Image science & computer vision team du Laboratoire Hubert Curien

Material Appearance and Image Analysis

Material appearance plays a central role in many domains, especially in (computational) printing, art design and visual diagnostics, where attributes as color, texture, brightness or translucency are of interest. The goal here is the analysis and prediction of such perceptual attributes from physical entities. Our objective approach combines photometry with image processing and, where appropriate, machine learning techniques. Models comprising light flux propagation and scattering are developed for this purpose.

Unconventional imaging

Combining imaging techniques and reconstruction algorithms extends the capabilities of conventional imaging. By working from optical system design to experimental set-ups, we can improve imaging for large telescopes, develop color lensless microscopy for microbiology, fluorescence and spectral imaging for color appearance. Thanks to an inverse problems approach, we can achieve a better reconstruction in digital holography and an improved image restauration in radar teledetection. A special focus is placed on the co-design of 3D imaging systems where the optical system and the image processing are jointly optimized.

Description and representation of images

In computer vision, to address high level semantic tasks as image recognition or scene analysis, modern approaches rely on new methods to describe and represent the visual information. In this research area we develop full representation models and algorithms including feature detection, local descriptions, statistical representation, machine learning, deep networks. We consider color, RGBD, infrared images or videos, and address applications as health, digital technologies or cultural heritage