“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.
In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.
Featured articles:
- De Weerdt, B. et al., “Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding” (https://ieeexplore.ieee.org/abstract/document/10094712)
- Yang X. et al., “Relevance Propagation through Deep Conditional Random Fields” (https://ieeexplore.ieee.org/abstract/document/10095075)
- Stergiou A. et al., “Play it back: Iterative attention for audio recognition” (https://ieeexplore.ieee.org/abstract/document/10096532, developed at the University of Bristol)
On Ferbruary 2 2021 at 15.00 Volodymyr Seliuchenko will defend his PhD entitled “Active pixels for high dynamic range and 3d imaging applications”.
Our world is being reshaped by machines which are getting closer to humans in perceptive and cognitive abilities enabling previously unimaginable applications Autonomous cars, mobile home assistant robots, drone delivery networks are just a few examples of the emerging disrupting technologies of this brave new world Accelerating trends in computational power availability fuel the evolution of artificial intelligence systems which become capable of digesting more and more information that, for systems interacting with the real world, must come from sensors These emerging mobile robotics applications rely heavily on the image and distance sensors to create awareness about their environment the quality of the sensory data, in most cases, determines the key performance parameters and system safety Real applications are often posing the sensory system challenging conditions pushing the sensor specifications to the limits and often calling for novel sensing and signal processing approaches
In this work, 2 D and 3 D image sensor systems, the key sensor components of mobile robotics, are discussed Firstly, quantum efficiency improvement methods and a method for dynamic range extension of 4 T image pixels that preserves 4 T pixel dark noise performance are proposed These quantum efficiency improvement methods and the dynamic range extension method can be applied to both 2 D and 3 D imaging Further, indirect Time of Flight 3 D image sensors are analyzed, and improved 3 D image sensors based on Current Assisted Photonic Demodulators are proposed Finally, a hybrid Time of Flight method that produces a time domain echo signal using photonic demodulator sensor is proposed and compared to direct Time of Flight methods.
At the award session of the International Federation of Medical and Biological Engineering IFMBE, ETRO’s work: In silico investigation of intra-abdominal pressure monitoring by means of transient radar method; A novel non-invasive solution based on body-wave interaction was selected as the 4th top paper of this year young investigator competition. Salar received the award on behalf of the team of Salar himself, Ali Pourkazemi1, Olsi Kamami, Kato Thibaut, Manu Malbrain, and Johan Stiens.
In total, around 100 abstracts were submitted in the first round and 12 of them were selected as the finalists.
On June 20th 2024 at 16:00, Yangxintong Lyu will defend their PhD entitled “DEEP-LEARNING-BASED MULTI-MODAL FUSION FOR TRAFFIC IMAGE DATA PROCESSING”.
Everybody is invited to attend the presentation in room I.0.02, or digitally via this link.
In recent years, deep-learning-based technologies have significantly developed, which is driven by a large amount of data associated with task-specific labels. Among the various formats used for representing object attributes in computer vision, RGB images stand out as a ubiquitous choice. Their value extends to traffic-related applications, particularly in the realms of autonomous driving and intelligent surveillance systems. By using an autonomous driving system, a car is capable of navigating and operating with diminished human interactions, while traffic conditions can be monitored and analysed by an intelligent system. Essentially, the techniques reduce human error and improve road safety, which significantly impacts our daily life.
Although many visual-based traffic analysis tasks can indeed be effectively solved by leveraging features extracted from a sole RGB channel, certain unresolved challenges persist that introduce extra difficulties under certain situations. First of all, extracting complicated information becomes demanding, especially under erratic lighting conditions, raising a need for auxiliary clues. Secondly, obtaining large-scale accurate labels for challenging tasks remains time-consuming, costly, and arduous. The former prompts exploration into capturing and exploiting additional information such that the objects can be observed from diverse aspects; in contrast, the latter requires either an increase in the volume of available data or the capability to learn from other datasets that already possess perfect labels.
In this thesis, we tackle multi-modal data fusion and data scarcity for intelligent transportation systems. Our first contribution is a novel RGB-Thermal fusion neural network for semantic segmentation. It ensures the segmentation under limited illumination. Our second contribution is a 3D-prior-based framework for monocular vehicle 6D pose estimation. The use of 3D geometry avoids the ill-posed pose prediction from a single camera viewpoint. Thanks to the extra 3D information, our novel method can handle distant and occluded vehicles. The third contribution is a real-world, large-scale vehicle make and model dataset that contains the most popular brands operating in Europe. Moreover, we propose a two-branch deep learning vehicle make and model recognition paradigm to reduce inter-make ambiguity. The last contribution is a weakly supervised vehicle 6D pose estimation paradigm by adapting knowledge built based on a novel synthetic dataset. The dataset includes a large amount of accurate labels for vehicles. By learning from the synthetic dataset, our method allows the significant reduction of expensive real-life vehicle pose annotations.
Comprehensive experimental results reveal that the newly introduced datasets hold significant promise for deep-learning-based processing of traffic image data. Moreover, the proposed methods surpass the existing baselines in the literature. Our research not only yields high-quality scientific publications but also underscores its value across both academic and industrial domains.
Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.
Two of ETRO’s postdocs, Angel for the project Equitable Oxymetry and Abel for the I-Healthy path project, have been selected for the MedTech accelerator: https://lifetech.brussels/en/medtech-accelerator-en/.
More info and pictures in these links:
After the conservation and restoration project for Jan Van Eyck’s masterpiece, the Royal Institute for Cultural Heritage documented both sides of the painting with hundreds of macro photography photos. Universum Digitalis then algorithmically assembled those images to produce gigapixel images of the artwork. The painting was previously digitized in 2015 using the same scientific protocol. Universum Digitalis seamlessly aligned both acquisitions, enabling a unique pixel-level comparison before and after restoration.
Comparison of the front and backside before and after restoration.
The restored painting will be exhibited at The Louvre Museum during the exhibition “Revoir Van Eyck – La Vierge du chancelier Rolin” from March 20th to June 17th, 2024. In parallel with the exhibition’s opening, the gigapixel images produced by Universum Digitalis will be made publicly accessible on the Closer to Van Eyck website.
https://www.louvre.fr/en/what-s-on/exhibitions/a-new-look-at-jan-van-eyck
