HAH802E1M Image Sensors and Processing (4 ECTS)
Presentation
Description:
The objective of this module is to enable students to acquire the basic knowledge necessary in most disciplines associated with engineering sciences concerning digital images and their use. To take this course, students must have knowledge of linear algebra as well as some notions of continuous and sampled signal processing. As the practical work is carried out in Matlab, some programming skills in this language are desirable. At the end of this course, students will have mastered the basics of image processing. They will understand the principles underlying most image analysis and modification software.
Hourly volumes:
Lectures: 16.5 hours
Tutorials: 12 hours
Practical work: 12 hours
Fieldwork: 0 hours
Required prerequisites:
L3 science or health.
Recommended prerequisites:
Nothing to report
More information
Knowledge assessment:
Final written exam with second session (coefficient 0.6) + Practical work exam based on report (coefficient 0.4).
Syllabus:
Lecture:
1 – Introduction to the course, luminance images, color images + history of photography and images. (O. Strauss)
2 – Image sensors in the modern world. (P. Falgayrettes)
3 – Non-photonics image acquisition technology: ultrasound images, microscopic images, radar images, mechanical images, thermal images, etc. (P. Falgayrettes)
4 – Representation of color in images, perceptual colors. Reconstructed images (tomography). (O. Strauss)
5 – Histograms and image binarization. (O. Strauss)
6 – Morphology, basic erosion/dilatation operators. Extension to grayscale morphology. (O. Strauss)
7 – The image as a sampled and quantified signal. (P. Falgayrettes)
8 – Grayscale image filtering, principle and convolution masks. (P. Falgayrettes)
9 – Image denoising, high-pass, low-pass, and band-pass filters. (P. Falgayrettes)
10 – Interpolation kernels, convolution kernels. (O. Strauss)
11 – Deconvolution, image derivation. (O. Strauss)
12 – Contour extraction, extraction of specific points (Harris). (O. Strauss)
13 – Contour chaining, hole elimination, etc. (P. Falgayrettes)
14&15 – Fourier transform on images. (P. Falgayrettes) (3 hours)
16 – Physical interpretation of images (return to model). (B. Wattrisse)
17 – Geometric transformations on images. (O. Strauss)
18 – Correlations and correlation-related methods. (O. Strauss)
19 – Parameterization of objects, geometric invariants, inertia matrices, etc. (P. Falgayrettes)
20 – Principles of compression. (P. Falgayrettes)
21 – Segmentation of images into regions. (O. Strauss)
Practical work
1 – Fundamentals of image processing, binarization, morphology, sampling, quantization.
2 – Representation of color: physical space, perceptual space.
3 – Fourier transform on images.
4 – Image filtering, discrete convolution.
5 – Sampling, interpolation, geometric transformations.
6 – Image derivation, contour extraction, Harris points.
Contacts
Responsible: O. Strauss – LIRMM, UM
Administrative contact(s): Claudie Fabry