Signal and Image Processing

Figure : Multiview people detection (ISPGroup)

ICTEAM researchers use and develop signal processing techniques to solve a problems ranging from physical layer problems, localisation or channel coding to image processing and recognition and networked media.

Principal Investigators :

Pierre-Antoine Absil, Christophe De Vleeschouwer, Laurent Jacques, Jérôme Louveaux, Benoit Macq, Paul Van Dooren, Luc Vandendorpe

Research Labs :

Image and Signal Processing Group (ISPGroup), INMA

Research Areas :

The research focuses on estimation and detection techniques for wired and wireless environments. Transmitters and receivers are designed and optimized for OFDM (multicarrier) transmission schemes, filter-bank based multicarrier modulation schemes, OFDMA systems, multiuser multiantenna systems (MIMO) and multicell/interference limited systems.
Particular attention is paid to iterative/soft information/turbo receivers both for detection and estimation (carrier phase, carrier frequency, channel state information) or synchronization. Optimization of modulation, coding and resource allocation are also investigated.

This research focuses on security achieved at the PHY layer. Particular attention is paid to scenarios where a transmitter wants to communicate to a legitimate receiver over parallel channels (for instance OFDM over frequency selective channels). An eavesdropper is able to capture information from some of the links. Along the work of cooperative techniques (relays) emphasis is put on transmission schemes helped by a relay. The objectives are to obtain bounds on secure transmission rates, investigate situations where non perfect CSI is available, and to design coding schemes to exploit the potential for secure communications.

This researches focuses on ultra wide band (UWB) based localisation or positioning. Methods considered are time of arrival (TOA), time difference of arrival (TDOA) and angle of arrival (AOA). Bounds have been derived to assess the potential of UWB, understand the impact of multipath propagation and investigate the ambiguities. Practical estimators are proposed and their performance is investigated. A practical testbed has been developed and is being upgraded. An accuracy of a few millimeters has been achieved for indoor positioning over distances of about ten meters and with obstacles.

Source coding and channel coding (or decoding) and transmission are often designed independently. This research track investigates scenarios where there is advantage or potential in jointly designing the encoders or decoders. The research focuses on bounds for some techniques, and practical schemes applying this philosophy, in particular for sensor networks.

Compression & streaming

Image and video compression algorithms are investigated, including for stereo and multi-view contents. Visually pleasant and fluent video streaming or image browsing are implemented by adapting compression and forwarding mechanisms to network and terminal resources. This implies the rate-distortion optimization of image/video packet schedules, but also adaptive switching between multiple versions of the content. For low bandwidth wireless accesses, interactive streaming architectures are investigated to allow the end-user to control the trade-offs involved when reducing the spatial and temporal resolution of the streamed content.

Content-based retrieval

Coarse-to-fine JPEG2000 image classification, active learning for surveillance scene retrieval.

Watermarking

Fingerprinting of mono and stereo images for Digital Cinema; 3D meshes watermarking.

Object & people detection and tracking algorithms are developed to understand behaviors in natural scenes. Application domains include autonomous production of visual reports (e.g. for team sport events), but also video-surveillance. Resource constrained allocation solutions are also proposed to build automatically personalized summaries of edited video feeds.

Another field of research is the solving of inverse problems from generalized sparsity prior (with applications in optics and X-ray CT), Compressed Sensing (theory and application), theoretical questions linked to the design of new sensors (for computer vision), applied mathematics for astronomical and biomedical signal processing questions, and representation of data on strange spaces (e.g., sphere, manifolds, or graphs).

This project aims at detecting segments or contours of objects in a given picture by means of graph-based techniques that unfold the community structures in a large graph. The communities found are also hierarchical, allowing to find subregions inside an object.

The research develops image & signal processing tools for the use in various biomedical contexts, including protein docking, radiotherapy, proton therapy, brachytherapy, surgery, EEG analysis, kinematic assessment through accelerometers, etc. 

