What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Neuromorphic engineering, Electronic engineering, Pattern recognition and Synapse.
In most of his Artificial intelligence studies, his work intersects topics such as Computer vision.
In Neuromorphic engineering, Moongu Jeon works on issues like CMOS, which are connected to Memristor.
The various areas that Moongu Jeon examines in his Electronic engineering study include Nanoscopic scale, Resistive touchscreen and Electronics.
His research integrates issues of Centroid and External Data Representation in his study of Pattern recognition.
His Synapse study incorporates themes from Unsupervised learning, Speech processing and Pattern recognition.
His most cited work include:
- Structure Preserving Dimension Reduction for Clustered Text Data Based on the Generalized Singular Value Decomposition (155 citations)
- Neuromorphic Hardware System for Visual Pattern Recognition With Memristor Array and CMOS Neuron (149 citations)
- RRAM-based synapse for neuromorphic system with pattern recognition function (120 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Artificial neural network.
Video tracking, Feature extraction, Deep learning, Object and Convolutional neural network are the core of his Artificial intelligence study.
His Pattern recognition research includes themes of Pixel, Image and Feature.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Detector.
His Artificial neural network study frequently involves adjacent topics like Frame.
His Stationary wavelet transform study combines topics from a wide range of disciplines, such as Harmonic wavelet transform and Second-generation wavelet transform.
He most often published in these fields:
- Artificial intelligence (63.16%)
- Pattern recognition (28.95%)
- Computer vision (25.88%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (63.16%)
- Pattern recognition (28.95%)
- Computer vision (25.88%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Deep learning.
His work in Artificial intelligence covers topics such as Construct which are related to areas like Pixel.
His biological study spans a wide range of topics, including Matching and Similarity.
Moongu Jeon interconnects Representation and False positive paradox in the investigation of issues within Computer vision.
In his research, Multi-task learning is intimately related to Mutual information, which falls under the overarching field of Machine learning.
His Deep learning research is multidisciplinary, incorporating elements of Contextual image classification, Cross entropy and Motion planning.
Between 2018 and 2021, his most popular works were:
- A labeled random finite set online multi-object tracker for video data (27 citations)
- Data Association for Multi-Object Tracking via Deep Neural Networks (24 citations)
- Key Points Estimation and Point Instance Segmentation Approach for Lane Detection. (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Moongu Jeon mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Convolutional neural network.
Moongu Jeon incorporates Artificial intelligence and Gaussian in his research.
His work on Stationary wavelet transform is typically connected to Abnormality as part of general Pattern recognition study, connecting several disciplines of science.
His work carried out in the field of Computer vision brings together such families of science as False positive paradox and Consistency.
His Artificial neural network research incorporates elements of Frame and Algorithm.
The Convolutional neural network study combines topics in areas such as Image segmentation, Noise reduction, Image denoising and Rectification.
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