What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Irene Yu-Hua Gu mainly focuses on Artificial intelligence, Electronic engineering, Electric power system, Computer vision and Power quality.
Her work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with her field of interest in Pooling.
Her Electronic engineering research integrates issues from Emphasis, Estimation theory, Power electronics and Electrical network.
The Electric power system study combines topics in areas such as Control theory, Expert system, Waveform, Signal processing and Data analysis.
Her Computer vision research is multidisciplinary, relying on both Robustness and Pattern recognition.
Her work on Feature extraction as part of general Pattern recognition study is frequently linked to Color histogram, bridging the gap between disciplines.
Her most cited work include:
- Statistical modeling of complex backgrounds for foreground object detection (904 citations)
- Signal processing of power quality disturbances (584 citations)
- Foreground object detection from videos containing complex background (366 citations)
What are the main themes of her work throughout her whole career to date?
Irene Yu-Hua Gu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Video tracking and Electronic engineering.
Her study in Feature extraction, Object detection, Deep learning, Image segmentation and Segmentation falls under the purview of Artificial intelligence.
Her work deals with themes such as Image processing and Background subtraction, which intersect with Object detection.
She works mostly in the field of Computer vision, limiting it down to concerns involving Geodesic and, occasionally, Riemannian manifold.
As a part of the same scientific study, she usually deals with the Pattern recognition, concentrating on Contextual image classification and frequently concerns with AdaBoost.
Irene Yu-Hua Gu combines subjects such as Power quality, Voltage, Electric power system, Electrical network and Signal processing with her study of Electronic engineering.
She most often published in these fields:
- Artificial intelligence (62.82%)
- Computer vision (41.88%)
- Pattern recognition (31.20%)
What were the highlights of her more recent work (between 2015-2021)?
- Artificial intelligence (62.82%)
- Pattern recognition (31.20%)
- Deep learning (8.97%)
In recent papers she was focusing on the following fields of study:
Irene Yu-Hua Gu mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Feature extraction and Computer vision.
Her Convolutional neural network and Segmentation study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Fall detection, bridging the gap between disciplines.
Her biological study spans a wide range of topics, including Salient and Recurrent neural network.
Her research in Deep learning intersects with topics in Feature learning, Feature and Overfitting.
Her study focuses on the intersection of Feature extraction and fields such as Image segmentation with connections in the field of Object detection.
Her Computer vision study incorporates themes from False alarm and Motion.
Between 2015 and 2021, her most popular works were:
- Deepside: A general deep framework for salient object detection (63 citations)
- On waveform distortion in the frequency range of 2 kHz–150 kHz—Review and research challenges (47 citations)
- An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images (33 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Irene Yu-Hua Gu mostly deals with Artificial intelligence, Deep learning, Pattern recognition, Feature extraction and Convolutional neural network.
Irene Yu-Hua Gu has included themes like Machine learning, Geodesic and Computer vision in her Artificial intelligence study.
Her study brings together the fields of Segmentation and Deep learning.
In her study, which falls under the umbrella issue of Segmentation, Feature is strongly linked to Salience.
Her work carried out in the field of Feature extraction brings together such families of science as Image segmentation, Conditional random field, Object detection, Feature learning and Voltage.
The study incorporates disciplines such as Classification methods, Convolution and Constant false alarm rate in addition to Convolutional neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
