References of "Shabayek, Abd El Rahman 50022366"
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See detailHighway Network Block with Gates Constraints for Training Very Deep Networks
Oyedotun, Oyebade UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in 2018 IEEE International Conference on Computer Vision and Pattern Recognition Workshop, June 18-22, 2018 (2018, June 19)

In this paper, we propose to reformulate the learning of the highway network block to realize both early optimization and improved generalization of very deep networks while preserving the network depth ... [more ▼]

In this paper, we propose to reformulate the learning of the highway network block to realize both early optimization and improved generalization of very deep networks while preserving the network depth. Gate constraints are duly employed to improve optimization, latent representations and parameterization usage in order to efficiently learn hierarchical feature transformations which are crucial for the success of any deep network. One of the earliest very deep models with over 30 layers that was successfully trained relied on highway network blocks. Although, highway blocks suffice for alleviating optimization problem via improved information flow, we show for the first time that further in training such highway blocks may result into learning mostly untransformed features and therefore a reduction in the effective depth of the model; this could negatively impact model generalization performance. Using the proposed approach, 15-layer and 20-layer models are successfully trained with one gate and a 32-layer model using three gates. This leads to a drastic reduction of model parameters as compared to the original highway network. Extensive experiments on CIFAR-10, CIFAR-100, Fashion-MNIST and USPS datasets are performed to validate the effectiveness of the proposed approach. Particularly, we outperform the original highway network and many state-ofthe- art results. To the best our knowledge, on the Fashion-MNIST and USPS datasets, the achieved results are the best reported in literature. [less ▲]

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See detailKey-Skeleton Based Feedback Tool for Assisting Physical Activity
Baptista, Renato UL; Ghorbel, Enjie UL; Shabayek, Abd El Rahman UL et al

in 2018 Zooming Innovation in Consumer Electronics International Conference (ZINC), 30-31 May 2018 (2018, May 31)

This paper presents an intuitive feedback tool able to implicitly guide motion with respect to a reference movement. Such a tool is important in multiple applications requiring assisting physical ... [more ▼]

This paper presents an intuitive feedback tool able to implicitly guide motion with respect to a reference movement. Such a tool is important in multiple applications requiring assisting physical activities as in sports or rehabilitation. Our proposed approach is based on detecting key skeleton frames from a reference sequence of skeletons. The feedback is based on the 3D geometry analysis of the skeletons by taking into account the key-skeletons. Finally, the feedback is illustrated by a color-coded tool, which reflects the motion accuracy. [less ▲]

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See detailIMPROVING THE CAPACITY OF VERY DEEP NETWORKS WITH MAXOUT UNITS
Oyedotun, Oyebade UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (2018, February 21)

Deep neural networks inherently have large representational power for approximating complex target functions. However, models based on rectified linear units can suffer reduction in representation ... [more ▼]

Deep neural networks inherently have large representational power for approximating complex target functions. However, models based on rectified linear units can suffer reduction in representation capacity due to dead units. Moreover, approximating very deep networks trained with dropout at test time can be more inexact due to the several layers of non-linearities. To address the aforementioned problems, we propose to learn the activation functions of hidden units for very deep networks via maxout. However, maxout units increase the model parameters, and therefore model may suffer from overfitting; we alleviate this problem by employing elastic net regularization. In this paper, we propose very deep networks with maxout units and elastic net regularization and show that the features learned are quite linearly separable. We perform extensive experiments and reach state-of-the-art results on the USPS and MNIST datasets. Particularly, we reach an error rate of 2.19% on the USPS dataset, surpassing the human performance error rate of 2.5% and all previously reported results, including those that employed training data augmentation. On the MNIST dataset, we reach an error rate of 0.36% which is competitive with the state-of-the-art results. [less ▲]

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See detailTowards Automatic Human Body Model Fitting to a 3D Scan
Saint, Alexandre Fabian A UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in D'APUZZO, Nicola (Ed.) Proceedings of 3DBODY.TECH 2017 - 8th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017 (2017, October)

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing ... [more ▼]

