What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
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 most cited work include:
- Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image (155 citations)
- Learning-Based License Plate Detection Using Global and Local Features (136 citations)
- Gait Recognition Under Various Viewing Angles Based on Correlated Motion Regression (133 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (83.75%)
- Computer vision (50.90%)
- Pattern recognition (40.43%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (83.75%)
- Pattern recognition (40.43%)
- Feature (10.83%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Coupled Bilinear Discriminant Projection for Cross-View Gait Recognition (28 citations)
- Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network (11 citations)
- Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
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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