Image Analysis Applications

X-RAY TOMOGRAPHY WITH SMALL NUMBER OF VIEWS
DATA : X-Ray, Ultrasound, Foucault, MRI, Infra-Red, Radar, Satellite

MARKOV FIELDS AND LOW LEVEL VISION TASKS
1986-1992

Simultaneously, feeling that 3D Markov random field techniques were a powerful conceptual tool to smooth out local detections of 3D features, I launched with the SUDIMAGE group a series of exploratory R&D applications of 3D Markov random fields to the efficient spatial reconstruction of “matter density” distribution in opaque 3D-objects. In practical terms, the physical “image” data were here acquired through sophisticated physical devices and sensors such as X-Ray tomography, Ultrasound sensors, Foucault currents. Our intensive team scientific work (R. AZENCOTT, B. CHALMOND, F. COLDEFY, E. GOUBET, J.P. WANG) first enabled 3D Markov random field applications to multi-films X-Rays image restoration, as well as to reconstruction of small 3D “defects” within large opaque solids, by X-Ray tomography based on a very small number of X-Rays 2D-views. Such 3D reconstruction problems arose at CEA France (Center for Atomic Energy), for plasma monitoring in scientific small scale “cold nuclear experiments”, or for in depth safety inspection of massive equipments, as well at Electricite de France (EDF) in the context of safety control for large equipments in electric production plants.
The corresponding inverse problems are here severely ill-posed, and to solve them correctly, one must impose “regularizing” constraints on the unknown 3D density distribution of matter. We showed that adequate pragmatic constraints can reasonably be implemented by ad hoc 3D Markov fields. With the help of B. CHALMOND, I maintained a long term and very fruitful scientific collaboration with the R&D departments of EDF on these topics, beginning with support for the PhD thesis written by Francois COLDEFY under my direction. COLDEFY showed that Markov random fields were an efficient way of consistently patching together local “defects” informations extracted from small finite sets of X-Rays views of a 3D solid object. With EDF, we used similar, but technically much more complex relaxation methods, to reconstruct 3D defects on the basis of large datacubes recorded by industrial ultrasound sensors. Concretized in the PhD thesis of E. GOUBET, with the intelligent and creative support of F. COLDEFY, our ultrasound methodology implied massive pre-treatments by wavelet analysis along ultrasound main propagation rays, and Gibbs energy function modelization of desirable characteristics for 3D spatial distribution of defects. Actual 3D defects localization on connected compact sets of “voxels” was then achieved by stochastic relaxation to minimize the 3D energy function. I naturally gave numerous graduate courses on Markov Random Fields and computer vision within the Paris Universities so called “DEA cursus”, and summarized our main advances in an invited graduate course for the 1995 International Summer School on Image Analysis organized by Henri MAITRE (Ecole Nationale Telecoms) at Les Houches France.

DATA : X-Ray, Ultrasound, Foucault, MRI, Infra-Red, Radar, Satellite
1989-1999

Between 1985 and 1988, I clearly understood that research in artificial vision crucially needed to preserve a good mix of rigorous mathematical theory and of intensive computer validations on concrete industrial applications to automatic image analysis and computer vision. This required short-term R&D industrial collaborations and middle-term scientific partnerships with innovative large industrial companies and state subsidized institutions, as well as sizable financial support to buy and maintain adequate computing hardware, and to pay market level salaries for computing engineers and system engineers. This led me in 1989, after three years of purely theoretical work, to create SUDIMAGE, a self-supported advanced consulting group in image analysis, gathering on a part-time basis a dozen of young Phds listed in the preceding paragraphs, most of them applied mathematicians then linked to CNRS, ENS Cachan, University of Paris 11.The initial cash investment of $ 10,000 was put up by myself, B. CHALMOND, and C. GRAFFIGNE, and the group then succeeded in completely self-subsidizing its intensive R&D developments until 1999. As founder and scientific director of SUDIMAGE from 1989 to 1999, I had the sophisticated and dedicated support of three creative computer vision specialists (B. CHALMOND, C. GRAFFIGNE, L. YOUNES). This led to the realization of more than 30 advanced R&D industrial projects in applied image analysis, such as :
- 3D reconstruction : X-ray tomography with small number of views ( EDF, CEA-DAM )
- 3D reconstruction : Ultrasound image analysis ( EDF )
- 3D reconstruction : Foucault currents multi-images analysis ( FRAMATOME )
- 3D reconstruction : Magnetic Resonance Imaging ( GENERAL ELECTRIC )
- Automated quality control through computer vision ( PSA )
- Contour lines extraction and Satellite images segmentation ( MATRA, SAGEM, MS2I )
- Texture wavelet analysis and segmentation for infra-red images ( SEFT, SAT )
- Texture wavelet analysis and segmentation for radar images ( CELAR )
- Patterns extraction in sismic images analysis for geological exploration ( TOTAL, ELF )
- 2D shapes extraction in low level scene analysis ( DASSAULT,AEROSPATIALE )
- 2D shapes recognition ( CEA-DAM )
- 2D image content automatic indexation ( ALCATEL )
Thus SUDIMAGE provided for 10 years a fertile and quite unusual opportunity for a group of talented french applied mathematicians to apply daring and advanced new probabilistic approaches towards the solution of industrial problems in artificial vision.