His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Image processing. His Artificial intelligence research integrates issues from Sketch and Machine learning. The various areas that he examines in his Pattern recognition study include Embedding and Histogram, Image.
The Computer vision study combines topics in areas such as Distortion and Robustness. His biological study spans a wide range of topics, including Pixel and Algorithm. Xinbo Gao interconnects Image resolution, Regularization and Convolutional neural network in the investigation of issues within Iterative reconstruction.
Xinbo Gao spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Image and Feature extraction. His Artificial intelligence study frequently draws connections to other fields, such as Machine learning. His work carried out in the field of Pattern recognition brings together such families of science as Deep learning, Representation and Cluster analysis.
His research integrates issues of Algorithm and Data mining in his study of Cluster analysis. He combines topics linked to Robustness with his work on Computer vision. His Image quality study incorporates themes from Distortion, Metric and Human visual system model.
Xinbo Gao mainly focuses on Artificial intelligence, Pattern recognition, Image, Convolutional neural network and Feature. Much of his study explores Artificial intelligence relationship to Computer vision. His Pattern recognition research includes themes of Image quality and Cluster analysis.
His Image research focuses on Code and how it relates to Pixel. His Convolutional neural network research integrates issues from Motion estimation and Function. His study in Feature is interdisciplinary in nature, drawing from both Channel and Computation.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Image, Computer vision and Artificial neural network. His studies link Task with Artificial intelligence. His research in Pattern recognition intersects with topics in Image quality, Pixel, Feature and Metric.
The study incorporates disciplines such as Generator, Focus, Representation and Benchmark in addition to Image. His Computer vision research is multidisciplinary, incorporating perspectives in Sketch, Weighting and Robustness. His research on Artificial neural network also deals with topics like
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A survey of graph edit distance
Xinbo Gao;Bing Xiao;Dacheng Tao;Xuelong Li.
Pattern Analysis and Applications (2010)
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
Kaibing Zhang;Xinbo Gao;Dacheng Tao;Xuelong Li.
IEEE Transactions on Image Processing (2012)
Ordinal Regression with Multiple Output CNN for Age Estimation
Zhenxing Niu;Mo Zhou;Le Wang;Xinbo Gao.
computer vision and pattern recognition (2016)
A multi-frame image super-resolution method
Xuelong Li;Yanting Hu;Xinbo Gao;Dacheng Tao.
Signal Processing (2010)
A Comprehensive Survey to Face Hallucination
Nannan Wang;Dacheng Tao;Xinbo Gao;Xuelong Li.
International Journal of Computer Vision (2014)
Image Quality Assessment Based on Multiscale Geometric Analysis
Xinbo Gao;Wen Lu;Dacheng Tao;Xuelong Li.
IEEE Transactions on Image Processing (2009)
Image Super-Resolution With Sparse Neighbor Embedding
Xinbo Gao;Kaibing Zhang;Dacheng Tao;Xuelong Li.
IEEE Transactions on Image Processing (2012)
Semi-Supervised Nonnegative Matrix Factorization via Constraint Propagation
Di Wang;Xinbo Gao;Xiumei Wang.
IEEE Transactions on Systems, Man, and Cybernetics (2016)
Blind Image Quality Assessment via Deep Learning
Weilong Hou;Xinbo Gao;Dacheng Tao;Xuelong Li.
IEEE Transactions on Neural Networks (2015)
Spectrum Sharing in Cognitive Radio Networks—An Auction-Based Approach
Xinbing Wang;Zheng Li;Pengchao Xu;Youyun Xu.
systems man and cybernetics (2010)
Profile was last updated on December 6th, 2021.
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