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All Research Topics @ISPGroup

Compressive Schlieren Deflectometry: This project concerns with the application of compressive sensing principles for characterizing transparent objects using schlieren deflectometry. This is an instance of real world applications of compressed sensing.

Respiratory motion model of lungs and tumor from skin surface as surrogate for radiotherapy: Respiratory induced motion of organs and tumors is a technical challenge in radiation therapy since it compromises treatment accuracy. Recently available optical surface scanners have opened the door of modeling and predicting internal motion from external skin surface motion.

Adaptive Video Access and Personnalized Summarization: Today’s media consumption evolves towards increased user-centric adaptation of contents, to meet the requirements of users having different expectations in terms of story-telling and heterogeneous constraints in terms of access devices. We propose personnalized summarization mechanisms, and adaptive streaming solutions to address this trend.

Imaging the Brain Microstructure: Diffusion tensor imaging, the most common model of the diffusion signal, is unable to represent the signal arising from water molecules diffusing in different compartments such as multiple fascicles and the extra-axonal space. Multi-fascicle models overcome this limitation by providing a parametric representation for each compartment, allowing the development of new brain network modeling.

Online Convolutional Dictionary Learning for Multimodal Imaging: Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this work, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with TV regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications.

Compressed Sensing and High Resolution Quantization: Measurement quantization is a critical step in the design and in the dissemination of new technologies implementing the Compressed Sensing (CS) paradigm. Quantization is indeed mandatory for transmitting, storing and even processing any data sensed by a CS device.

Compressive Optical Deflectometric Tomography: The refractive index map allows the optical characterization of complex transparent materials such as optical fibers or intraocular lenses. This research topic addresses the problem of reconstructing the refractive index map of a transparent object from few amount of optical deflectometric measurements. We aim at developing a numerical reconstruction method which makes Optical Deflectometric Tomography compressive and robust to noise.

Bridging 1-bit and High-Resolution Quantized Compressed Sensing with QIHT: In the framework of Quantized Compressed Sensing, we tried to bridge two extreme cases: 1-bit and high resolution quantization. The requirement of consistency of the reconstructed signal with quantized measurement led us to a new reconstruction algorithm called Quantized IHT (QIHT) that outperforms classical algorithms (IHT and BPDN) at low resolutions.

Blind Image Deconvolution for Non-parametric PSF estimation in Solar Astronomy: This research proposes to estimate the point spread function (PSF) of an telescope from the observation of the transit of a celestial body (e.g., the Moon or Venus) in front of the Sun. This is achieved by solving a regularized blind deconvolution problem.

Bilinear and Biconvex Inverse Problems for Computational Sensing Systems: A research effort in the solution of blind calibration and deconvolution problems arising in compressive imaging.

Imaging the Wallerian degeneration in the spinal cord: This research is focused on the Wallerian degeneration process in the spinal cord. The purpose is to identify the best biomarkers into the observations of Wallerian degeneration process by diffusion weighted imaging in magnetic resonance imaging.

Fusion-based techniques to enhance images and videos: One of the most successful image fusion strategies is based on the Laplacian pyramid decomposition. In the context of multi-scale fusion, the Laplacian pyramid decomposition has recently been demonstrated to be effective for several challenging tasks to enhance images and videos.

Video segmentation: Image/video segmentation aims at partitioning the visual frames into non-overlapping areas with different semantical contents. It has tremendous applications in data compression, tracking, augmented reality, activity or object recognition, video annotation and video retrieval. Our group focus on fast and efficient segmentation methods, in such a way to extract content with high-level of abstraction from videos.

Foreground object detection: Background modeling and foreground mask extraction are key components of low-level computer vision systems. They aim at extracting moving objects in natural scenes observed with static cameras, and thereby often constitute preliminary steps to object recognition, scene understanding and behavioral analysis.

Sport player foreground detector reinforcement through visual texture classification: In the context of sport events, this project deals with the improvement of a player detector based on a foreground detector. It considers some visual texture features in order to discriminate true from false detections.

Ball detection and tracking: Foreground detector reinforcement through trajectory analysis: In the context of team sport events monitoring, the various phases of the game must be delimited and interpreted. In the case of a basketball game, the detection and the tracking of the ball are mandatory. However, one of the difficulties is that the ball is often occluded by players. This project deals with the detection of the ballistic trajectory of a ball thrown between two players or toward the basket. Ballistic trajectories are build on the 3D ball candidates previously detected at each time-stamp from a foreground detector.

Multiple object tracking with prior detections and graph formalisms: This project considers the tracking of multiple objects within video sequence(s). Fundamentally, it aims at formalising application scenarios in which reliability and the discriminability of the object features vary over time. In order to address problems involving large number of targets, and because automatic detection algorithms have gained maturity, our work assumes that a set of prior and plausible targets detections are available at each time instant.

Automatic Team Sport Coverage: Computer vision tools drive the automatic and democratic production of sport events video reports, by using scene analysis to select an appropriate viewpoint in a static and/or dynamic multi-camera infrastructure.

Target tracking for the automatic control of Pan Tilt Zoom cameras: Capturing close-up video sequences of an object of interest evolving in a large field of view often requires to cover this field of view with tens of cameras. This is especially the case in surveillance and sport coverage contexts. The use of Pan-Tilt-Zoom cameras allows zooming and focusing on an object along its displacement with a single camera, but requires a sufficiently reliable feedback about the target position/trajectory from the image processing module in order to perform high quality automatic tracking.

Deep learning for detection and analysis in team sport images: In the context of indoor team sport events, detection and characterization of objects of interest for game strategy analysis and autonomous production and broadcasting from Convolutional Neural Networks.

Detection of facial expressions and CG animation: This work is aimed at developing a semi-automate system to animate facial expressions. The system consists of detection of facial expressions and Computer Graphics animations of a facial charactor.

ImagX Research Group: iMagX is a joint project between UCL (Université catholique de Louvain, Belgium) and IBA, world leader in proton therapy. Our interdisciplinary R&D team of 20 engineers, computer scientists, physicists and PhDs, works at the intersection of the scientific, clinical and industrial worlds, in order to create innovative imaging solutions to improve cancer treatment in proton therapy and radiotherapy.

Last updated September 19, 2017, at 09:08 AM