2023 - Research.com Computer Science in Australia Leader Award
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.
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.
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.
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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Linear Regression for Face Recognition
Imran Naseem;Roberto Togneri;Mohammed Bennamoun.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
A.S. Mian;M. Bennamoun;R. Owens.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
A.S. Mian;M. Bennamoun;R. Owens.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey
Yulan Guo;Mohammed Bennamoun;Ferdous Ahmed Sohel;Min Lu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Salman H. Khan;Munawar Hayat;Mohammed Bennamoun;Ferdous A. Sohel.
IEEE Transactions on Neural Networks (2018)
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Yulan Guo;Yulan Guo;Ferdous Ahmed Sohel;Mohammed Bennamoun;Min Lu.
International Journal of Computer Vision (2013)
Ontology learning from text: A look back and into the future
Wilson Wong;Wei Liu;Mohammed Bennamoun.
ACM Computing Surveys (2012)
A New Representation of Skeleton Sequences for 3D Action Recognition
Qiuhong Ke;Mohammed Bennamoun;Senjian An;Ferdous Sohel.
computer vision and pattern recognition (2017)
A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
Yulan Guo;Mohammed Bennamoun;Ferdous Sohel;Min Lu.
International Journal of Computer Vision (2016)
On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes
A. Mian;M. Bennamoun;R. Owens.
International Journal of Computer Vision (2010)
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