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing clothing. The underlying body shape is thus partially or completely occluded. Yet, it is very desirable to recover the shape of a covered body as it provides non-invasive means of measuring and analysing it. This is particularly convenient for patients in medical applications, customers in a retail shop, as well as in security applications where suspicious objects under clothing are to be detected. To recover the body shape from the 3D scan of a person in any pose, a human body model is usually fitted to the scan. Current methods rely on the manual placement of markers on the body to identify anatomical locations and guide the pose fitting. The markers are either physically placed on the body before scanning or placed in software as a postprocessing step. Some other methods detect key points on the scan using 3D feature descriptors to automate the placement of markers. They usually require a large database of 3D scans. We propose to automatically estimate the body pose of a person from a 3D mesh acquired by standard 3D body scanners, with or without texture. To fit a human model to the scan, we use joint locations as anchors. These are detected from multiple 2D views using a conventional body joint detector working on images. In contrast to existing approaches, the proposed method is fully automatic, and takes advantage of the robustness of state-of-art 2D joint detectors. The proposed approach is validated on scans of people in different poses wearing garments of various thicknesses and on scans of one person in multiple poses with known ground truth wearing close-fitting clothing. [less ▲]

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See detailFacial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
Oyedotun, Oyebade UL; Demisse, Girum UL; Shabayek, Abd El Rahman UL et al

in 2017 IEEE International Conference on Computer Vision Workshop (ICCVW) (2017, August 21)

Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we ... [more ▼]

Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we propose deep convolutional neural network (DCNN) for joint learning of robust facial expression features from fused RGB and depth map latent representations. We posit that learning jointly from both modalities result in a more robust classifier for facial expression recognition (FER) as opposed to learning from either of the modalities independently. Particularly, we construct a learning pipeline that allows us to learn several hierarchical levels of feature representations and then perform the fusion of RGB and depth map latent representations for joint learning of facial expressions. Our experimental results on the BU-3DFE dataset validate the proposed fusion approach, as a model learned from the joint modalities outperforms models learned from either of the modalities. [less ▲]

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See detailTraining Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections
Oyedotun, Oyebade UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in 24th International Conference on Neural Information Processing, Guangzhou, China, November 14–18, 2017 (2017, July 31)

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are ... [more ▼]

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are forward propagated through the model. Hence, later network layers receive less informative signals about the input data, consequently making training less effective. In this work, we address the problem of feature reuse by taking inspiration from an earlier work which employed residual learning for alleviating the problem of feature reuse. We propose a modification of residual learning for training very deep networks to realize improved generalization performance; for this, we allow stochastic shortcut connections of identity mappings from the input to hidden layers.We perform extensive experiments using the USPS and MNIST datasets. On the USPS dataset, we achieve an error rate of 2.69% without employing any form of data augmentation (or manipulation). On the MNIST dataset, we reach a comparable state-of-the-art error rate of 0.52%. Particularly, these results are achieved without employing any explicit regularization technique. [less ▲]

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See detailDEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Saint, Alexandre Fabian A UL et al

in IEEE International Conference on Image Processing, Beijing 17-20 Spetember 2017 (2017)

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See detailFlexible Feedback System for Posture Monitoring and Correction
Baptista, Renato UL; Antunes, Michel; Shabayek, Abd El Rahman UL et al

in IEEE International Conference on Image Information Processing (ICIIP) (2017)

In this paper, we propose a framework for guiding patients and/or users in how to correct their posture in real-time without requiring a physical or a direct intervention of a therapist or a sports ... [more ▼]

In this paper, we propose a framework for guiding patients and/or users in how to correct their posture in real-time without requiring a physical or a direct intervention of a therapist or a sports specialist. In order to support posture monitoring and correction, this paper presents a flexible system that continuously evaluates postural defects of the user. In case deviations from a correct posture are identified, then feedback information is provided in order to guide the user to converge to an appropriate and stable body condition. The core of the proposed approach is the analysis of the motion required for aligning body-parts with respect to postural constraints and pre-specified template skeleton poses. Experimental results in two scenarios (sitting and weight lifting) show the potential of the proposed framework. [less ▲]

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