His primary areas of investigation include Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Convolutional neural network. As part of his studies on Artificial intelligence, Yue Huang often connects relevant subjects like Algorithm. His research on Computer vision frequently links to adjacent areas such as Probabilistic logic.
The Pattern recognition study combines topics in areas such as Bayesian network, Iterative reconstruction, Compressed sensing and Bayesian inference. Yue Huang has researched Convolutional neural network in several fields, including Feature extraction, Image segmentation, Kernel and Robustness. His Deep learning study combines topics in areas such as Discriminative model, Feature learning and Categorization.
Yue Huang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Image. Segmentation, Artificial neural network, Compressed sensing, Convolutional neural network and Feature are the subjects of his Artificial intelligence studies. His study in Convolutional neural network is interdisciplinary in nature, drawing from both Pyramid, Training set, Data mining and Pyramid.
The concepts of his Pattern recognition study are interwoven with issues in Supervised learning and Noise reduction. His biological study spans a wide range of topics, including Transfer of learning, Labeled data and Robustness. In the field of Image, his study on Single image overlaps with subjects such as Naturalness and Underwater.
Yue Huang focuses on Artificial intelligence, Pattern recognition, Deep learning, Discriminative model and Convolutional neural network. As part of his studies on Artificial intelligence, Yue Huang often connects relevant areas like Machine learning. His Pattern recognition research is multidisciplinary, incorporating perspectives in Normalization, Feature, Interpolation, Supervised learning and Generative grammar.
Yue Huang interconnects Robustness and Computer vision in the investigation of issues within Deep learning. Yue Huang combines subjects such as Classifier, Feature and Feature vector with his study of Discriminative model. His Convolutional neural network research includes themes of Algorithm, Error detection and correction, Compressed sensing and Labeled data.
Artificial intelligence, Pattern recognition, Feature extraction, Segmentation and Artificial neural network are his primary areas of study. Yue Huang does research in Artificial intelligence, focusing on Deep learning specifically. His work deals with themes such as Annotation, Entropy and Precision medicine, which intersect with Deep learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Adversarial system, Image synthesis, Image, Translation and Supervised learning. His work on Image segmentation as part of general Segmentation study is frequently connected to Weighted network, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His biological study deals with issues like Pyramid, which deal with fields such as Convolutional neural network.
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 review of the use of recycled solid waste materials in asphalt pavements
Yue Huang;Roger N. Bird;Oliver Heidrich.
Resources Conservation and Recycling (2007)
Removing Rain from Single Images via a Deep Detail Network
Xueyang Fu;Jiabin Huang;Delu Zeng;Yue Huang.
computer vision and pattern recognition (2017)
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
Xueyang Fu;Delu Zeng;Yue Huang;Xiao-Ping Zhang.
computer vision and pattern recognition (2016)
Development of a life cycle assessment tool for construction and maintenance of asphalt pavements
Yue Huang;Roger Bird;Oliver Heidrich.
Journal of Cleaner Production (2009)
A fusion-based enhancing method for weakly illuminated images
Xueyang Fu;Delu Zeng;Yue Huang;Yinghao Liao.
Signal Processing (2016)
PanNet: A Deep Network Architecture for Pan-Sharpening
Junfeng Yang;Xueyang Fu;Yuwen Hu;Yue Huang.
international conference on computer vision (2017)
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Chuan Shi;Xiangnan Kong;Yue Huang;Philip S. Yu.
IEEE Transactions on Knowledge and Data Engineering (2014)
A retinex-based enhancing approach for single underwater image
Xueyang Fu;Peixian Zhuang;Yue Huang;Yinghao Liao.
international conference on image processing (2014)
A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation
Xueyang Fu;Yinghao Liao;Delu Zeng;Yue Huang.
IEEE Transactions on Image Processing (2015)
Progressive Feature Alignment for Unsupervised Domain Adaptation
Chaoqi Chen;Weiping Xie;Wenbing Huang;Yu Rong.
computer vision and pattern recognition (2019)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: