Prof. Mohammed Bennamoun

School of Physics, Maths and Computing, Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Perth, Australia

Prof. Mohammed Bennamoun

School of Physics, Maths and Computing, Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Perth, Australia

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Academic Career Length

35Years (1992-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 December 2021 in IEEE Transactions on Pattern Analysis and Machine Intelligence

Journal Article

Deep Learning for 3D Point Clouds: A Survey

Hao Liu

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

6 September 2023 in Proceedings of the IEEE

Journal Article

Training Spiking Neural Networks Using Lessons From Deep Learning

Max Ward

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package, snnTorch , are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

8 July 2010 in IEEE Transactions on Pattern Analysis and Machine Intelligence

Journal Article

Linear Regression for Face Recognition

Imran Naseem

Roberto Togneri

Mohammed Bennamoun

In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.

17 August 2017 in IEEE Transactions on Neural Networks and Learning Systems

Journal Article

Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes are abundant, making them an overrepresented majority, and data of other classes are scarce, making them an underrepresented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority classes. In this paper, we propose a cost-sensitive (CoSen) deep neural network, which can automatically learn robust feature representations for both the majority and minority classes. During training, our learning procedure jointly optimizes the class-dependent costs and the neural network parameters. The proposed approach is applicable to both binary and multiclass problems without any modification. Moreover, as opposed to data-level approaches, we do not alter the original data distribution, which results in a lower computational cost during the training process. We report the results of our experiments on six major image classification data sets and show that the proposed approach significantly outperforms the baseline algorithms. Comparisons with popular data sampling techniques and CoSen classifiers demonstrate the superior performance of our proposed method.

1 July 2017

Conference Paper

A New Representation of Skeleton Sequences for 3D Action Recognition

Qiuhong Ke

Mohammed Bennamoun

Senjian An

Ferdous Sohel

Farid Boussaid

This paper presents a new method for 3D action recognition with skeleton sequences (i.e., 3D trajectories of human skeleton joints). The proposed method first transforms each skeleton sequence into three clips each consisting of several frames for spatial temporal feature learning using deep neural networks. Each clip is generated from one channel of the cylindrical coordinates of the skeleton sequence. Each frame of the generated clips represents the temporal information of the entire skeleton sequence, and incorporates one particular spatial relationship between the joints. The entire clips include multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We propose to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames of the clips in parallel to incorporate spatial structural information for action recognition. Experimental results clearly show the effectiveness of the proposed new representation and feature learning method for 3D action recognition.

24 April 2013 in International Journal of Computer Vision

Journal Article

Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

Yulan Guo

Ferdous Sohel

Mohammed Bennamoun

Min Lu

Jianwei Wan

About Me

Prof. Mohammed Bennamoun received his Master’s degree from Queen’s University, Kingston, Canada in the area of Control Theory; he received his PhD from Queen’s /Q.U.T in Brisbane, Australia in the area of Computer Vision. He lectured Robotics at Queen’s University and then joined QUT in 1993 as an Associate Lecturer. He then became a Lecturer in 1996 and a Senior Lecturer in 1998 at QUT. He is currently a Winthrop Professor with the Department of Computer Science and Software Engineering at the University of Western Australia (UWA) and is a Researcher of computer vision, machine/deep learning, robotics, and signal/speech processing. He published more than 200 journals and over 260 conference publications. His areas of interest include computer vision (particularly 3D), e.g., object recognition and biometrics; machine/deep learning; robotics (e.g., obstacle avoidance and robot grasping); signal/image processing; control theory. He was awarded more than 80 competitive research grants (more than AUD 35 million in funding) from the Australian Research Council and numerous other government, UWA, and industry research grants. He served for two terms (three years each term) on the Australian Research Council (ARC) College of Experts and the 2018 ARC Excellence in Research for Australia (ERA). He is currently a Senior Area Editor of IEEE Signal Processing Letters and an Associate Editor of IEEE Transactions on Image Processing and IEEE Transactions on Artificial Intelligence.

Prof. Mohammed Bennamoun

Prof. Mohammed Bennamoun

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About Me