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学术论文

论文成果:

  1. Zejun Hu, Bing Han(韩冰), Zhang, et al. Modeling of ultraviolet aurora intensity associated with interplanetary and geomagnetic parameters based on neural networks. Space Weather. 2021.(SCI检索)
  2. Bing Han(韩冰), Meng Zhang, Xinbo Gao, et al. Automatic Classification Method of Thyroid Pathological Images using Multiple Magnification Factors[J]. Neurocomputing, 2021, 460(8). (SCI检索)
  3. Yiyuan Han, Bing Han(韩冰), Xinbo Gao. Human scanpath estimation based on semantic segmentation guided by common eye fixation behaviors[J]. Neurocomputing, 2021, 453:705-717.(EI:20204209359165  SCI: 000663417700004)
  4. Kaojin Zhu, Bing Han(韩冰). MLEDet: Vehicle Detection in UAV Images[C]. IPMV 2020: 2020 2nd International Conference on Image Processing and Machine Vision. 2020:79-86. (EI:20205009615427)
  5. Bing Han(韩冰), Yunhao Wang, Zheng Yang, Xinbo Gao. Small-scale Pedestrian Detection Based on Deep Neural Network[J]. IEEE Transactions on Intelligent Transportation Systems,2020, 21(7), 3046-3055. DOI: 10.1109/TITS.2019.2923752. (SCI: 000545516200030, EI:20202808926535)
  6. Yiyuan Han, Bing Han(韩冰), Zejun Hu, Xinbo Gao, Lixia Zhang, Huigen Yang, and Bin Li. Prediction and Variation of Auroral Oval Boundary Based on Deep Learning Model and Space Physical Parameters[J]. Nonlinear Processes in Geophysics, 2020,27(1): 11-22. (SCI:000512305300001)
  7.  Wenliang Qiu, Xinbo Gao, Bing Han(韩冰). Saliency detection using a deep conditional random field network[J]. Pattern Recognition, 2020, 103 : 107266.  (SCI: 000530845000014)
  8. Wenliang Qiu, Xinbo Gao, Bing Han(韩冰). Video saliency detection via pairwise interaction[J]. Chinese Journal of Electronics, 2020. (SCI:000573608100003)
  9. Yujun Zhang, Bing Han(韩冰), Xinbo Gao, et al. Personalized Travel Recommendation via Multi-view Representation Learning[C]. Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, Cham, 2019: 97-109. (EI:20202008650079)
  10. Zheng Yang, Bing Han(韩冰), Guowei Wei, et al. Video saliency detection based on eye-movement guided region matching and LDP embedded optical flow[C]. International CCF Conference on Artificial Intelligence. Springer, Singapore, 2019, 1001(1): 117-130. (EI:20193507386491)
  11. Bing Han(韩冰), Lixia Zhang, Xinbo Gao. The region based MMTD energy function for image segmentation[J]. Multimedia Tools Appl. 2019,78(12): 16695-16726,.(SCI:000472094500043,EI: 20185206315269)
  12.  Ping Wang, Bing Han(韩冰), Jie Li, et al. Structural Reweight Sparse Subspace Clustering [J]. Neural Processing Letters, 2019, 49(3): 965-977.(SCI:000483206800007,EI: 20182305282955)
  13.  Wenliang Qiu, Xinbo Gao, Bing Han(韩冰). Eye Fixation assisted video saliency detection via total variation-based pairwise interaction[J]. IEEE Transactions on Image Processing, 2018,27(10): 4724 - 4739. (SCI: 000437028300002, EI: 20182305289252)
  14. Bing Han(韩冰), Yiyuan Han, Xinbo Gao, Lixia Zhang. Boundary constraint factor embedded localizing active contour model for medical image segmentation[J]. Journal of Ambient Intelligence and Humanized Computing.2019, 10(10): 3853-3862.  (SCI: 000487047400010, EI: 20183605772994)
  15. Bing Han(韩冰), Fuyue Chu, Xinbo Gao, et al. A Multi-size Kernels CNN with Eye Movement Guided Task-Specific Initialization for Aurora Image Classification[C]. CCF Chinese Conference on Computer Vision. Springer, Singapore, 2017: 533-544. (EI: 20175104550045)
  16. Wenliang Qiu, Xinbo Gao, Bing Han(韩冰). A superpixel-based CRF saliency detection approach[J]. Neurocomputing, 2017, 244: 19-32.  (SCI: 000345272500008)
  17. Bing Han(韩冰), Lixia Zhang, Xinbo Gao, Yating Song, An Improved Convolutional Auto-Encoder model for Aurora image classification[C]. Intelligence Science and Big Data Engineering. (IScIDE2016). 
