Kai-Fu Yang / 杨开富

Associate Research Professor, 副研究员

University of Electronic Science and Technology of China

Email: yangkf [AT] uestc [DOT] edu [DOT] cn

 
 

 

 

Research Interests:

Brain-inspired Computer Vision: My research is aiming at building brain-inspired algorithms for image processing and computer vision applications such as image enhancement, color constancy, and scene understanding. Focus on the computational mechanisms in the visual system (e.g., visual search, color processing, etc.).

Cognitive Computing: To explore the underlying computational theory of visual perception and learning, and to build more powerful machine learning system based on the basic principles of cognitive computing. I also pay attention to the applications of pattern recognition and machine learning methods in vision research.

 

 

Publications:

Image processing & Computer Vision

  1. Kai-Fu Yang, Hui Li, Hulin Kuang, Chao-Yi Li, and Yong-Jie Li*. An Adaptive Method for Image Dynamic Range Adjustment. IEEE Trans. Circuits and Systems for Video Technology (TCSVT), 29 (3)：640-652，2019. [pdf]

  2. Hulin Kuang, Kai-Fu Yang, Yong-Jie Li*, Leanne Lai Hang Chan*, Hong Yan. Bayes Saliency Based Object Proposal Generator for Nighttime Traffic Images.IEEE Trans. Intelligent Transportation Systems(TITS),19(3):814-825, 2018. [pdf]

  3. Kai-Fu Yang, Hui Li, Chao-Yi Li, and Yong-Jie Li*. A Unified Framework for Salient Structure Detection by Contour-Guided Visual Search. IEEE Trans. Image Processing (TIP), 25(8): 3475-3488, 2016. [pdf] [code]

  4. Tao Deng, Kaifu Yang, Yongjie Li*, Hongmei Yan*. Where Does the Driver Look? Top-Down Based Saliency Detection in a Traffic Driving Environment. IEEE Trans. Intelligent Transportation Systems (TITS), 17 (7): 2051-2062, 2016.[pdf]

  5. Kai-Fu Yang, Shao-Bing Gao, Yong-Jie Li. Efficient Illuminant Estimation for Color Constancy Using Grey Pixels. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [pdf] <project page>

  6. Kai-Fu Yang, Shao-Bing Gao, Ce-Feng Guo, Chao-Yi Li, Yong-Jie Li*. Boundary Detection Using Double-Opponency and Spatial Sparseness Constraint. IEEE Trans. Image Processing (TIP), 24(8):2565-2578, 2015. [pdf] <project page>

  7. Shao-Bing Gao, Kai-Fu Yang, Chao-Yi Li, Yong-Jie Li*. Color Constancy Using Double-Opponency.  IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 37(10): 1973-1985,2015. [pdf] <project page>

  8. Kai-Fu Yang, Chao-Yi Li, and Yong-Jie Li*. Multifeature-based Surround Inhibition Improves Contour Detection in Natural Images. IEEE Trans. Image Processing (TIP), 23(12):5020-5032,2014. [pdf] <project page>

  9. Shaobing Gao, Wangwang Han, Kaifu Yang, Chaoyi Li, Yongjie Li. Efficient Color Constancy with Local Surface Reflectance Statistics. Proceeding of European Conference on Computer Vision (ECCV), Part II, LNCS 8690, 2014, PP.158-173 [pdf] <project page>

  10. Kaifu Yang, Shaobing Gao, Chaoyi Li, Yongjie Li. Efficient Color Boundary Detection with Color-opponent Mechanisms. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp2810-2817. [pdf] <project page>

  11. Shaobing Gao, Kaifu Yang, Chaoyi Li, and Yongjie Li. A Color Constancy Model with Double-Opponency Mechanisms. Proceeding of IEEE International Conference on Computer Vision (ICCV), pp.929-936, 2013. [pdf] <project page>

  12. Chi Zeng, Yongjie Li*, Kaifu Yang, Chaoyi Li. Contour detection based on a non-classical receptive field model with butterfly-shaped inhibition subregions. Neurocomputing, 74：1527–1534, 2011.
 
 

Neural & Cognitive Computing

  1. Hong-Wen Cao,Kai-Fu Yang, Hong-Mei Yan*. Character Decomposition and Transposition of Chinese Compound Words in the Right and Left Visual Fields. i-perception, p1–16, 2016, doi: 10.1177/2041669516675366. [pdf]

  2. Kai-Fu Yang, Chao-Yi Li, and Yong-Jie Li*. Potential Roles of the Interaction Between Model V1 Neurons with Orientation-Selective and Non-selective Surround Inhibition in Contour Detection. Frontiers in Neural Circuits, 9:30, 2015. [pdf]
  
 

Medical Image Analysis

  1. Yang Chen, Ming Zhang, Hong-Mei Yan, Yong-Jie Li, Kai-Fu Yang*. A New Ultrasound Speckle Reduction Algorithm Based on Superpixel Segmentation and Detail Compensation. Applied Sciences, 9(8),1693, 2019. [pdf]

  
 

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 latest update： 02-May-2019