Image Processing

Today, image processing is ubiquitous in information technology: medicine, biology, agriculture, entertainment, culture, measurement, mechanical engineering …

Image processing involves applying mathematical transformations to images in order to modify their appearance or extract information from them. More generally, image processing aims to manipulate the underlying information contained in an image. While it was long performed using electronic circuits, image processing is now carried out almost exclusively digitally—that is, via algorithms typically programmed in an imperative language (C, C++, Java, Python, etc.).

This course aims to provide a solid foundation in image processing. Among other topics, 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 lectures are supplemented by supporting videos.

The course consists primarily of 11 lectures covering the fundamentals of the main areas of image processing and 3 lab sessions, for which students must choose a topic from among 6 options. Students may choose to complete the lab assignments using images they provide that are relevant to their field of study.

The objective of this module is to provide students with a foundation in image processing that will enable them to 1) understand the operations performed by image processing software, 2) read articles on image processing, 3) develop their own applications, and 4) continue their education in this field on their own.

Fundamentals of Signal Processing.

Some knowledge of programming in a language.

Written assignment: 60%, lab work (based on lab report): 40%

• Image formation, digital image, luminance image, color image, …
• Image Acquisition Technology.
• Mathematical Morphology
• Convolution kernels, interpolation kernels: discrete representations of transformations defined in the continuous domain.
• Derivation of images.
• Extraction of contours and specific points.
• Fourier Transform of Images.
• Image filtering, digital convolution, and noise reduction.
• Correlation and distances between images.
• The Principle of Image Compression.
• Geometric transformations.

CM: 4:30 p.m.

 Practical Training: 9:00 a.m.