Moncef Gabbouj

Moncef Gabbouj
Tampere University | UTA · Department of Signal Processing

Ph.D.

About

951
Publications
209,923
Reads
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19,238
Citations
Citations since 2016
348 Research Items
12559 Citations
201620172018201920202021202205001,0001,5002,0002,500
201620172018201920202021202205001,0001,5002,0002,500
201620172018201920202021202205001,0001,5002,0002,500
201620172018201920202021202205001,0001,5002,0002,500
Additional affiliations
January 2011 - December 2015
Tampere University of Technology
Position
  • Professor
January 2002 - July 2007
Tampere University of Technology
Position
  • Head of Department
January 1998 - present
Tampere University
Position
  • Professor (Full)
Education
January 1987 - December 1989
Purdue University
Field of study
  • Electrical Engineering
August 1985 - December 1986
Purdue University
Field of study
  • Electrical Engineering
August 1981 - May 1985
Oklahoma State University - Stillwater
Field of study
  • Electrical Engineering

Publications

Publications (951)
Article
Full-text available
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem....
Preprint
Full-text available
Affective computing has garnered researchers' attention and interest in recent years as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is quite complex. While various stress studies use facial expressions and wearables, most existing datasets rely on proces...
Article
Affective computing has garnered researchers’ attention and interest in recent years as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is quite complex. While various stress studies use facial expressions and wearables, most existing datasets rely on proces...
Preprint
Full-text available
Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such as image deblurring, denoising, and exposure correction where there is usually a strong assumption on the artifact type and severity. As a pioneer study in blind X-ray re...
Preprint
Full-text available
Despite the superior performance of CNN, deploying them on low computational power devices is still limited as they are typically computationally expensive. One key cause of the high complexity is the connection between the convolution layers and the fully connected layers, which typically requires a high number of parameters. To alleviate this iss...
Preprint
Full-text available
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augment...
Preprint
Full-text available
This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approaches for personalized ECG monitoring require both abnormal and normal heartbeats for the training of the dedicated classifier. However, in a real-world scenario where the...
Article
Full-text available
Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed r...
Data
Appendix for the study: Blind ECG Restoration by Operational Cycle-GANs
Article
Full-text available
Objective: ECG recordings often suffer from a set of artifacts with varying types, severities, and durations, and this makes an accurate diagnosis by machines or medical doctors difficult and unreliable. Numerous studies have proposed ECG denoising; however, they naturally fail to restore the actual ECG signal corrupted with such artifacts due to...
Preprint
Full-text available
Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the maj...
Preprint
Full-text available
Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable du...
Article
Full-text available
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors. Recently, this issue has been addressed by deep 1-D convolutional neural networks (CNNs) that h...
Preprint
Full-text available
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often...
Preprint
Full-text available
Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for class...
Preprint
Full-text available
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a novel framework for the band selection problem: Self-Representation Learning (SRL) with Sparse 1D-Operational Autoencoder (SOA). The proposed SLR-SOA approach introd...
Preprint
Full-text available
Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the rep...
Preprint
Full-text available
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data usually contain measurements with uncertainties and artifacts, the input space in the proposed framework consists of probability distributions to model the uncertainties associa...
Preprint
Full-text available
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, an...
Preprint
In this work, we propose several attention formulations for multivariate sequence data. We build on top of the recently introduced 2D-Attention and reformulate the attention learning methodology by quantifying the relevance of feature/temporal dimensions through latent spaces based on self-attention rather than learning them directly. In addition,...
Preprint
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms. While most of the previous works consider the ideal scenario of presence of both modalities at...
Article
Full-text available
Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and...
Chapter
In this chapter, recent state-of-the-art techniques in biosignal time-series analysis will be presented. We shall start with the problem of patient-specific ECG beat classification where the objective is to discriminate the arrhythmic beats from the normal (healthy) beats of an individual patient. So, we will answer the ultimate question of how to...
Chapter
The availability of an increasing amount of computational power and large-scale public data sets has driven the field of object detection and tracking with an unprecedented development speed, finding applications in many areas. This chapter surveys the most prominent methods in the field. We first formulate the problems of object detection and sing...
