Prof. Kidiyo Kpalma visited the School of Information Science and Engineering

Professor Kidiyo Kpalma, from Institut National des Sciences Appliquées (INSA- Institute for Applied Sciences) in Rennes, visited the School of Information Science and Engineering. He gave a lecture on Image analysis and interpretation, and will give lectures in the next two weeks.

Kidiyo Kpalma joined the University of Rennes 1 in France from whom he obtained his M Sc. in Signal Processing in 1988. He then joined the National Institute for Applied Sciences of Rennes (INSA) for his PhD studies in Image Processing: he received this degree in 1992.

Since 1994, he is Associate Professor (Ma?tre de conferences) at INSA where he teaches Signal and Systems, Signal Processing and DSP in the department of Electronic and Computer Engineering. As a member of the Image and Remote Sensing group of the Institute of Electronics and Telecommunications of Rennes (IETR), his research interests are image analysis, pattern recognition, image fusion and remote sensing.

He received, his HDR (Habilitation à diriger des recherches), the higher Doctorate in France, in Signal Processing and Telecommunications from the University of Rennes 1 in 2009.

IETR brings together researchers working in the fields of electronics and telecommunications at the University of Rennes 1, the Rennes Institute for Applied Sciences (INSA) and SUPELEC. With more than 200 people, the institute is linked to French National Center for Scientific Research (CNRS) and places the emphasis on developing the skills needed to explore a wide range of emerging applications.

Image analysis and interpretation

Lectures are dedicated to "Image analysis and interpretation". These lectures will enable attendees to apprehend the essential aspects of digital image analysis and understanding. Image analysis consist of extracting useful and meaningful information so that to help the user understand the content of the image. This information may be extracted directly from the image itself or from its transform. Exercises will be developed in parallel, on one hand at the blackboard and on the other hand with dedicated software.


  1. Introduction to Digital Image
  2. Mathematical Morphology
  3. Filtering
  4. Enhancement
  5. Restoration
  6. Introduction to Analysis and Segmentation
  7. Features for edge based segmentation
  8. Edge-based segmentation
  9. Features for region based segmentation
  10. Region-based segmentation
  11. Classification

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