What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Radar.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Data mining.
His work in Pattern recognition addresses subjects such as Face detection, which are connected to disciplines such as Color model.
His work in the fields of Computer vision, such as Color histogram and Pixel, overlaps with other areas such as Spectral line.
His research integrates issues of Shunting, Inhibitory postsynaptic potential, Feature extraction, Convolutional neural network and Topology in his study of Artificial neural network.
The various areas that Abdesselam Bouzerdoum examines in his Radar study include Classification methods, Gait and Spectrogram.
His most cited work include:
- Skin segmentation using color pixel classification: analysis and comparison (666 citations)
- Neural network for quadratic optimization with bound constraints (292 citations)
- A novel skin color model in YCbCr color space and its application to human face detection (228 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Radar are his primary areas of study.
His works in Feature extraction, Image segmentation, Convolutional neural network, Deep learning and Pixel are all subjects of inquiry into Artificial intelligence.
His work on Image quality, Motion detection and Image processing as part of general Computer vision study is frequently linked to Object detection, bridging the gap between disciplines.
His work deals with themes such as Contextual image classification, Face detection and Feature, which intersect with Pattern recognition.
Artificial neural network is a subfield of Machine learning that Abdesselam Bouzerdoum explores.
Abdesselam Bouzerdoum focuses mostly in the field of Radar, narrowing it down to matters related to Signal and, in some cases, Ground-penetrating radar.
He most often published in these fields:
- Artificial intelligence (73.37%)
- Computer vision (41.12%)
- Pattern recognition (34.91%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (73.37%)
- Pattern recognition (34.91%)
- Computer vision (41.12%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Radar.
All of his Artificial intelligence and Feature extraction, Artifact, Image, Regularization and Feature investigations are sub-components of the entire Artificial intelligence study.
His Pattern recognition research is multidisciplinary, relying on both Frame and Vector autoregression.
His Computer vision research incorporates themes from Artificial neural network, Inference, Trajectory and Sonar.
Abdesselam Bouzerdoum interconnects Classifier and Convolutional neural network in the investigation of issues within Deep learning.
His study in the field of Clutter is also linked to topics like Object detection.
Between 2017 and 2021, his most popular works were:
- Multipolarization Through-Wall Radar Imaging Using Low-Rank and Jointly-Sparse Representations (19 citations)
- Customer reward-based demand response program to improve demand elasticity and minimise financial risk during price spikes (14 citations)
- GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Abdesselam Bouzerdoum focuses on Artificial intelligence, Deep learning, Convolutional neural network, Pattern recognition and Image processing.
His Artificial intelligence research includes elements of Radar, White matter and Computer vision.
Many of his research projects under Radar are closely connected to Sparse matrix with Sparse matrix, tying the diverse disciplines of science together.
His Computer vision research is multidisciplinary, incorporating perspectives in Detector and Sonar.
His Convolutional neural network research includes themes of Transfer of learning and Recall rate.
His work on Segmentation, Image segmentation and Classifier as part of general Pattern recognition study is frequently connected to Gaussian process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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