References of "Aouada, Djamila 50000437"
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See detailEnhancement of Dynamic Depth Scenes by Upsampling for Precise Super-Resolution (UP-SR)
Al Ismaeil, Kassem; Aouada, Djamila UL; Mirbach, Bruno et al

in Computer Vision and Image Understanding (2016)

Multi-frame super-resolution is the process of recovering a high resolution image or video from a set of captured low resolution images. Super-resolution approaches have been largely explored in 2-D ... [more ▼]

Multi-frame super-resolution is the process of recovering a high resolution image or video from a set of captured low resolution images. Super-resolution approaches have been largely explored in 2-D imaging. However, their extension to depth videos is not straightforward due to the textureless nature of depth data, and to their high frequency contents coupled with fast motion artifacts. Recently, few attempts have been introduced where only the super-resolution of static depth scenes has been addressed. In this work, we propose to enhance the resolution of dynamic depth videos with non-rigidly moving objects. The proposed approach is based on a new data model that uses densely upsampled, and cumulatively registered versions of the observed low resolution depth frames. We show the impact of upsampling in increasing the sub-pixel accuracy and reducing the rounding error of the motion vectors. Furthermore, with the proposed cumulative motion estimation, a high registration accuracy is achieved between non-successive upsampled frames with relative large motions. A statistical performance analysis is derived in terms of mean square error explaining the effect of the number of observed frames and the effect of the super-resolution factor at a given noise level. We evaluate the accuracy of the proposed algorithm theoretically and experimentally as function of the SR factor, and the level of contaminations with noise. Experimental results on both real and synthetic data show the effectiveness of the proposed algorithm on dynamic depth videos as compared to state-of-art methods. [less ▲]

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See detailVisual and human-interpretable feedback for assisting physical activity
Goncalves Almeida Antunes, Michel UL; Baptista, Renato UL; Demisse, Girum UL et al

in European Conference on Computer Vision (ECCV) Workshop on Assistive Computer Vision and Robotics Amsterdam, (2016)

Physical activity is essential for stroke survivors for recovering some autonomy in daily life activities. Post-stroke patients are initially subject to physical therapy under the supervision of a health ... [more ▼]

Physical activity is essential for stroke survivors for recovering some autonomy in daily life activities. Post-stroke patients are initially subject to physical therapy under the supervision of a health professional, but due to economical aspects, home based rehabilitation is eventually suggested. In order to support the physical activity of stroke patients at home, this paper presents a system for guiding the user in how to properly perform certain actions and movements. This is achieved by presenting feedback in form of visual information and human-interpretable messages. The core of the proposed approach is the analysis of the motion required for aligning body-parts with respect to a template skeleton pose, and how this information can be presented to the user in form of simple recommendations. Experimental results in three datasets show the potential of the proposed framework. [less ▲]

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See detailFeature Engineering Strategies for Credit Card Fraud Detection
Correa Bahnsen, Alejandro; Aouada, Djamila UL; Stojanovic, Aleksandar et al

in Expert Systems with Applications (2016), 51

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See detailA Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection
Goncalves Almeida Antunes, Michel UL; Aouada, Djamila UL; Ottersten, Björn UL

in IEEE Winter Conference on Applications of Computer Vision (WACV), 2016 (2016)

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See detailDetecting Credit Card Fraud using Periodic Features
Correa Bahnsen, Alejandro; Aouada, Djamila UL; Stojanovic, Aleksandar et al

in IEEE International Conference on Machine Learning and Applications (2015, December)

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See detailExample-Dependent Cost-Sensitive Decision Trees
Correa Bahnsen, Alejandro UL; Aouada, Djamila UL; Ottersten, Björn UL

in Expert Systems with Applications (2015), 42(19), 6609-6619

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not ... [more ▼]

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. State-of-the-art example-dependent cost-sensitive techniques only introduce the cost to the algorithm, either before or after training, therefore, leaving opportunities to investigate the potential impact of algorithms that take into account the real financial example-dependent costs during an algorithm training. In this paper, we propose an example-dependent cost-sensitive decision tree algorithm, by incorporating the different example-dependent costs into a new cost-based impurity measure and a new cost-based pruning criteria. Then, using three different databases, from three real-world applications: credit card fraud detection, credit scoring and direct marketing, we evaluate the proposed method. The results show that the proposed algorithm is the best performing method for all databases. Furthermore, when compared against a standard decision tree, our method builds significantly smaller trees in only a fifth of the time, while having a superior performance measured by cost savings, leading to a method that not only has more business-oriented results, but also a method that creates simpler models that are easier to analyze. [less ▲]

