Image Processing

Nowadays, image processing is omnipresent in information technologies: medicine, biology, agriculture, entertainment, culture, measurement, mechanics...

Image processing consists of applying mathematical transformations to images in order to modify their appearance or to extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. If it has long been achieved through electronic circuits, image processing is nowadays almost exclusively done digitally, ie via algorithms programmed generally with an imperative language (C, C++, Java, Python, ...).

This course aims to provide a solid foundation in image processing. It covers image formation and acquisition, color transformations, morphological operations, geometric transformations, compression, frequency transformations, recognition and matching techniques, ... and an introduction to deep learning methods. The courses are complemented by support videos.

The teaching unit is mainly composed of 11 didactic courses dealing with the basics in the main fields of image processing and 3 practical work sessions whose subjects are to be chosen among 6 proposals. The students can choose to carry out the work on images that they bring corresponding to their field of training.

The objective of this module is to give the student who follows it the basics in image processing allowing him 1/ to understand the operations performed by image processing software, 2/ to read articles on image processing, 3/ to develop his own applications and 4/ to continue by himself his training in this field.

Basics of signal processing.

Some programming knowledge in a language.

Written 60%, Practical work (on report) 40%.

• Image formation, digital image, luminance image, color image, ...
• Image acquisition technology.
• Mathematical morphology
• Convolution kernels, interpolation kernels: discrete representation of transformation defined in the continuous domain.
• Image derivation.
• Extraction of contours and special points.
• Fourier transform on images.
• Image filtering, digital convolution and noise reduction.
• Correlation and distances between images.
• Principle of image compression.
• Geometric transformations.

CM: 4:30 pm

 PT: 9:00 am