Publications of Samir Azrour
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See detailA two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Geurts, Pierre ULiege et al

in Advanced Concepts for Intelligent Vision Systems (2017, September)

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we ... [more ▼]

In this paper, we present a two-step methodology to improve existing human pose estimation methods from a single depth image. Instead of learning the direct mapping from the depth image to the 3D pose, we first estimate the orientation of the standing person seen by the camera and then use this information to dynamically select a pose estimation model suited for this particular orientation. We evaluated our method on a public dataset of realistic depth images with precise ground truth joints location. Our experiments show that our method decreases the error of a state-of-the-art pose estimation method by 30%, or reduces the size of the needed learning set by a factor larger than 10. [less ▲]

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See detailLeveraging orientation knowledge to enhance human pose estimation methods
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

in Articulated Motion and Deformable Objects AMDO 2016 (2016, July)

Predicting accurately and in real-time 3D body joint positions from a depth image is the cornerstone for many safety, biomedical, and entertainment applications. Despite the high quality of the depth ... [more ▼]

Predicting accurately and in real-time 3D body joint positions from a depth image is the cornerstone for many safety, biomedical, and entertainment applications. Despite the high quality of the depth images, the accuracy of existing human pose estimation methods from single depth images remains insufficient for some applications. In order to enhance the accuracy, we suggest to leverage a rough orientation estimation to dynamically select a 3D joint position prediction model specialized for this orientation. This orientation estimation can be obtained in real-time either from the image itself, or from any other clue like tracking. We demonstrate the merits of this general principle on a pose estimation method similar to the one used with Kinect cameras. Our results show that the accuracy is improved by up to 45.1 %, with respect to a method using the same model for all orientations. [less ▲]

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See detailImproving pose estimation by building dedicated datasets and using orientation
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

Poster (2016, May 19)

Markerless pose estimation systems are useful for various applications including human- computer interaction, activity recognition, security, gait analysis, and computer-assisted medical interventions ... [more ▼]

Markerless pose estimation systems are useful for various applications including human- computer interaction, activity recognition, security, gait analysis, and computer-assisted medical interventions. They have attracted much interest since the release of low-cost depth cameras such as Microsoft’s Kinect camera. Shotton et al. and Girshick et al. pioneered tractable methods that infer a full-body pose reconstruction in real-time. Despite this technological breakthrough, the accuracy of human pose estimation from single depth images remains insufficient for some applications. Our work aims at building a simulation environment to create images databases suited for any camera position and improving the mainstream machine learning-based pose estimation algorithms. [less ▲]

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See detailDefining a score based on gait analysis for the longitudinal follow-up of MS patients
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

in Multiple Sclerosis Journal (2015, October 09), 23(S11), 408-409

BACKGROUND. The project GAIMS [ECTRIMS 2013 P800] aims at developing a gait measuring system particularily suited for the clinical routine, and providing a reference database with the gait characteristics ... [more ▼]

BACKGROUND. The project GAIMS [ECTRIMS 2013 P800] aims at developing a gait measuring system particularily suited for the clinical routine, and providing a reference database with the gait characteristics of many MS patients (MSP) and healthy people (HP). As the gait impairments are related to the disease progression, defining an objective and quantitative score based on the gait characteristics would be useful for the longitudinal follow-up. Based on the dataset of GAIMS and machine learning techniques (MLT), a score, well correlated with the EDSS, can be defined [Azrour et al. ESANN 2014]. OBJECTIVE. Burggraaff et al. [ECTRIMS 2014 P033] showed that paired comparisons can help human raters to better judge the state of the patients. In the same spirit, we aim at predicting the difference of EDSS between two persons or between two visits of a same person, based on clinical gait measures. We show that the pairwise comparison strategy leads to a score (Gait-Score) well correlated with the EDSS and sensitive to small modifications of the gait. METHODS. The gait of 162 HP and 72 MSP (44 with EDSS>3) has been recorded and analyzed with GAIMS. The Gait-Score is defined using the MLT of [Geurts et al. 2006]. We can compute the Gait-Score of a person by comparing him to others with known EDSS, and compute the difference of Gait-Score of a same person at two different moments. We measure the merits of the Gait-Score by the correlation between the predicted Gait-Score and the EDSS, as well as the ability to detect subtle gait deteriorations among people with ataxia induced by a low dose of alcohol (data of [Piérard et al. ESANN 2014]). RESULTS. The Gait-Score is well correlated with the EDSS (Pearson’s correlation=0.8743). Moreover, it manages to detect a gait deterioration after a small alcohol intake for 19 persons out of 24 (79% correct) which is much better than what was obtained by visual inspection of neurologists (62% according to [Piérard et al. ESANN 2014]). CONCLUSIONS. Based on the accurate gait measures provided by GAIMS, we are able to derive a Gait-Score, automatically, that is well correlated with the EDSS. Moreover, this score is able to detect subtle deteriorations of the gait caused by a low dose of alcohol. These results reinforce our conviction that the use of an automatic method based on gait analysis is very promising for the longitudinal follow-up of MS patients and the assessment of the impact of new drugs and rehabilitation programs. [less ▲]

