On March 31 2022 at 16.00 Tobias Birnbaum will defend his PhD entitled “A generic source coding. Methodology and architecture of dynamic holograms”.

Everybody is invited to attend the presentation live in room D.2.01 or online via https://us02web.zoom.us/j/84066799755?pwd=YkVseXFaVmlEQVM4ZU1leDBNdFN0Zz09#success

For wave phenomena, the holographic principle describes how, based upon light propagation laws and a recording of the amplitude and the phase of a wave front in one place – called a hologram – a wave front in another place can be obtained. The holographic principle can be applied to, among others any electro-magnetic wave. It has great impact on applications such as holographic microscopy, interferometry and non-destructive testing. Applied to visible light, holograms allow seamless observation of 3D content without any distortions or adversary effects such as mismatching visual cues. At sufficient space-frequency bandwidths, holograms become optically indistinguishable from reality and can be refocused at observation time. When those high-quality holograms became digitally accessible due to advances in processing power in recent years, manipulation, duplication, and computergeneration from purely synthetic content became feasible. Applied to macroscopic content, the most promising applications include preservation of cultural treasures, art, entertainment, educational purposes, medical imaging, surgical assistance, big data visualizations, and computer aided design. However, digital holograms can only convey as much information because of their large space-frequency bandwidths resulting in resolutions of several gigapixel. Thus compression becomes a necessity, especially for dynamic content. As holograms of visible light are based on the interference of diffracted coherent light, they look similar to the patterns visible on the surface of a pond, after throwing a hand full of pebbles into. In a numerical hologram, typically, each point in the scene influences every point in the hologram. Both facts together render signal characteristics of holograms conceptually very different from regular images and videos, and thus novel strategies to compress dynamic holograms need to be investigated.
This PhD thesis consists of several aspects necessary to design such strategies as well as a first proposition of a holographic video codec suitable for multipleindependently objects. Most contributions exploit heavily the concepts of spatial frequency (number of lines per unit length) and optical phase-space (also known as space-frequency or time-frequency domain). The novel contributions include: compression of static Fourier holograms based on wave atoms refinement of a STFT based static compresion scheme suited for all DH types a segmentation of holograms corresponding to scenes of multiple independently moving objects and resulting from it, a generic holographic motion compensation scheme for such scenes. From the latter an inter-frame compression strategy is derived and a generic video compression scheme is proposed. Further contributions concern, various contributions to subjective quality assessment of digital holograms a newly proposed versatile similarity measure for complex numbers and studies on speckle denoising of the back-propagated wave fields with the objective to find lowcomplexity algorithms with acceptable visual performance.

The motivation letter is very important and should clearly describe your background, experience, and goals in your career.

On September 21 2022 at 10.00, Pengpeng Hu will defend his PhD entitled “Deep learning-based 3D human body shape reconstruction from point clouds”.

Everybody is invited to attend the presentation online via this link.

3D reconstruction of the human body shape is a fundamental problem in computer vision, which is valuable for various human-centric applications such as computer animation, virtual reality, and clothing design, to name a few. 3D scanning is a popular technology for acquiring the geometry of a subject based on which a 3D body reconstruction can be produced. Although countless body scanners were developed to meet different industrial requirements and a lot of advanced algorithms were proposed for optimizing the reconstructed body models, many problems are still not properly solved. These problems, however, are difficult to address using conventional methods.

Recent years have witnessed the rapid development of artificial intelligence, especially deep learning. Encouraged by the significant success of deep learning in image processing, an increasing number of researchers attempted to extend deep learning to deal with 3D data. Following this trend, we proposed deep learning-based solutions to several challenges existing in modern 3D body scanning and reconstruction.

In this thesis, we focus on four challenges of 3D body scanning, namely, (1) estimation of body shape under clothing, (2) body reconstruction from impaired point clouds, (3) registration of non-overlapping point clouds, and (4) animatable body reconstruction using a single depth camera. The first challenge arises from the fact that existing 3D scanning solutions require the subjects to get scanned with minimal clothing as the scanning device can only record the outmost surface of objects. This scanning procedure is inconvenient to most people and is also an infringement of the right to privacy. The second challenge comes from the observation that impaired point clouds are common in practice but they lack a systematic study. Moreover, the problems of misalignment and problematic posture are neglected in existing solutions. The third challenge is a classical problem: partial point cloud registration. We found that existing methods mainly rely on the assumption that the source and the target point clouds have sufficient overlap and none of them could handle non-overlapping registration. The last challenge is addressed as many applications demand dynamic human body models. Traditional methods require expensive professional devices to produce such models.

We have addressed these four challenges by leveraging the deep learning paradigm. Our first contribution is to propose the first deep learning-based method in the literature for estimating the body shape under clothing from a single 3D dressed body scan. To facilitate the proposed model, a novel dataset consisting of large-scale dressed body scans and corresponding ground-truth body shapes is proposed. Our second contribution is a novel deep learning approach for jointly reconstructing an accurate body mesh and normalizing the posture of the human body model from a low-quality body point cloud in arbitrary postures. It proposes to directly reconstruct high-fidelity body shapes from impaired point clouds instead of attempting to point cloud repairments. Our third contribution is the first deep learning-based method in the literature to align non-overlapping partial point clouds. Using this method, an omnidirectional body can be obtained from only two non-overlapping body scans. The last contribution in this thesis is to propose a novel deep learning-based method to reconstruct an animatable body shape from only two depth images and at the same time allow for large pose variations between the camera shots.

Extensive experiments based on different datasets have demonstrated that the proposed methods outperform the reference methods from the literature. Our work has resulted in numerous high-quality scientific publications and has demonstrated impact at both academic and industrial levels.

De Jonge Academie heeft elf nieuwe leden verkozen na een open oproep. Het is ons een genoegen u te mogen melden dat prof. dr. Jeroen Van Schependom de Jonge Academie binnenkort vervoegt als topwetenschapper.

Jeroen Van Schependom (VUB) onderzoekt hoe structurele en functionele beeldvorming van de hersenen kunnen bijdragen aan een betere opvolging van mensen met neurodegeneratieve aandoeningen zoals multiple sclerose en de ziekte van Alzheimer. Daarnaast onderzoekt hij of nieuwe niet-invasieve methodes van hersenstimulatie kunnen helpen om deze ziektes af te remmen

Jeroen blijft lid tot 31 maart 2027. Wij kijken binnen de Jonge Academie erg uit naar samenwerking met hem. Jeroen wordt plechtig geïnaugureerd op woensdagnamiddag 30 maart 2022 om 15u30 in het Paleis der Academiën in Brussel, een gelegenheid waarop wij u van harte uitnodigen. Deze inauguratiezitting is ook een thema-event over Homo Ludens: toeval, serendipiteit en spel(impuls) in de wetenschap — naast een lecture performance, inauguratie en afzwaai stellen we er ook de Maja #7 Homo Ludens voor en het kinderspel

• From 1990 onwards, publications and PhDs are regularly produced, and two world firsts are realised: an electro-optical switch (optical thyristor) with a sensitivity of 18aJ and a surface-emitting LED with a quantum efficiency larger than 50 %. Research themes are now in Photonics and Opto-electronics and Artificial Life. LAMI counts approximately 20 collaborators.
• First integrated circuit design and realization: a median filter for image processing in collaboration with Humboldt Universität zu Berlin

Evaluate the variations of your blood volume with photoplethysmography – ETRO-VUB

Experiment and discover how light-based devices can provide information about your health. You’ll find out all about photoplethysmography and its biomedical applications. This non-invasive technology provides a lot of information about the human body.