Welcome in the Image and Signal Processing Group
Researchers of the ISPGroup use and develop signal and image processing techniques for applications like:
- Signal Acquisition, Compression & Streaming
- (Multiple) Object Tracking
- Content-based Data Retrieval
- Biomedical Signal & Medical Image Processing
- Watermarking of Multi-Dimensional Signals
- Compressed Sensing and Inverse Problem Solving (e.g., in Optics, Astronomy, Medical Imaging)
- Hyperspectral Imaging: Theory, Models, Algorithms and Bio-Medical Applications
- Tomographic Methods in Bio-Medical Applications (CT, CBCT, ...)
- Sparse Signal Representation, Restoration and Processing
- Quantitative Analysis of Microscopy Images
- Machine Learning and Deep Learning for Image Processing and Computer Vision
- June 18th, 2019: Best paper award at Computer Vision in Sports workshop in CVPR2019 for the joint work with ULiège:
A. Cioppa, A. Deliege, M. Istasse, C. De Vleeschouwer, Marc Van Droogenbroeck, ARTHuS: Adaptive Real-Time Human Segmentation in Sports through Online Distillation, Computer Vision in Sports, CVPR 2019.
- June 2019: Welcome to Long Beach! See our 4 contributions to ICML 2019 and CVPR 2019:
- S. Carbonnelle and C. De Vleeschouwer, 'Layer rotation: a surprisingly simple indicator of generalization in deep networks?', Workshop on Identifying and Understanding Deep Learning Phenomena, ICML 2019.
- M. Istasse, J. Moreau, and C. De Vleeschouwer, Associative Embedding for Team Discrimination, Computer Vision in Sports, CVPR 2019.
- A. Cioppa et al., ARTHuS: Adaptive Real-Time Human Segmentation in Sports through Online Distillation, Computer Vision in Sports, CVPR 2019, joint work with ULiège.
- B. Brummer and C. De Vleeschouwer, Natural Image Noise Dataset, New Trends in Image Restoration and Enhancement workshop, CVPR 2019.
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Next and Last ISP Seminars:
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The ISPGroup is active in the following main research topics:
Sensing, Imaging and Rendering: What is the most efficient way to sense a signal or to image reality? How can the sensing stage already "compute" information in a way that "just the information we need" is captured? This is the general research topic that we address here. This covers fields like, inverse problem solving, compressed sensing and compressive imaging, data restoration techniques, hyperspectral and light field imaging, or virtual view point rendering.
Representation and Communication: How can we efficiently represent an image signal, or general data in as few parameter as possible? Which kind of (greedy) algorithm can deduce such parameters? How can this "sparse" representation be transmitted in a compact bitstream, potentially under latency and bitbudget constraints? How can we adapt the forwarded content to semantic user requirements? How can we build personalized summaries of edited video feeds automatically?
Analysis and Interpretation: Video are segmented into semantically meaningful objects, based on texture and motion analysis. Images descriptors are extracted to characterize visual contents. Targets of interest are detected, recognized, and tracked to understand behaviors in natural scenes. Application domains include autonomous production of visual reports (e.g. for team sport events), but also intelligent vision in surveillance, or cells images analysis in biology.
Medical Image Processing: Our group develops image & signal processing tools and models for the use in various medical contexts, including radiotherapy, proton therapy, brachytherapy, surgery, EEG analysis, kinematic, diffusion tensor imaging, etc.
Focus on a randomly picked research topic:
Deep Learning for Anomaly Detection in Industrial Vision: In order to detect anomaly in an unsupervised scheme, an autoencoder is trained to reconstruct clean images out of defect-free images corrupted with synthetic noise. During inference an arbitrary (with or without anomaly) image is projected onto the normal space of images. The intensity of the residual map between the original image and its reconstruction estimates the likelihood of a region to be defective.