What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Kuanquan Wang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Biometrics.
Artificial intelligence is closely attributed to Line in his work.
The study incorporates disciplines such as Speech recognition, Similarity and Feature in addition to Pattern recognition.
In his research, Euclidean distance classifier, Small set and Training set is intimately related to Feature vector, which falls under the overarching field of Computer vision.
The Feature extraction study combines topics in areas such as Facial recognition system and Cognitive neuroscience of visual object recognition.
His studies in Biometrics integrate themes in fields like Gabor filter, Wavelet decomposition, Wavelet and Fuzzy logic.
His most cited work include:
- Palmprint recognition using eigenpalms features (455 citations)
- Fisherpalms based palmprint recognition (368 citations)
- Neighborhood Component Feature Selection for High-Dimensional Data (179 citations)
What are the main themes of his work throughout his whole career to date?
Kuanquan Wang focuses on Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Internal medicine.
Biometrics, Segmentation, Feature, Facial recognition system and Image segmentation are the primary areas of interest in his Artificial intelligence study.
His Biometrics study combines topics from a wide range of disciplines, such as Matching, Feature vector, Line, Authentication and Gabor filter.
His research in Pattern recognition tackles topics such as Deep learning which are related to areas like Convolutional neural network.
His research in Computer vision intersects with topics in Visualization and Tongue.
His studies examine the connections between Internal medicine and genetics, as well as such issues in Cardiology, with regards to Action potential duration.
He most often published in these fields:
- Artificial intelligence (60.06%)
- Pattern recognition (42.53%)
- Computer vision (27.87%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (60.06%)
- Pattern recognition (42.53%)
- Deep learning (4.89%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Neuroscience and Electrophysiology.
As part of his studies on Artificial intelligence, Kuanquan Wang often connects relevant subjects like Residual.
The Feature extraction and Stationary wavelet transform research Kuanquan Wang does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Variable, therefore creating a link between diverse domains of science.
His study looks at the relationship between Deep learning and fields such as Segmentation, as well as how they intersect with chemical problems.
His research integrates issues of Tissue level, Biological pacemaker, Ion channel and Sick sinus syndrome in his study of Neuroscience.
His biological study spans a wide range of topics, including Atrioventricular block and Cardiology.
Between 2019 and 2021, his most popular works were:
- ResNet‐Attention model for human authentication using ECG signals (28 citations)
- Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography. (16 citations)
- Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Internal medicine
His primary scientific interests are in Segmentation, Pattern recognition, Artificial intelligence, Deep learning and Convolutional neural network.
His research investigates the connection between Segmentation and topics such as Benchmark that intersect with issues in Convergence, Function and Differentiable function.
His Pattern recognition study combines topics in areas such as Artificial neural network, Fourier transform and Inference.
Kuanquan Wang mostly deals with Stationary wavelet transform in his studies of Artificial intelligence.
His work deals with themes such as Algorithm, Cardiac magnetic resonance imaging and Biometrics, which intersect with Convolutional neural network.
The concepts of his Image segmentation study are interwoven with issues in Image processing, Salient, Focus and Voxel.
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