  18. Bing Han(韩冰), Lixia Zhang, Xinbo Gao, et al. Embedded locality discriminant GPLVM for dimensionality reduction[C]. International Joint Conference on Neural Networks. IEEE, 2016:2431-2438. (EI: 20165203194115)
  19. Bing Han(韩冰), Xinbo Gao, Hui Liu, et al. Auroral Oval Boundary Modeling Based on Deep Learning Method[C]. International Conference on Intelligent Science and Big Data Engineering. Springer, Cham, 2015:96-106. ( EI:20154501492259)
  20. Bing Han(韩冰), Yating Song, Xinbo Gao, et al. Dynamic Aurora Sequence Recognition Using Volume Local Directional Pattern with local and global features[J]. Neurocomputing, 2015, 184:168-175. (SCI:000374364300016, EI:20155201727543)
  21. Zhonghua Jia, Bing Han(韩冰), Xinbo Gao. 2DPCANet: Dayside Aurora Classification Based on Deep Learning[C]. CCF Chinese Conference on Computer Vision, 2015, 323-334, Xi'an, China. (EI: 20155201735379)
  22. Xi. Yang, Xinbo. Gao, Jie Li, Bing Han(韩冰). A Shape-initialized and Intensity-adaptive Level Set Method for Auroral Oval Segmentation. Information Sciences[J]. 2014, 277: 794-807. (SCI: 000338390200048, EI:20142317797974)
  23. Bing Han(韩冰), Xiaojing Zhao, Xuelong Li, Dacheng Tao, Zejun Hu, Hongqiao Hu. Dayside Aurora Classification via BIFs-Based Sparse Representation Using Manifold Learning[J]. International Journal of Computer Mathematics, 2014, 91(11): 2415-2426. (SCI: 000345272500008)
  24. Liu Hui, Xinbo Gao, Bing Han(韩冰). An Automatic MSRM Method with a Feedback Based on Shape Information for Auroral Oval Segmentation[C]. 2013 International Conference on Intelligence Science and Big Data Engineering, Beijing, July 31- August 2, 2013. (EI: 20140517246462)
  25. Bing Han(韩冰), Xinbo Gao, Xuelong Li, Dacheng Tao.A Biological Inspired Features Based Saliency Map[C].  International Conference on Computing, Networking and Communications, 2012,371-37, February 2, Hawaii, USA.(EI: 20121714960595)
  26. Bing Han(韩冰), Xinbo Gao, Lili Tcheang, Vincent Walsh. Saliency based on cortex-like mechanisms[J]. International Journal of Computer Mathematics, 2011, 88(16-18):3942-39521.(SCI:000297692200012)
  27. Bing Han (韩冰), Xinbo Gao, Vincent Walsh, Lili Tetchung. A Saliency Map method with Cortex-like Mechanisms and Sparse Representation[C]. ACM International Conference on Image and Video Retrieval, 2010, 228-234. (EI: 20103413185333)
  28. Bing Han(韩冰), Lili Tetchung, Vincent Walsh, Xinbo Gao. A Novel Feature Combination Methods for Saliency-based Visual Attention[C]. 2009 Fifth International Conference on Natural Computation, 2009, 5: 18-22. (EI:20101512839793)
  29. Bing Han (韩冰), Xinbo Gao,, Hongbing Ji. A novel feature weighted fuzzy clustering method for shot boundary detection[C]. ICNC-FSKD, Lecture Notes in Artificial Intelligence, Springer-Verlag, 2006, Vol.4223: 471-480. (SCI: ISI000241106000055;EI:064510224201)
  30. Bing Han (韩冰), Xinbo Gao,, Hongbing Ji. A novel method of biomimetic pattern recognition for shot boundary detection[J]. Chinese Journal of Electronics, 2006, 15(4A): 941-944. (SCI:000241920200041;EI:064610241592)
  31. Bing Han (韩冰), Xinbo Gao,, Hongbing Ji. A novel feature weighted fuzzy clustering algorithm[J]. Journal of Computational Information Systems,2006,2(3): 1141-1146.(EI:070110339550)
  32. 8.Bing Han (韩冰), Xinbo Gao,, Hongbing Ji. Automatic news audio classification based on selective ensemble SVMs[C]. ISNN’05, Lecture Notes in Computer Science, Springer-Verlag, 2005, Vol. 3497, 363-368. (SCI:000230167200059;EI:05399382092)
  33. Bing Han (韩冰), Xinbo Gao,, Hongbing Ji. A unified fr[ant]amework for shot boundary detection[C]. CIS’05, Lecture Notes in Artificial Intelligence, Springer-Verlag, Vol.3801,2005:997-1002 (SCI:000234873700148;EI:06229909304)
  34. 王平,李洁,韩冰,胡泽骏,高新波,刘建军,胡红桥。极区电离层对流速度的浅层神经网络建模与分析. 地球物理学报,2021.(SCI检索)
  35. 韩冰,高新波,胡泽骏等,人工智能助力极地科考,中国人工智能学会通讯,2018,1:22-31.
