Dr. Jean-Pierre Radoux Profile

Dr. Jean-Pierre Radoux

at Altran Technologies

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Publications (5)

Proceedings Article | 1 April 2020 Presentation + Paper

Touchless interface interaction by hand tracking with a depth camera and a convolutional neural network

Audrey Leong-Hoi, Sarah Murat, Tanguy Acker, Quentin Sattler, Jean-Pierre Radoux

Proceedings Volume 11353, 113531Z (2020) https://doi.org/10.1117/12.2555676

KEYWORDS: Human-machine interfaces, Cameras, 3D modeling, Surgery, Convolutional neural networks, Computing systems, Neural networks, Gesture recognition, RGB color model, 3D vision

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Proceedings Article | 1 April 2020 Presentation + Paper

Catheter tracking and data fusion for reducing the x-ray exposition in an interventional radiology procedure

Jesus Zegarra Flores, Hugues De Lastic Saint Jal, Antoine Oberle, Jean Pierre Radoux

Proceedings Volume 11353, 1135308 (2020) https://doi.org/10.1117/12.2553116

KEYWORDS: Sensors, X-rays, Filtering (signal processing), Head, Image-guided intervention, X-ray imaging, Data fusion, Image fusion, Image processing, Image segmentation

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Proceedings Article | 30 March 2020 Presentation + Paper

Non-rigid registration of 3D points clouds of deformed liver models with Open3D and PyCPD

Audrey Leong-Hoï, Anaïs Chambrial, Marine Collet, Jean-Pierre Radoux

Proceedings Volume 11351, 113511N (2020) https://doi.org/10.1117/12.2555673

KEYWORDS: Clouds, Liver, 3D modeling, Image registration, Rigid registration, Image processing, Detection and tracking algorithms, Cameras, Augmented reality, Statistical modeling

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Proceedings Article | 24 May 2018 Presentation + Paper

Conception of a touchless human machine interaction system for operating rooms using deep learning

Florian Pereme, Jesus Zegarra Flores, Mirko Scavazzin, Franck Valentini, Jean-Pierre Radoux

Proceedings Volume 10679, 106790R (2018) https://doi.org/10.1117/12.2319141

KEYWORDS: Surgery, Human-machine interfaces, Neural networks, Cameras, RGB color model, Convolutional neural networks, Data modeling, Zoom lenses, Contamination, Image segmentation

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Touchless Human-Computer Interaction (HMI) is important in sterile environments, especially, in operating rooms (OR). Surgeons need to interact with images from scanners, rayon X, ultrasound images, etc. Problems about contamination may happen if surgeons must touch a keyboard or the mouse. To reduce the contamination and to give the possibility to the surgeon to be more autonomous during the operation, different projects have been developed in the Medic@ team from 2011. In order to recognize the hand and the gestures, two main projects: Gesture Tool Box and K2A; based on the use of the Kinect’s device (with a depth camera) have been prototyped. The detection of the hand gesture was done by segmentation and hand descriptors on RGB images, but always with a dependency on the depth camera (Kinect) to the detection of the hand. Additionally, this approach does not give the possibility that the system adapts to a new gesture demanded by the end-user, for example, if a new gesture is demanded, a new algorithm must be programed and tested. Thanks to the evolution of NVDIA cards to reduce time processing algorithms for CNN, the last approach explored was the use of the deep learning algorithms. The Gesture tool box project done was to analyze the hand gesture detection using a CNN (pre-trained in VGG 16) and transfer learning. The results were very promising showing 85% of accuracy for the detection of 10 different gestures form LSF ( French Sign Language) and also it was possible to create a user interface to give autonomy to the end user to add his own gesture and to do the transfer learning automatically. However, we still had some problems about the real time delay (0,8s) recognition and the dependency of the Kinect device. In this article, a new architecture is proposed, in which we want to use standard cameras and to reduce the real time delay of the hand and gesture detection. The state of the art shows the use of a YOLOv2 using Darknet framework as a good option with faster time recognition compared to other CNN. We have implemented YOLOv2 for the detection of the hand and signs with good results in gesture detection and with 0.10 seconds on gesture time recognition in laboratory conditions. Future work will include reducing the errors of our model, recognizing intuitive and standard gestures and doing tests in real conditions.

Proceedings Article | 18 March 2016 Paper

Multiple video sequences synchronization during minimally invasive surgery

Abdelkrim Belhaoua, Johan Moreau, Alexandre Krebs, Julien Waechter, Jean-Pierre Radoux, Jacques Marescaux

Proceedings Volume 9786, 97861S (2016) https://doi.org/10.1117/12.2216881

KEYWORDS: Video, Surgery, Video processing, Video compression, Laparoscopy, Machine vision, Computer vision technology, Cameras, Embedded systems, Augmented reality, Video surveillance, Field programmable gate arrays, Medical devices, Signal processing

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