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See detailUnified Multi-Lateral Filter for Real-Time Depth Map Enhancement
Garcia Becerro, Frederic UL; Aouada, Djamila UL; Mirbach, Bruno et al

in Image & Vision Computing (2015), 41

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See detailA novel cost-sensitive framework for customer churn predictive modeling
Correa Bahnsen, Alejandro UL; Aouada, Djamila UL; Ottersten, Björn UL

in Decision Analytics (2015), 2(5),

Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to ... [more ▼]

Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this paper, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4% [less ▲]

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See detailReal-Time Non-Rigid Multi-Frame Depth Video Super-Resolution
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Solignac, Thomas et al

in IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'15), (Best paper award) (2015, June 12)

This paper proposes to enhance low resolution dynamic depth videos containing freely non–rigidly moving objects with a new dynamic multi–frame super–resolution algorithm. Existent methods are either ... [more ▼]

This paper proposes to enhance low resolution dynamic depth videos containing freely non–rigidly moving objects with a new dynamic multi–frame super–resolution algorithm. Existent methods are either limited to rigid objects, or restricted to global lateral motions discarding radial displacements. We address these shortcomings by accounting for non–rigid displacements in 3D. In addition to 2D optical flow, we estimate the depth displacement, and simultaneously correct the depth measurement by Kalman filtering. This concept is incorporated efficiently in a multi–frame super–resolution framework. It is formulated in a recursive manner that ensures an efficient deployment in real–time. Results show the overall improved performance of the proposed method as compared to alternative approaches, and specifically in handling relatively large 3D motions. Test examples range from a full moving human body to a highly dynamic facial video with varying expressions. [less ▲]

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See detailPatch-based Statistical Performance Analysis of Upsampling for Precise Super–Resolution
Aouada, Djamila UL; Al Ismaeil, Kassem UL; Ottersten, Björn UL

in 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'15) (2015, March)

All existent methods for the statistical analysis of super–resolution approaches have stopped at the variance term, not accounting for the bias in the mean square error. In this paper we give an original ... [more ▼]

All existent methods for the statistical analysis of super–resolution approaches have stopped at the variance term, not accounting for the bias in the mean square error. In this paper we give an original derivation of the bias term. We propose to use a patch-based method inspired by the work of (Chatterjee and Milanfar, 2009). Our approach, however, is completely new as we derive a new affine bias model dedicated for the multi-frame super resolution framework. We apply the proposed statistical performance analysis to the Upsampling for Precise Super–Resolution (UP-SR) algorithm. This algorithm was shown experimentally to be a good solution for enhancing the resolution of depth sequences in both cases of global and local motions. Its performance is herein analyzed theoretically in terms of its approximated mean square error, using the proposed derivation of the bias. This analysis is validated experimentally on simulated static and dynamic depth sequences with a known ground truth. This provides an insightful understanding of the effects of noise variance, number of observed low resolution frames, and super–resolution factor on the final and intermediate performance of UP–SR. Our conclusion is that increasing the number of frames should improve the performance while the error is increased due to local motions, and to the upsampling which is part of UP-SR. [less ▲]

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See detailTemplate-Based Statistical Shape Modelling on Deformation Space
Demisse, Girum UL; Aouada, Djamila UL; Ottersten, Björn UL

in 22nd IEEE International Conference on Image Processing (2015)

A statistical model for shapes in $\mathbb{R}^2$ or $\mathbb{R}^3$ is proposed. Shape modelling is a difficult problem mainly due to the non-linear nature of its space. Our approach considers curves as ... [more ▼]

A statistical model for shapes in $\mathbb{R}^2$ or $\mathbb{R}^3$ is proposed. Shape modelling is a difficult problem mainly due to the non-linear nature of its space. Our approach considers curves as shape contours, and models their deformations with respect to a deformable template shape. Contours are uniformly sampled into a discrete sequence of points. Hence, the deformation of a shape is formulated as an action of transformation matrices on each of these points. A parametrized stochastic model based on Markov process is proposed to model shape variability in the deformation space. The model's parameters are estimated from a labeled training dataset. Moreover, a similarity metric based on the Mahalanobis distance is proposed. Subsequently, the model has been successfully tested for shape recognition, synthesis, and retrieval. [less ▲]

