References of "Aouada, Djamila 50000437"
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See detailUnsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion
Goncalves Almeida Antunes, Michel UL; Barreto, Joao P.; Aouada, Djamila UL et al

in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (2017)

The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene ... [more ▼]

The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene project into circles in the image, and three lines suffice to calibrate the camera up to an ambiguity between focal length and radial distortion. The calibration results highly depend on accurate circle estimation, which is hard to accomplish, because lines tend to project into short circular arcs. To overcome this problem, we show that, given a short circular arc edge, it is possible to robustly determine a line that goes through the center of the corresponding circle. These lines, henceforth called Lines of Circle Centres (LCCs), are used in a new method that detects sets of parallel lines and estimates the calibration parameters, including the center and amount of distortion, focal length, and camera orientation with respect to the Manhattan frame. Extensive experiments in both semi-synthetic and real images show that our algorithm outperforms state- of-the-art approaches in unsupervised calibration from a single image, while providing more information. [less ▲]

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See detailSTARR - Decision SupporT and self-mAnagement system for stRoke survivoRs Vision based Rehabilitation System
Shabayek, Abd El Rahman UL; Baptista, Renato UL; Papadopoulos, Konstantinos UL et al

in European Project Space on Networks, Systems and Technologies (2017)

This chapter explains a vision based platform developed within a European project on decision support and self-management for stroke survivors. The objective is to provide a low cost home rehabilitation ... [more ▼]

This chapter explains a vision based platform developed within a European project on decision support and self-management for stroke survivors. The objective is to provide a low cost home rehabilitation system. Our main concern is to maintain the patients' physical activity while carrying a continuous monitoring of his physical and emotional state. This is essential for recovering some autonomy in daily life activities and preventing a second damaging stroke. Post-stroke patients are initially subject to physical therapy under the supervision of a health professional to follow up on their daily physical activity and monitor their emotional state. However, due to social and economical constraints, home based rehabilitation is eventually suggested. Our vision platform paves the way towards having low cost home rehabilitation. [less ▲]

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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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See detailVideo-Based Feedback for Assisting Physical Activity
Baptista, Renato UL; Goncalves Almeida Antunes, Michel UL; Aouada, Djamila UL et al

in 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2017)

In this paper, we explore the concept of providing feedback to a user moving in front of a depth camera so that he is able to replicate a specific template action. This can be used as a home based ... [more ▼]

In this paper, we explore the concept of providing feedback to a user moving in front of a depth camera so that he is able to replicate a specific template action. This can be used as a home based rehabilitation system for stroke survivors, where the objective is for patients to practice and improve their daily life activities. Patients are guided in how to correctly perform an action by following feedback proposals. These proposals are presented in a human interpretable way. In order to align an action that was performed with the template action, we explore two different approaches, namely, Subsequence Dynamic Time Warping and Temporal Commonality Discovery. The first method aims to find the temporal alignment and the second one discovers the interval of the subsequence that shares similar content, after which standard Dynamic Time Warping can be used for the temporal alignment. Then, feedback proposals can be provided in order to correct the user with respect to the template action. Experimental results show that both methods have similar accuracy rate and the computational time is a decisive factor, where Subsequence Dynamic Time Warping achieves faster results. [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 detailEnhanced Trajectory-based Action Recognition using Human Pose
Papadopoulos, Konstantinos UL; Goncalves Almeida Antunes, Michel UL; Aouada, Djamila UL et al

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

Action recognition using dense trajectories is a popular concept. However, many spatio-temporal characteristics of the trajectories are lost in the final video representation when using a single Bag-of ... [more ▼]

Action recognition using dense trajectories is a popular concept. However, many spatio-temporal characteristics of the trajectories are lost in the final video representation when using a single Bag-of-Words model. Also, there is a significant amount of extracted trajectory features that are actually irrelevant to the activity being analyzed, which can considerably degrade the recognition performance. In this paper, we propose a human-tailored trajectory extraction scheme, in which trajectories are clustered using information from the human pose. Two configurations are considered; first, when exact skeleton joint positions are provided, and second, when only an estimate thereof is available. In both cases, the proposed method is further strengthened by using the concept of local Bag-of-Words, where a specific codebook is generated for each skeleton joint group. This has the advantage of adding spatial human pose awareness in the video representation, effectively increasing its discriminative power. We experimentally compare the proposed method with the standard dense trajectories approach on two challenging datasets. [less ▲]

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See detailDeformation Based Curved Shape Representation
Demisse, Girum UL; Aouada, Djamila UL; Ottersten, Björn UL

in IEEE Transactions on Pattern Analysis & Machine Intelligence (2017)

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an ... [more ▼]

In this paper, we introduce a deformation based representation space for curved shapes in Rn. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g. local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets. [less ▲]

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See detailFraud Detection by Stacking Cost-Sensitive Decision Trees
Correa Bahnsen, Alejandro; Villegas, Sergio; Aouada, Djamila UL et al

in Data Science for Cyber-Security (DSCS), London 25-27 September (2017)

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See detailReal-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations
Al Ismaeil, Kassem; Aouada, Djamila UL; Solignac, Thomas et al

in IEEE Transactions on Pattern Analysis & Machine Intelligence (2016), 39(10), 2045-2059

We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low ... [more ▼]

We propose a novel approach for enhancing depth videos containing non-rigidly deforming objects. Depth sensors are capable of capturing depth maps in real-time but suffer from high noise levels and low spatial resolutions. While solutions for reconstructing 3D details in static scenes, or scenes with rigid global motions have been recently proposed, handling unconstrained non-rigid deformations in relative complex scenes remains a challenge. Our solution consists in a recursive dynamic multi-frame superresolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per-pixel tracking where both depth measurements and deformations are dynamically optimized. The geometric smoothness is subsequently added using a multi-level L1 minimization with a bilateral total variation regularization. The performance of this method is thoroughly evaluated on both real and synthetic data. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time. [less ▲]

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See detailSimilarity Metric For Curved Shapes In Euclidean Space
Demisse, Girum UL; Aouada, Djamila UL; Ottersten, Björn UL

in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 (2016, June 26)

In this paper, we introduce a similarity metric for curved shapes that can be described, distinctively, by ordered points. The proposed method represents a given curve as a point in the deformation space ... [more ▼]

In this paper, we introduce a similarity metric for curved shapes that can be described, distinctively, by ordered points. The proposed method represents a given curve as a point in the deformation space, the direct product of rigid transformation matrices, such that the successive action of the matrices on a fixed starting point reconstructs the full curve. In general, both open and closed curves are represented in the deformation space modulo shape orientation and orientation preserving diffeomorphisms. The use of direct product Lie groups to represent curved shapes led to an explicit formula for geodesic curves and the formulation of a similarity metric between shapes by the $L^{2}$-norm on the Lie algebra. Additionally, invariance to reparametrization or estimation of point correspondence between shapes is performed as an intermediate step for computing geodesics. Furthermore, since there is no computation of differential quantities on the curves, our representation is more robust to local perturbations and needs no pre-smoothing. We compare our method with the elastic shape metric defined through the square root velocity (SRV) mapping, and other shape matching approaches [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 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 detailRGB-D and Thermal Sensor Fusion
Rocco, Ignacio; Goncalves Almeida Antunes, Michel UL; Aouada, Djamila UL et al

in 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2016)

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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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