
  • [39] 袁非牛,Yu Shi,Lin Zhang,Yuming Fang. A Cross-scale Mixed Attention Network for Smoke Segmentation. DIGITAL SIGNAL PROCESSING,2023,134(0):1-11.
  • [40] 袁非牛,Yaowen Zhu,Kang Li,Zhijun Fang,Jinting Shi. An Anisotropic Non-local Attention Network for Image Segmentation. MACHINE VISION AND APPLICATIONS,2022,33(23):1-15.
  • [41] 袁非牛,Xue Xia,Jinting Shi. Mixed Co-occurrence of Local Binary Patterns and Hamming-distance-based Local Binary Patterns. INFORMATION SCIENCES,2018,460–461(1):202-222.
  • [42] 袁非牛. Encoding Pairwise Hamming Distances of Local Binary Patterns for Visual Smoke Recognition. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,468(1):193-212.
  • [43] 袁非牛. 采用聚合Gabor核和局部二元模式的烟雾识别方法. 小型微型计算机系统,2019,40(4):827-833.
  • [44] 袁非牛. 自编码神经网络理论及应用综述. 计算机学报,2019,42(1):203-230.
  • [45] 袁非牛,Zhengxiao Zhang,Zhijun Fang. An Effective CNN and Transformer Complementary Network for Medical Image Segmentation. PATTERN RECOGNITION,2023,136(1):1-12.
  • [46] 袁非牛. Encoding Pairwise Hamming Distances of Local Binary Patterns for Visual Smoke Recognition. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,468(1):193-212.
  • [47] 袁非牛. Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition. KSII Transactions on Internet and Information Systems,2019,13(4):2078-2093.
  • [48] 袁非牛. Deep Smoke Segmentation. NEUROCOMPUTING,2019,357(1):248–260.
  • [49] 袁非牛. Convolutional Neural Networks based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition. MACHINE VISION AND APPLICATIONS,2019,30(2):345–358.
  • [50] 袁非牛. A Gated Recurrent Network with Dual Classification Assistance for Smoke Semantic Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30(5):4409-4422.
  • [51] 袁非牛. A Confidence Prior for Image Dehazing. PATTERN RECOGNITION,2021,119(7):1-16.
  • [52] 袁非牛,Yu Shi,Lin Zhang,Yuming Fang. A Cross-scale Mixed Attention Network for Smoke Segmentation. DIGITAL SIGNAL PROCESSING,2023,134(0):1-11.
  • [53] 袁非牛,Yaowen Zhu,Kang Li,Zhijun Fang,Jinting Shi. An Anisotropic Non-local Attention Network for Image Segmentation. MACHINE VISION AND APPLICATIONS,2022,33(23):1-15.
  • [54] 袁非牛,Xue Xia,Jinting Shi. Mixed Co-occurrence of Local Binary Patterns and Hamming-distance-based Local Binary Patterns. INFORMATION SCIENCES,2018,460–461(1):202-222.
  • [55] 袁非牛,Xue Xia,Jinting Shi,Lin Zhang,黄继风. Learning multi-scale and multi-order features from 3D local differences for visual smoke recognition. INFORMATION SCIENCES,2018,468(1):193-212.
  • [56] 袁非牛,Lin Zhang,Xue Xia,Qinghua Huang,Xuelong Li. A Wave-shaped Deep Neural Network for Smoke Density Estimation. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29(11):2301-2313.
  • [57] 袁非牛,Kang Li,汪春梅,Zhijun Fang. A Lightweight Network for Smoke Semantic Segmentation. PATTERN RECOGNITION,2023,137(1):1-11.
  • [58] 袁非牛,Kang Li,汪春梅,Jinting Shi,Yaowen Zhu. Fully Extracting Feature Correlation Between and Within Stages for Semantic Segmentation. DIGITAL SIGNAL PROCESSING,2022,127(103578):1-11.
  • [59] 袁非牛. 面向烟雾识别与纹理分类的Gabor网络. 中国图象图形学报,2019,42(2):269-281.
  • [60] 袁非牛. 采用聚合Gabor核和局部二元模式的烟雾识别方法. 小型微型计算机系统,2019,40(4):827-833.
  • [61] 袁非牛. 自编码神经网络理论及应用综述. 计算机学报,2019,42(1):203-230.
  • [62] 袁非牛,Zhengxiao Zhang,Zhijun Fang. An Effective CNN and Transformer Complementary Network for Medical Image Segmentation. PATTERN RECOGNITION,2023,136(1):1-12.
  • [63] 袁非牛. Deep Smoke Segmentation. NEUROCOMPUTING,2019,357(1):248–260.
  • [64] 袁非牛. Convolutional Neural Networks based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition. MACHINE VISION AND APPLICATIONS,2019,30(2):345–358.
  • [65] 袁非牛. A Gated Recurrent Network with Dual Classification Assistance for Smoke Semantic Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30(5):4409-4422.
  • [66] 袁非牛. A Confidence Prior for Image Dehazing. PATTERN RECOGNITION,2021,119(7):1-16.
  • [67] 袁非牛,Yaowen Zhu,Kang Li,Zhijun Fang,Jinting Shi. An Anisotropic Non-local Attention Network for Image Segmentation. MACHINE VISION AND APPLICATIONS,2022,33(23):1-15.
  • [68] 袁非牛,Xue Xia,Jinting Shi. Mixed Co-occurrence of Local Binary Patterns and Hamming-distance-based Local Binary Patterns. INFORMATION SCIENCES,2018,460–461(1):202-222.
  • [69] 袁非牛,Kang Li,汪春梅,Zhijun Fang. A Lightweight Network for Smoke Semantic Segmentation. PATTERN RECOGNITION,2023,137(1):1-11.
  • [70] 袁非牛,Kang Li,汪春梅,Jinting Shi,Yaowen Zhu. Fully Extracting Feature Correlation Between and Within Stages for Semantic Segmentation. DIGITAL SIGNAL PROCESSING,2022,127(103578):1-11.
  • [71] 袁非牛,Zhengxiao Zhang,Zhijun Fang. An Effective CNN and Transformer Complementary Network for Medical Image Segmentation. PATTERN RECOGNITION,2023,136(1):1-12.
  • [72] 袁非牛,Zhaoda Tang,汪春梅,Qinghua Huang,Jinting Shi. A Multiple Gated Boosting Network for Multi-Organ Medical Image Segmentation. IET Image Processing,2023,17(10):3028-3039.
  • [73] 袁非牛,Zeshu Dong,Lin Zhang,Xue Xia,Jinting Shi. Cubic-cross convolutional attention and count prior embedding for smoke segmentation. PATTERN RECOGNITION,2022,131(1):1-10.
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