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See detailView-Independent Enhanced 3D Reconstruction of Non-Rigidly Deforming Objects
Afzal, Hassan UL; Aouada, Djamila UL; Destelle, Francois et al

in 16th International Conference on Computer Analysis of Images and Patterns (2015)

In this paper, we target enhanced 3D reconstruction of non-rigidly deforming objects based on a view-independent surface representation with an automated recursive filtering scheme. This work improves ... [more ▼]

In this paper, we target enhanced 3D reconstruction of non-rigidly deforming objects based on a view-independent surface representation with an automated recursive filtering scheme. This work improves upon the KinectDeform algorithm which we recently proposed. KinectDeform uses an implicit viewdependent volumetric truncated signed distance function (TSDF) based surface representation. The view-dependence makes its pipeline complex by requiring surface prediction and extraction steps based on camera’s field of view. This paper proposes to use an explicit projection-based Moving Least Squares (MLS) surface representation from point-sets. Moreover, the empirical weighted filtering scheme in KinectDeform is replaced by an automated fusion scheme based on a Kalman filter. We analyze the performance of the proposed algorithm both qualitatively and quantitatively and show that it is able to produce enhanced and feature preserving 3D reconstructions. [less ▲]

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See detailEnsemble of Example-Dependent Cost-Sensitive Decision Trees
Bahnsen, Alejandro Correa; Aouada, Djamila UL; Ottersten, Björn UL

in arXiv preprint arXiv:1505.04637 (2015)

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard ... [more ▼]

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings. [less ▲]

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See detailExample-Dependent Cost-Sensitive Logistic Regression for Credit Scoring
Correa Bahnsen, Alejandro UL; Aouada, Djamila UL; Ottersten, Björn UL

in 2014 13th International Conference on Machine Learning and Applications (2014, December 03)

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost ... [more ▼]

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do not take into account the real financial costs associated with the lending business. In this paper, we propose a new example-dependent cost matrix for credit scoring. Furthermore, we propose an algorithm that introduces the example-dependent costs into a logistic regression. Using two publicly available datasets, we compare our proposed method against state-of-the-art example-dependent cost-sensitive algorithms. The results highlight the importance of using real financial costs. Moreover, by using the proposed cost-sensitive logistic regression, significant improvements are made in the sense of higher savings. [less ▲]

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See detailRGB-D Multi-View System Calibration for Full 3D Scene Reconstruction
Afzal, Hassan UL; Aouada, Djamila UL; Fofi, David et al

in 22nd International Conference on Pattern Recognition (ICPR'14) (2014)

One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We ... [more ▼]

One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We propose BAICP+ which combines Bundle Adjustment (BA) and Iterative Closest Point (ICP) algorithms to take into account both 2D visual and 3D shape information in one minimization formulation to estimate relative pose parameters of each camera. BAICP+ is generic enough to take different types of visual features into account and can be easily adapted to varying quality of 2D and 3D data. We perform experiments on real and simulated data. Results show that with the right weighting factor BAICP+ has an optimal performance when compared to BA and ICP used independently or sequentially. [less ▲]

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See detailSurface UP-SR for an Improved Face Recognition Using Low Resolution Depth Cameras
Aouada, Djamila UL; Al Ismaeil, Kassem UL; Kedir Idris, Kedija et al

in 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS'14) (2014)

We address the limitation of low resolution depth cameras in the context of face recognition. Considering a face as a surface in 3-D, we reformulate the recently proposed Upsampling for Precise ... [more ▼]

We address the limitation of low resolution depth cameras in the context of face recognition. Considering a face as a surface in 3-D, we reformulate the recently proposed Upsampling for Precise Super–Resolution algorithm as a new approach on three dimensional points. This reformulation allows an efficient implementation, and leads to a largely enhanced 3-D face reconstruction. Moreover, combined with a dedicated face detection and representation pipeline, the proposed method provides an improved face recognition system using low resolution depth cameras. We show experimentally that this system increases the face recognition rate as compared to directly using the low resolution raw data. [less ▲]

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See detailSPN2: Single-Sided Privacy Preserving Nearest Neighbor and its Application to Face Recognition
Aouada, Djamila UL; Khader, Dalia UL

in 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS'14) (2014)