  • Shape analysis for protein docking including 3D mesh processing and the analysis of protein surface properties; 
  • Radio/proton therapy: rigid and non-rigid image registrations methods for 2D-3D and 3D-3D images both for single and multiple modalities as well as for surfaces; 
  • Segmentation techniques either using prior knowledge (atlas-based) or allowing user interaction (graph cuts); 
  • EEG reconstruction, transcranial magnetic stimulation, as well as on the use of functional imaging for measuring motion disorders;
  • Accelerometers are used for kinematic analysis, e.g.  to quantify the motor disturbances due to Parkinson's disease or to measure objectively the effects of the rehabilitation process following a stroke;
  • Human-computer interactions to create intuitive user interfaces for the clinical world.

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all publications.


Journal Articles


1. Monnoyer de Galland de Carnières, Gilles; Feuillen, Thomas; Vandendorpe, Luc; Jacques, Laurent. Grid Hopping in Sensor Networks: Acceleration Strategies for Single-Step Estimation Algorithms. In: IEEE Transactions on Signal Processing, Vol. 72, no.1, p. 4463-4478 (2024). doi:10.1109/tsp.2024.3465842. http://hdl.handle.net/2078.1/292969

2. Mathys, Aurore; Pollet, Yann; Gressin, Adrien; Muth, Xavier; Brecko, Jonathan; Dekoninck, Wouter; Vandenspiegel, Didier; Jodogne, Sébastien; Semal, Patrick. Sphaeroptica: A tool for pseudo-3D visualization and 3D measurements on arthropods. In: PLOS ONE, Vol. 19, no.10, p. 1-32 (2024). doi:10.1371/journal.pone.0311887. http://hdl.handle.net/2078.1/292875

3. Kirkove, D; Ben Mustapha, S; Jodogne, Sébastien; Pétré, B. L'utilisation de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient en radiothérapie : étude de faisabilité. In: Revue médicale de Liège, Vol. 79, no.S1, p. 100-106 (2024). http://hdl.handle.net/2078.1/287399

4. Jodogne, Sébastien. Apport du logiciel libre en imagerie médicale. In: Revue médicale de Liège, Vol. 79, no.S1, p. 75-83 (2024). http://hdl.handle.net/2078.1/287398

5. Yildirim, Hasan; Storrer, Laurent; De Doncker, Philippe; Louveaux, Jérôme; Horlin, François. A multi-antenna super-resolution passive Wi-Fi radar algorithm: Combined model order selection and parameter estimation. In: IET Radar, Sonar & Navigation, Vol. 16, no.8, p. 1376-1387 (2023). doi:10.1049/rsn2.12267. http://hdl.handle.net/2078.1/278442

6. Dirksen, Sjoerd; Genzel, Martin; Stollenwerk, Alexander; Jacques, Laurent. The Separation Capacity of Random Neural Networks. In: Journal of Machine Learning Research, Vol. 23, no.209, p. 1--47 (2022). (Accepté/Sous presse). http://hdl.handle.net/2078.1/267732

7. Schellekens, Vincent; Jacques, Laurent. Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches. In: IEEE Transactions on Signal Processing, Vol. 70, no.1, p. 1348-1360 (2022). doi:10.1109/tsp.2022.3157486. http://hdl.handle.net/2078.1/265613

8. Guérit, Stéphanie; Sivankutty, Siddharth; Lee, John Aldo; Rigneault, Hervé; Jacques, Laurent. Compressive Imaging Through Optical Fiber with Partial Speckle Scanning. In: SIAM Journal on Imaging Sciences, Vol. 15, no.2, p. 387-423 (2022). doi:10.1137/21m1407586. http://hdl.handle.net/2078.1/260300

9. Benjilali, Wissam; Guicquero, William; Jacques, Laurent; Sicard, Gilles. Hardware-Compliant Compressive Image Sensor Architecture Based on Random Modulations and Permutations for Embedded Inference. In: IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 67, no.4, p. 1218-1231 (2020). doi:10.1109/tcsi.2020.2971565. http://hdl.handle.net/2078.1/242057

