His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Cluster analysis and Image. His work in Hyperspectral imaging, Image processing, Support vector machine, Synthetic aperture radar and Deep learning is related to Artificial intelligence. His study brings together the fields of Artificial neural network and Pattern recognition.
His Cluster analysis research includes themes of Discriminative model and Non-negative matrix factorization. As part of the same scientific family, Shuyuan Yang usually focuses on Image, concentrating on Iterative reconstruction and intersecting with Sample and Superresolution. In his study, Graphical model, Kernel and Feature is inextricably linked to Pixel, which falls within the broad field of Feature extraction.
Shuyuan Yang mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Pixel and Synthetic aperture radar. His study in Artificial intelligence focuses on Sparse approximation, Image, Hyperspectral imaging, Feature extraction and Cluster analysis. His study explores the link between Hyperspectral imaging and topics such as Dimensionality reduction that cross with problems in Subspace topology.
His study in Feature extraction is interdisciplinary in nature, drawing from both Feature, Artificial neural network, Computational complexity theory, Deep learning and Kernel. His Pattern recognition study incorporates themes from Contextual image classification and Compressed sensing. Shuyuan Yang has included themes like Change detection, Image segmentation and Speckle pattern in his Synthetic aperture radar study.
His main research concerns Artificial intelligence, Pattern recognition, Pixel, Deep learning and Convolutional neural network. His work is connected to Contextual image classification, Image, Feature extraction, Artificial neural network and Multispectral image, as a part of Artificial intelligence. His Image study is concerned with the field of Computer vision as a whole.
Feature learning is closely connected to Feature in his research, which is encompassed under the umbrella topic of Pattern recognition. His Pixel study combines topics from a wide range of disciplines, such as Classifier, Panchromatic film, Fuzzy logic, Speckle pattern and Robustness. His work in Deep learning covers topics such as Data set which are related to areas like Contrast and Matrix decomposition.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Pixel and Artificial neural network. His Artificial intelligence research focuses on Convolutional neural network and Change detection. His work in Change detection addresses subjects such as Synthetic aperture radar, which are connected to disciplines such as Speckle pattern, Discriminative model, Similarity measure, Image processing and Kernel.
His work carried out in the field of Pattern recognition brings together such families of science as Subspace topology and Cluster analysis. The study incorporates disciplines such as Contextual image classification, Object, Feature extraction and Preprocessor in addition to Deep learning. He interconnects Image resolution, Panchromatic film, Multispectral image, Remote sensing and Upsampling in the investigation of issues within Pixel.
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 quantum particle swarm optimization
Shuyuan Yang;Min Wang;Licheng jiao.
congress on evolutionary computation (2004)
A Survey of Deep Learning-Based Object Detection
Licheng Jiao;Fan Zhang;Fang Liu;Shuyuan Yang.
IEEE Access (2019)
Image fusion based on a new contourlet packet
Shuyuan Yang;Min Wang;Licheng Jiao;Ruixia Wu.
Information Fusion (2010)
Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding
Shuyuan Yang;Min Wang;Yiguang Chen;Yaxin Sun.
IEEE Transactions on Image Processing (2012)
Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification
Licheng Jiao;Miaomiao Liang;Huan Chen;Shuyuan Yang.
IEEE Transactions on Geoscience and Remote Sensing (2017)
POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information
Fang Liu;Licheng Jiao;Biao Hou;Shuyuan Yang.
IEEE Transactions on Geoscience and Remote Sensing (2016)
Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Nuo Tong;Nuo Tong;Shuiping Gou;Shuyuan Yang;Dan Ruan.
Medical Physics (2018)
Semi-Supervised Hyperspectral Image Classification Using Spatio-Spectral Laplacian Support Vector Machine
Lixia Yang;Shuyuan Yang;Penglei Jin;Rui Zhang.
IEEE Geoscience and Remote Sensing Letters (2014)
Fusion of multispectral and panchromatic images based on support value transform and adaptive principal component analysis
Shuyuan Yang;Min Wang;Licheng Jiao.
Information Fusion (2012)
Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN
Shuyuan Yang;Min Wang;YanXiong Lu;Weidong Qi.
Signal Processing (2009)
Profile was last updated on December 6th, 2021.
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