Qiang Wu spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Biometrics and Gait. The Artificial intelligence study combines topics in areas such as Machine learning and Identification. As part of one scientific family, Qiang Wu deals mainly with the area of Pattern recognition, narrowing it down to issues related to the Feature, and often Metric space, Metric and Quantization.
His Computer vision study frequently links to related topics such as Discriminative model. Qiang Wu has included themes like Feature and Similarity in his Biometrics study. His Pixel study combines topics from a wide range of disciplines, such as Image resolution and Spiral.
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Pixel and Feature extraction. Qiang Wu works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Classifier, as a part of the same area of interest. His Edge detection, Biometrics, Depth map, Image segmentation and Feature detection investigations are all subjects of Computer vision research.
His study in the fields of Support vector machine under the domain of Pattern recognition overlaps with other disciplines such as Gait. His studies examine the connections between Pixel and genetics, as well as such issues in Algorithm, with regards to Point cloud. His Feature research is multidisciplinary, incorporating elements of Discriminative model and Closed captioning.
Qiang Wu focuses on Artificial intelligence, Pattern recognition, Feature, Computer vision and Machine learning. Artificial intelligence is closely attributed to Generalization in his study. Qiang Wu has researched Pattern recognition in several fields, including Matching, Representation and Bilinear interpolation.
The concepts of his Feature study are interwoven with issues in Fractal dimension and Discriminative model. In general Computer vision study, his work on Vessel segmentation often relates to the realm of Retinal, Highly skilled and Livestock, thereby connecting several areas of interest. His work in Feature extraction addresses issues such as Convolutional neural network, which are connected to fields such as Segmentation, Point cloud and Pixel.
His primary areas of investigation include Artificial intelligence, Machine learning, Ranging, Computer vision and Pattern recognition. His Artificial intelligence study frequently links to adjacent areas such as Margin. His Machine learning study integrates concerns from other disciplines, such as Classifier and Embedding.
In Computer vision, he works on issues like Ranking, which are connected to RGB color model. His work carried out in the field of Pattern recognition brings together such families of science as Matching and Bilinear interpolation. His Bilinear interpolation research integrates issues from Subspace topology and Pairwise comparison.
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
arXiv: Computer Vision and Pattern Recognition (2018)
Support Vector Machine Soft Margin Classifiers: Error Analysis
Di-Rong Chen;Qiang Wu;Yiming Ying;Ding-Xuan Zhou.
Journal of Machine Learning Research (2004)
Learning Rates of Least-Square Regularized Regression
Qiang Wu;Yiming Ying;Ding-Xuan Zhou.
Foundations of Computational Mathematics (2006)
Learning-Based License Plate Detection Using Global and Local Features
Huaifeng Zhang;Wenjing Jia;Xiangjian He;Qiang Wu.
international conference on pattern recognition (2006)
Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image
Worapan Kusakunniran;Qiang Wu;Hongdong Li;Jian Zhang.
international conference on computer vision (2009)
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Qiang Wu;Ding-Xuan Zhou.
Neural Computation (2005)
Multi-kernel regularized classifiers
Qiang Wu;Yiming Ying;Ding-Xuan Zhou.
Journal of Complexity (2007)
Support vector regression for multi-view gait recognition based on local motion feature selection
Worapan Kusakunniran;Qiang Wu;Jian Zhang;Hongdong Li.
computer vision and pattern recognition (2010)
Gait Recognition Under Various Viewing Angles Based on Correlated Motion Regression
W. Kusakunniran;Qiang Wu;Jian Zhang;Hongdong Li.
IEEE Transactions on Circuits and Systems for Video Technology (2012)
Multilevel Framework to Detect and Handle Vehicle Occlusion
Wei Zhang;Q.M.J. Wu;Xiaokang Yang;Xiangzhong Fang.
IEEE Transactions on Intelligent Transportation Systems (2008)
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