  36. 严月,韩冰,高新波,连慧芳,基于粘性流体粒子运动模型的极光视频分类.计算机学会人工智能会议(CCFAI2017),中国云南昆明
  37. 韩冰, 贾中华, 高新波. 改进的主成分分析网络极光图像分类方法. 西安电子科技大学学报(自然科学版), 2017, 44(1):83-88. (EI: 20171303500029)
  38. 魏国威, 韩冰, 仇文亮, 高新波. 基于超像素块的视频显著区域提取. 中国数据挖掘会议(CCDM2016), 中国广西桂林.
  39. 宋亚婷, 韩冰, 高新波. 基于张量动态纹理模型的极光视频分类. 南京大学学报(自然科学), 2016, 52(1):184-193.
  40. 王秀梅, 韩冰, 高新波,. 基于环形局部方向模式的弧状极光序列检测. 计算机科学与探索, 2015, 9(5):586-593.
  41. 韩冰, 廖谦, 高新波. 基于空时极向LBP的极光序列事件检测.软件学报,2014,25(9): 2172-2179. (EI201441094917)
  42. 王平,韩冰,高新波,刘慧. 基于BP神经网络的极光卵位置预测第十四届多值逻辑与模糊逻辑学术年会, (MVFL2014), 中国山东济南.
  43. 杨曦, 李洁, 韩冰, 高新波. 一种分层小波模型下的极光图像分类算法. 西安电子科技大学学报,2013,40(2): 18-24.
  44. 韩冰, 杨辰, 高新波. 融合显著信息的LDA极光图像分类. 软件学报,2013,24(11):2758-2766.(EI: 20140117166623)
  45. 韩冰, 仇文亮. 一种特征显著性编码的极光图像分类方法. 西安电子科技大学学报,2013,40(6):180-186. (EI: 20140117165510)
  46. 韩冰, 高新波, 李洁. 改进视皮层视觉机制的视觉注意力模型. 计算机科学与探索,2011,5(11) :1014-1020.
  47. 韩冰,高新波,Lili Tcheang Vincent Walsh. 基于变精度粗糙加权聚类的视觉注意力模型. 第十二届多值逻辑与模糊逻辑学术年会, 2010, 300-309.
  48. 韩冰, 高新波, 姬红兵. 基于模糊粗糙集的新闻镜头分割方法. 电子学报, 2006,34(6):1085-1089.(EI:063610101197)

专利成果:

  1. 基于深度学习的甲状腺癌病理图像分类方法    申请号:202011259621.3
  2. 基于深度学习的遮挡行人检测方法  申请号:201910286482.4
  3. 基于生成式对抗网络的极光卵强度图像建模方法   申请号:201910347210.0
  4. 基于目标特征敏感性和深度学习的车辆跟踪方法   授权号:ZL201911408023.5
  5. 基于深度学习的甲状腺乳头状癌病理图像分类方法     申请号:201911415563.6
  6. 基于多模态异质信息的个性化推荐方法    申请号:210910544396.9
  7. 基于条件随机场的路标识别方法     申请号:201810399451.5
  8. 基于分级注视图和条件随机场的眼动注视图预测方法    授权号:ZL201810360076.3
  9. 基于空域分类网路和时域分类网络融合的视频分类方法    授权号:ZL201810475657.1
  10. 基于深度学习的小尺寸行人目标检测方法   授权号:ZL201810577466.6
  11. 基于粘性流体粒子运动模型的视频序列分类方法   授权号:ZL201710189229.8
  12. 基于中介真值程度度量的局部主动轮廓图像分割方法    授权号:ZL201710189231.5
  13. 融合静态信息与动态信息的视频序列分割方法    授权号:ZL201710190220.9
  14. 基于球状鲁棒序列局部二值化模式的序列分类方法   授权号:ZL201710365058.X
  15. 基于嵌入边界约束因子的局部主动轮廓图像分割方法   授权号:ZL201710865220.4
  16. 基于贝叶斯融合的视频显著性检测方法   授权号:ZL201710570472.4
  17. 利用显著信息引导的图像不规则马赛克拼接方法     授权号:ZL201610224497.4
  18. 基于区域分割的视频显著性检测方法   授权号:ZL2016102249728
  19. 基于深度学习的极光卵位置确定方法   授权号: ZL2015101023486
  20. 基于动态纹理模型表征的极光视频分类方法   授权号:ZL2015104123838
  21. 基于张量动态纹理模型的极光视频分类方法   授权号:ZL2015104125250
  22.  基于深度学习二维主成分分析网络的极光图像检测方法   授权号:ZL2015104187030
  23. 基于优化卷积自动编码网络的极光图像分类方法     授权号:ZL201510976336.6
  24. 遥感图像变化检测方法   授权号:ZL201410441207.2
  25. 基于改进视觉注意模型的序列图像显著区域检测方法   授权号:ZL2014103177395
  26. 基于空时极向局部二值模式的极光图像序列分类方法   授权号: ZL201410160536.X
  27. 基于亮度自适应水平集的极光卵分割方法   授权号: ZL2012103886956
  28. 基于视觉注意力模型的自然场景目标检测方法   授权号: ZL201010537951.4