We address the privacy concerns that raise when running a nearest neighbor (NN) search on confidential data in a surveillance system composed of a client and a server. The proposed privacy preserving NN ... [more ▼]

We address the privacy concerns that raise when running a nearest neighbor (NN) search on confidential data in a surveillance system composed of a client and a server. The proposed privacy preserving NN search uses Boneh-Goh-Nissim encryption to hide both the query data captured by the client and the database records stored in the server. As opposed to state–of–the–art approaches which rely on a large number of interactions, this encryption enables the client to fully outsource the NN computation to the server; hence, ensuring a single-sided private computation, and resulting in a one–round protocol between the server and the client. We analyze the practical feasibility of this algorithm on a face recognition problem. We formally prove and experimentally show that the resulting system maintains the recognition rate while fully preserving the privacy of both the database and the acquired faces. [less ▲]

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See detailKinectDeform: Enhanced 3D Reconstruction of Non-Rigidly Deforming Objects
Afzal, Hassan UL; Al Ismaeil, Kassem UL; Aouada, Djamila UL et al

in Second International Conference on 3D Vision (2014)

In this work we propose KinectDeform, an algorithm which targets enhanced 3D reconstruction of scenes containing non-rigidly deforming objects. It provides an innovation to the existing class of ... [more ▼]

In this work we propose KinectDeform, an algorithm which targets enhanced 3D reconstruction of scenes containing non-rigidly deforming objects. It provides an innovation to the existing class of algorithms which either target scenes with rigid objects only or allow for very limited non-rigid deformations or use pre-computed templates to track them. KinectDeform combines a fast non-rigid scene tracking algorithm based on octree data representation and hierarchical voxel associations with a recursive data filtering mechanism. We analyze its performance on both real and simulated data and show improved results in terms of smoothness and feature preserving 3D reconstructions with reduced noise. [less ▲]

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See detailImproving Credit Card Fraud Detection with Calibrated Probabilities
Correa Bahnsen, Alejandro UL; Stojanovic, Aleksandar UL; Aouada, Djamila UL et al

in Proceedings of the fourteenth SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014. (2014)

Previous analysis has shown that applying Bayes minimum risk to detect credit card fraud leads to better results measured by monetary savings, as compared with traditional methodologies. Nevertheless ... [more ▼]

Previous analysis has shown that applying Bayes minimum risk to detect credit card fraud leads to better results measured by monetary savings, as compared with traditional methodologies. Nevertheless, this approach requires good probability estimates that not only separate well between positive and negative examples, but also assess the real probability of the event. Unfortunately, not all classification algorithms satisfy this restriction. In this paper, two different methods for calibrating probabilities are evaluated and analyzed in the context of credit card fraud detection, with the objective of finding the model that minimizes the real losses due to fraud. Even though under-sampling is often used in the context of classification with unbalanced datasets, it is shown that when probabilistic models are used to make decisions based on minimizing risk, using the full dataset provides significantly better results. In order to test the algorithms, a real dataset provided by a large European card processing company is used. It is shown that by calibrating the probabilities and then using Bayes minimum Risk the losses due to fraud are reduced. Furthermore, because of the good overall results, the aforementioned card processing company is currently incorporating the methodology proposed in this paper into their fraud detection system. Finally, the methodology has been tested on a different application, namely, direct marketing. [less ▲]

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See detailDynamic Super Resolution of Depth Sequences with Non-Rigid Motions
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Mirbach, Bruno et al

in 20th International Conference on Image Processing (2013, September)

We enhance the resolution of depth videos acquired with low resolution time-of-flight cameras. To that end, we propose a new dedicated dynamic super-resolution that is capable to accurately super-resolve a ... [more ▼]

We enhance the resolution of depth videos acquired with low resolution time-of-flight cameras. To that end, we propose a new dedicated dynamic super-resolution that is capable to accurately super-resolve a depth sequence containing one or multiple moving objects without strong constraints on their shape or motion, thus clearly outperforming any existing super-resolution techniques that perform poorly on depth data and are either restricted to global motions or not precise because of an implicit estimation of motion. Our proposed approach is based on a new data model that leads to a robust registration of all depth frames after a dense upsampling. The texture-less nature of depth images allows to robustly handle sequences with multiple moving objects as confirmed by our experiments. [less ▲]

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