Jianzhuang Liu mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Facial recognition system. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Point. His work carried out in the field of Pattern recognition brings together such families of science as Convolution and Deep learning.
His Computer vision research includes themes of Computational geometry and Sparse approximation. His research in Algorithm intersects with topics in Constrained clustering, Graph theory, Mathematical optimization, Bounding overwatch and Real image. His Facial recognition system research incorporates themes from Gabor filter and Invariant.
Jianzhuang Liu focuses on Artificial intelligence, Pattern recognition, Computer vision, Object and Image. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Classifier and other disciplines. His Pattern recognition study incorporates themes from Facial recognition system, Feature and Benchmark.
He has researched Facial recognition system in several fields, including Cognitive neuroscience of visual object recognition and Linear discriminant analysis. His biological study deals with issues like Sketch, which deal with fields such as Visual Word. The study incorporates disciplines such as Pixel and Moiré pattern in addition to Image.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Machine learning. He merges Artificial intelligence with Process in his research. His study in Pattern recognition is interdisciplinary in nature, drawing from both Perspective, Similarity, Feature and Benchmark.
His work in Computer vision addresses issues such as Matching, which are connected to fields such as Contrast and End-to-end principle. His Convolutional neural network study also includes fields such as
His primary areas of investigation include Artificial intelligence, Pattern recognition, Convolutional neural network, Process and Artificial neural network. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision. His Computer vision research incorporates elements of Data modeling, Key and Training set.
The Pattern recognition study combines topics in areas such as Image, Translation and Resolution. Projection, Filter, Circulant matrix and Algorithm is closely connected to Backpropagation in his research, which is encompassed under the umbrella topic of Convolutional neural network. He combines subjects such as Channel, Differentiable function and Reduction with his study of Artificial neural network.
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Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor
Baochang Zhang;Yongsheng Gao;Sanqiang Zhao;Jianzhuang Liu.
IEEE Transactions on Image Processing (2010)
2D Shape Matching by Contour Flexibility
Chunjing Xu;Jianzhuang Liu;Xiaoou Tang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
Robust 3D Face Recognition by Local Shape Difference Boosting
Yueming Wang;Jianzhuang Liu;Xiaoou Tang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Gabor Convolutional Networks
Shangzhen Luan;Chen Chen;Baochang Zhang;Jungong Han.
IEEE Transactions on Image Processing (2018)
A spatial-temporal approach for video caption detection and recognition
Xiaoou Tang;Xinbo Gao;Jianzhuang Liu;Hongjiang Zhang.
IEEE Transactions on Neural Networks (2002)
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Zhenguo Li;Jianzhuang Liu;Xiaoou Tang.
international conference on machine learning (2008)
Hidden Factor Analysis for Age Invariant Face Recognition
Dihong Gong;Zhifeng Li;Dahua Lin;Jianzhuang Liu.
international conference on computer vision (2013)
Constrained clustering via spectral regularization
Zhenguo Li;Jianzhuang Liu;Xiaoou Tang.
computer vision and pattern recognition (2009)
Graph-based method for face identification from a single 2D line drawing
Jianzhuang Liu;Yong Tsui Lee.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries
Bo Li;Yijuan Lu;Chunyuan Li;Afzal Godil.
Computer Vision and Image Understanding (2015)
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