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See detailDiagnosing multiple sclerosis with a gait measuring system, an analysis of the motor fatigue, and machine learning
Pierard, Sébastien ULiege; Azrour, Samir ULiege; Phan-Ba, Rémy et al

in Multiple Sclerosis Journal (2014, September 11), 20(S1), 171

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See detailDesign of a reliable processing pipeline for the non-intrusive measurement of feet trajectories with lasers
Pierard, Sébastien ULiege; Azrour, Samir ULiege; Van Droogenbroeck, Marc ULiege

in International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2014, May)

Reliable measurements of feet trajectories are needed in some applications, such as biomedical applications. This paper describes the data processing pipeline used in GAIMS, which is a non-intrusive ... [more ▼]

Reliable measurements of feet trajectories are needed in some applications, such as biomedical applications. This paper describes the data processing pipeline used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple range laser scanners. Our processing pipeline relies on a new tracking paradigm, and it is based on two innovative algorithms: the first algorithm localizes the feet directly from the observed point cloud without any clustering, and the other algorithm identifies the feet. After reviewing the various types of noise affecting the point cloud, this paper explains the limitations of the classical processing approach and gives an overview of our new pipeline. The effectiveness of the proposed approach is established by discussing the results that have been obtained in several studies based on GAIMS. [less ▲]

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See detailData normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Geurts, Pierre ULiege et al

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2014, April)

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised ... [more ▼]

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised learning, the condition of patients with multiple sclerosis based on their gait descriptors obtained with a gait analysis system. As the morphological characteristics of individuals influence their gait while being in first approximation independent of the disease level, an original strategy of data normalization with respect to these characteristics is described and applied beforehand in order to obtain more reliable predictions. In addition, we explain how we address the problem of missing data which is a common issue in the field of clinical evaluation. Results show that, based on machine learning combined to the proposed data handling techniques, we can predict a score highly correlated with the condition of patients. [less ▲]

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See detailMeasuring feet trajectories: challenges and applications
Pierard, Sébastien ULiege; Azrour, Samir ULiege; Van Droogenbroeck, Marc ULiege

Conference (2013, November 07)

Measuring reliable feet trajectories is needed in many applications. This paper provides the principles used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple ... [more ▼]

Measuring reliable feet trajectories is needed in many applications. This paper provides the principles used in GAIMS, which is a non-intrusive system that measures feet trajectories based on multiple range laser scanners. We present the technical challenges that we had to address, as well as an overview of the implemented processing pipeline of GAIMS. [less ▲]

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See detailUsing GAit Measuring System (GAIMS) to discriminate patients with multiple sclerosis from healthy person
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Van Droogenbroeck, Marc ULiege

Poster (2013, November 07)

Among voluntary movements, gait is the most affected by multiple sclerosis. Gait impairment is also a good indicator of the disease progression. However, measurement of gait character- istics made by ... [more ▼]

Among voluntary movements, gait is the most affected by multiple sclerosis. Gait impairment is also a good indicator of the disease progression. However, measurement of gait character- istics made by neurologists is usually limited to the use of a stopwatch. The GAit Measuring System (GAIMS), provides a wider range of measurements that allow the definition of several relevant gait descriptors. The work presented here shows the effectiveness of these gait descriptors and machine learning techniques to discriminate between healthy persons and patients with multiple sclerosis. [less ▲]

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See detailGAIMS: A Reliable Non-Intrusive Gait Measuring System
Pierard, Sébastien ULiege; Azrour, Samir ULiege; PHAN BA, Remy ULiege et al

in ERCIM News (2013), 95

Gait observation and analysis can provide invaluable information about an individual [1]. Studies that have interpreted gait using traditional imaging devices have demonstrated that it is difficult to ... [more ▼]

Gait observation and analysis can provide invaluable information about an individual [1]. Studies that have interpreted gait using traditional imaging devices have demonstrated that it is difficult to make reliable measurements with colour cameras. GAIMS, our new system resulting from a multidisciplinary project born from collaboration between engineers and neurologists, aims at developing non-intrusive and reliable tools to provide quantitative measures of gait and interpretations of the acquired data. Following a current trend in imaging, it takes advantage of imaging sensors that measure distance instead of colour. While its principles are general, GAIMS is currently used for the diagnosis of multiple sclerosis (MS) and the continued evaluation of disease progression [2]. It is the first available system to fully satisfy the clinical routine and its associated constraints. [less ▲]

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