10. Moshtaghpour, Amirafshar; Bioucas-Dias, Jose M.; Jacques, Laurent. Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling with Haar Wavelet Basis. In: IEEE Transactions on Information Theory, Vol. 66, no. 11, p. 7253 - 7273 (2020). doi:10.1109/tit.2020.2992852 (Accepté/Sous presse). http://hdl.handle.net/2078.1/229736


Conference Papers


1. Sechaud, Victor; Jacques, Laurent; Tachella, Julián. Equivariance-based self-supervised learning for audio signal recovery from clipped measurements. In: Proc of 32nd European Signal Processing Conference (EUSIPCO). Vol. 1, no.1, p. 852 (2024). 2024 xxx. http://hdl.handle.net/2078.1/292968

2. Semal, Patrick; Mathys, Aurore; Brecko, Jonathan; Chapman, Tara; Pollet, Yann; Herpers, Jean-Marc; Theeten, Franck; Van den Spiegel, Didier; Tilleux, Caroline; Angenon, Els; Jodogne, Sébastien. Serveur multimédia DICOM pour le partage des numérisations des collections anthropologiques. 2024 xxx. http://hdl.handle.net/2078.1/292682

3. Sadre, Wei; Huet-Dastarac, Margerie; Deffet, Sylvain; Sterpin, Edmond; Barragan Montero, Ana Maria; Jodogne, Sébastien; Lee, John Aldo. PARROT - A Versatile Platform for AI-Driven Image Segmentation and Dose Prediction. In: Proc. of the 5th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024). 2024 xxx. http://hdl.handle.net/2078.1/291340

4. Langlois, Quentin; Jodogne, Sébastien. Embeddings for Motor Imagery Classification. In: Proc. of IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP 2024). p. 1-6. IEEE, 2024 xxx. doi:10.1109/MLSP58920.2024.10734730. http://hdl.handle.net/2078.1/291339

5. Chatzopoulos, Edouard; Jodogne, Sébastien. Assessing the Impact of Deep Learning Backbones for Mass Detection in Breast Imaging. In: Lecture Notes in Computer Science. Vol. 14976, p. 33-47 (2024). Springer Nature Switzerland: Cham, Switzerland, 2024 xxx. doi:10.1007/978-3-031-67285-9_3. http://hdl.handle.net/2078.1/289661

6. Chatzopoulos, Edouard; Jodogne, Sébastien. Integrated and Interoperable Platform for Detecting Masses on Mammograms. In: Studies in Health Technology and Informatics. Vol. 316, p. 1103-1107 (2024). IOS Press, 2024 xxx. doi:10.3233/SHTI240603. http://hdl.handle.net/2078.1/289660

7. Jodogne, Sébastien. Plateforme libre, intégrée et interopérable pour la détection de masses en mammographie. 2024 xxx. http://hdl.handle.net/2078.1/289658

8. Langlois, Quentin; Szelagowski, Nicolas; Vanderdonckt, Jean; Jodogne, Sébastien. Open Platform for the De-identification of Burned-in Texts in Medical Images using Deep Learning. In: Proc. of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024). Vol. 1, p. 297-304 (2024). SCITEPRESS – Science and Technology Publications, Lda. 2024 xxx. doi:10.5220/0012430300003657. http://hdl.handle.net/2078.1/282801

9. Jodogne, Sébastien. Setting a PACS on FHIR. In: Proc. of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024). Vol. 2, p. 123-131 (2024). SCITEPRESS - Science and Technology Publications, Lda. 2024 xxx. doi:10.5220/0012384600003657. http://hdl.handle.net/2078.1/281131

10. Daglayan Sevim, Hazan; Vary, Simon; Leplat, Valentin; Gillis, Nicolas; Absil, Pierre-Antoine. Direct Exoplanet Detection Using L1 Norm Low-Rank Approximation. In: Proceedings of BNAIC/BeNeLearn 2023. p. 1-13 (2023). arXiv, 2023 xxx. doi:10.48550/arXiv.2304.03619. http://hdl.handle.net/2078.1/281059