Chapter
The expressive power of deep neural networks has enabled us to successfully tackle several modeling problems in computer vision, natural language processing, and financial forecasting in the last few years. Nowadays, neural networks achieving state-of-the-art (SoTA) performance in any field can be formed by hundreds of layers with millions of param...
Chapter
This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental signs of MI can be visible as the abnormality in one or several segments of the left ventricle (LV) wall, where a segment may...
Article
Full-text available
Spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty. Thus,...
Article
Full-text available
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective. The proposed attention module is incorporated into the recently proposed Neural Bag of Feature (NBoF) model to...
Article
Single image-based intrinsic image decomposition attempts to separate one input image into several intrinsic components, which is inherently an under-constrained problem. Some recent works have been proposed to estimate the intrinsic components using encoder-decoder structures. However, they generally lack exploration of the different component-ori...
Article
Advances in communication technologies and computational capabilities of Internet of Things (IoT) devices enable a range of complex applications that require ever increasing processing of sensors' data. An illustrative example is real-time video surveillance that captures videos of target scenes and process them to detect anomalies using deep learn...
Article
Full-text available
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augment...
Article
Full-text available
Objective: Despitethe proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. R...
Article
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation....
Preprint
Full-text available
Classical image denoising methods utilize the non-local self-similarity principle to effectively recover image content from noisy images. Current state-of-the-art methods use deep convolutional neural networks (CNNs) to effectively learn the mapping from noisy to clean images. Deep denoising CNNs manifest a high learning capacity and integrate non-...
Preprint
Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches operate directly on the input image. In this paper, we argue that this problem can be decomposed into three channel-wise independent and symmetric sub-problems and propos...
Preprint
Recently, there has been an increasing interest in applying attention mechanisms in Convolutional Neural Networks (CNNs) to solve computer vision tasks. Most of these methods learn to explicitly identify and highlight relevant parts of the scene and pass the attended image to further layers of the network. In this paper, we argue that such an appro...
Article
In this paper, a novel data-driven method for weight initialization of Multilayer Perceptrons and Convolutional Neural Networks based on discriminant learning is proposed. The approach relaxes some of the limitations of competing data-driven methods, including unimodality assumptions, limitations on the architectures related to limited maximal dime...
Article
Full-text available
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detect...
Article
Full-text available
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of...
Article
Full-text available
Deep Learning provided powerful tools for forecasting financial time series data. However, despite the success of these approaches on many challenging financial forecasting tasks, it is not always straightforward to employ DL-based approaches for highly volatile and non-stationary time financial series. To this end, in this paper, an adaptive input...
Chapter
Monitoring the structural performance of engineering structures has always been pertinent for maintaining structural health and assessing the life cycle of structures. Structural Health Monitoring (SHM) and Structural Damage Detection (SDD) fields have been topics of ongoing research over the years to explore and verify different monitoring techniq...
Preprint
Full-text available
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring methods especially based on Deep Learning networks focusing mostly on detecting bearing faults; however, none of...
Preprint
Full-text available
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, co...
Preprint
Full-text available
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low quality and noisy signals acquired from mobile ECG sensors such as Holter monitors. Recently, this issue has been addressed by deep 1D Convolutional Neural Networks (CNNs) that have achieved state-of-...
Preprint
Full-text available
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detect...
Article
Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection or classification performance of a MCL model, with higher compression rates often associated with lower inference accuracy. However, higher compression rates are more ame...
Preprint
Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection or classification performance of a MCL model, with higher compression rates often associated with lower inference accuracy. However, higher compression rates are more ame...
Preprint
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is because deep neural network optimized with stochastic gradient descent is sensitive to the input variable range and prone to numerical issues. Different than other types...
Preprint
Full-text available
In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruction algorithm for the compressed information. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solu...