What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Stereoscopy and Visualization.
His Artificial intelligence research focuses on Iterative reconstruction, Autostereoscopy, Stereo display, Depth map and Rendering.
His Computer vision study frequently draws connections to other fields, such as Interpolation.
His study looks at the intersection of Pattern recognition and topics like Image with Pooling.
His research in Stereoscopy intersects with topics in Image quality, Visual perception and Metric.
His Image processing research includes themes of Smoothing, Linear system and Computer graphics.
His most cited work include:
- Fast global image smoothing based on weighted least squares. (206 citations)
- Gradient-Enhancing Conversion for Illumination-Robust Lane Detection (129 citations)
- Cost Aggregation and Occlusion Handling With WLS in Stereo Matching (127 citations)
What are the main themes of his work throughout his whole career to date?
Kwanghoon Sohn spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Pixel.
His study in Artificial intelligence focuses on Convolutional neural network, Image, Feature, Feature extraction and Robustness.
His Computer vision study often links to related topics such as Computer graphics.
His Pattern recognition study integrates concerns from other disciplines, such as Matching and Depth map.
His study explores the link between Algorithm and topics such as Coding that cross with problems in Multiview Video Coding.
The study incorporates disciplines such as Pose and Invariant in addition to Facial recognition system.
He most often published in these fields:
- Artificial intelligence (81.33%)
- Computer vision (60.44%)
- Pattern recognition (26.04%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (81.33%)
- Pattern recognition (26.04%)
- Convolutional neural network (9.58%)
In recent papers he was focusing on the following fields of study:
Kwanghoon Sohn focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Feature extraction.
His studies deal with areas such as Matching and Machine learning as well as Artificial intelligence.
His Pattern recognition research includes elements of Inference, Leverage, Transformer and Stereo matching.
His research integrates issues of Object detection, Iterative reconstruction, Unsupervised learning, Supervised learning and Kernel in his study of Convolutional neural network.
The Computer vision study which covers Mobile device that intersects with Lidar.
The various areas that Kwanghoon Sohn examines in his Feature extraction study include Visualization and Embedding.
Between 2016 and 2021, his most popular works were:
- FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence (69 citations)
- Recurrent Transformer Networks for Semantic Correspondence (56 citations)
- Unified Confidence Estimation Networks for Robust Stereo Matching (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Kwanghoon Sohn mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Feature extraction.
As part of one scientific family, Kwanghoon Sohn deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Matching, and often Depth map.
He interconnects Transformer, Deep neural networks, Pixel, Image and RGB color model in the investigation of issues within Pattern recognition.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Channel, Supervised learning, Unsupervised learning and Iterative reconstruction.
Kwanghoon Sohn is involved in the study of Computer vision that focuses on Image processing in particular.
While the research belongs to areas of Feature extraction, Kwanghoon Sohn spends his time largely on the problem of Emotion recognition, intersecting his research to questions surrounding Facial expression, Visualization, Face and Facial recognition system.
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