What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Sparse approximation.
His Artificial intelligence research focuses on subjects like Deconvolution, which are linked to Deblurring.
His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network and Cluster analysis.
Many of his research projects under Computer vision are closely connected to Trajectory with Trajectory, tying the diverse disciplines of science together.
His Segmentation research incorporates themes from Support vector machine, Graph based, Graph kernel and Graph.
His Sparse approximation research focuses on Machine learning and how it relates to Online algorithm and Eye tracking.
His most cited work include:
- Part-Based Visual Tracking with Online Latent Structural Learning (193 citations)
- Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (145 citations)
- Close the loop: Joint blind image restoration and recognition with sparse representation prior (141 citations)
What are the main themes of his work throughout his whole career to date?
Yanning Zhang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Image and Hyperspectral imaging.
His works in Deep learning, Pixel, Robustness, Feature and Segmentation are all subjects of inquiry into Artificial intelligence.
Particularly relevant to Scale-space segmentation is his body of work in Segmentation.
Pattern recognition is often connected to Artificial neural network in his work.
Synthetic aperture radar, Object detection, Video tracking, Image processing and Tracking are the subjects of his Computer vision studies.
His studies deal with areas such as Image resolution, Noise, Sparse matrix, Iterative reconstruction and Compressed sensing as well as Hyperspectral imaging.
He most often published in these fields:
- Artificial intelligence (84.19%)
- Pattern recognition (48.33%)
- Computer vision (40.09%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (84.19%)
- Pattern recognition (48.33%)
- Deep learning (10.69%)
In recent papers he was focusing on the following fields of study:
Yanning Zhang spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Image.
His work on Artificial intelligence deals in particular with Hyperspectral imaging, Convolutional neural network, Feature, Artificial neural network and Feature extraction.
He works in the field of Pattern recognition, namely Segmentation.
His study on Deep learning also encompasses disciplines like
- Boosting, which have a strong connection to Feature learning and Cluster analysis,
- Feature which intersects with area such as Metric, Mutual information, Biometrics and Word error rate.
In his research on the topic of Computer vision, Network architecture, Recurrent neural network, Emotion classification and Headset is strongly related with Robustness.
His research integrates issues of Monocular, Focus and Convolution in his study of Image.
Between 2019 and 2021, his most popular works were:
- 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images (28 citations)
- Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification (23 citations)
- Deep HDR Imaging via A Non-Local Network (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Artificial intelligence, Pattern recognition, Deep learning, Image and Computer vision are his primary areas of study.
His Convolutional neural network, Feature extraction, Feature, Hyperspectral imaging and Segmentation study are his primary interests in Artificial intelligence.
His Pattern recognition study typically links adjacent topics like Robustness.
His Deep learning course of study focuses on Artificial neural network and Surgical planning and Encoder.
His work in the fields of Image, such as Superresolution, intersects with other areas such as Set.
His Computer vision research incorporates elements of Task and Data set.
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