His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Robustness and Convolutional neural network. His study in Visualization, Deep learning, Feature extraction, Video tracking and Cognitive neuroscience of visual object recognition is done as part of Artificial intelligence. His work on RGB color model, Image, Camera resectioning and Object is typically connected to Line as part of general Computer vision study, connecting several disciplines of science.
His RGB color model study incorporates themes from Image sensor and Gesture recognition. His Pattern recognition research is multidisciplinary, relying on both Semantics and Hash function. The study incorporates disciplines such as Change detection, Filter, Pruning, Algorithm and Convolution in addition to Convolutional neural network.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Image. His study connects Machine learning and Artificial intelligence. His Pattern recognition research incorporates elements of Convolution and Representation.
His Computer vision study frequently links to related topics such as Visualization. In his work, Pruning is strongly intertwined with Algorithm, which is a subfield of Convolutional neural network. His research investigates the connection between Image and topics such as Hash function that intersect with problems in Binary code.
Jungong Han mostly deals with Artificial intelligence, Pattern recognition, Convolutional neural network, Benchmark and Computer vision. Feature, Segmentation, Image, RGB color model and Feature extraction are subfields of Artificial intelligence in which his conducts study. Jungong Han interconnects Image resolution, Field and Deep learning in the investigation of issues within Image.
His work on Discriminative model as part of general Pattern recognition research is frequently linked to Property, bridging the gap between disciplines. His biological study spans a wide range of topics, including Overhead, Pruning, Network model, Data compression ratio and Algorithm. Jungong Han has included themes like DUAL and Pattern recognition in his Computer vision study.
Artificial intelligence, Convolutional neural network, Image, Pattern recognition and Feature extraction are his primary areas of study. His research in Artificial intelligence intersects with topics in Machine learning and Computer vision. His Convolutional neural network study also includes
His Image research incorporates themes from Network model, Deep learning, Reduction and Pruning. His Pattern recognition research includes themes of Exploit, Pascal and Benchmark. His work deals with themes such as Feature based, Leverage, Natural language processing, Text retrieval and Iterative method, which intersect with Feature extraction.
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.
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Jungong Han;Ling Shao;Dong Xu;Jamie Shotton.
IEEE Transactions on Systems, Man, and Cybernetics (2013)
Gabor Convolutional Networks
Shangzhen Luan;Chen Chen;Baochang Zhang;Jungong Han.
IEEE Transactions on Image Processing (2018)
Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review
Qiang Zhang;Yi Liu;Rick S. Blum;Jungong Han.
Information Fusion (2018)
Automatic video-based human motion analyzer for consumer surveillance system
Weilun Lao;Jungong Han.
IEEE Transactions on Consumer Electronics (2009)
Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining
Dingwen Zhang;Junwei Han;Jungong Han;Ling Shao.
IEEE Transactions on Neural Networks (2016)
RGB-D datasets using microsoft kinect or similar sensors: a survey
Ziyun Cai;Jungong Han;Li Liu;Ling Shao.
Multimedia Tools and Applications (2017)
Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment
Jungong Han;E. J. Pauwels;P. M. de Zeeuw.
IEEE Transactions on Consumer Electronics (2012)
Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier
Baochang Zhang;Yun Yang;Chen Chen;Linlin Yang.
IEEE Transactions on Image Processing (2017)
Cross-View Retrieval via Probability-Based Semantics-Preserving Hashing
Zijia Lin;Guiguang Ding;Jungong Han;Jianmin Wang.
IEEE Transactions on Systems, Man, and Cybernetics (2017)
Video-Based Fall Detection in the Home Using Principal Component Analysis
Lykele Hazelhoff;Jungong Han.
advanced concepts for intelligent vision systems (2008)
Profile was last updated on December 6th, 2021.
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