What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Cognitive neuroscience of visual object recognition.
His study in Deep learning, Feature extraction, Robustness, Artificial neural network and Discriminative model are all subfields of Artificial intelligence.
Mohammed Bennamoun regularly ties together related areas like Representation in his Pattern recognition studies.
His study in Computer vision is interdisciplinary in nature, drawing from both Local reference frame and Principal component analysis.
His Local reference frame research integrates issues from Feature matching, Feature, Noise and 3D modeling.
His studies deal with areas such as Image processing, Subspace topology and Facial expression as well as Facial recognition system.
His most cited work include:
- Linear Regression for Face Recognition (786 citations)
- An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (421 citations)
- Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes (393 citations)
What are the main themes of his work throughout his whole career to date?
Mohammed Bennamoun spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Algorithm.
His Artificial intelligence course of study focuses on Machine learning and Classifier.
His Pattern recognition study combines topics in areas such as Object, Feature and Robustness.
His Computer vision study focuses mostly on Image registration, Pose, RGB color model, Image segmentation and Object detection.
His study in the fields of Matching under the domain of Algorithm overlaps with other disciplines such as Distortion.
The Cognitive neuroscience of visual object recognition study combines topics in areas such as Local reference frame and Representation.
He most often published in these fields:
- Artificial intelligence (75.28%)
- Pattern recognition (40.04%)
- Computer vision (36.90%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (75.28%)
- Deep learning (10.89%)
- Pattern recognition (40.04%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Deep learning, Pattern recognition, Machine learning and Convolutional neural network.
His study ties his expertise on Computer vision together with the subject of Artificial intelligence.
Mohammed Bennamoun has included themes like Point cloud, Field, Robustness, Focus and Synthetic data in his Deep learning study.
His work deals with themes such as Contextual image classification, Context and Residual, which intersect with Pattern recognition.
His Convolutional neural network study incorporates themes from Object, Speech recognition, Structure tensor and Gesture recognition.
The study incorporates disciplines such as Kernel, Cognitive neuroscience of visual object recognition, Machine vision and Kernel in addition to Artificial neural network.
Between 2018 and 2021, his most popular works were:
- Deep Learning for 3D Point Clouds: A Survey. (121 citations)
- Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era (43 citations)
- Biometric Recognition Using Deep Learning: A Survey (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Deep learning, Convolutional neural network, Machine learning and Object detection are his primary areas of study.
Mohammed Bennamoun interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
His Pattern recognition research incorporates elements of Cognitive neuroscience of visual object recognition, Residual and Gesture.
His Deep learning study combines topics from a wide range of disciplines, such as Field, Feature, Focus and Underwater.
His Convolutional neural network research is multidisciplinary, relying on both Recurrent neural network, Gesture recognition, Speech recognition, Object and Sequence.
He combines subjects such as Robustness and Data science with his study of Object detection.
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