HAH802E1M Image Sensors and Processing (4 ECTS)
Overview
Description:
The objective of this module is to enable students to acquire the basic knowledge required in most engineering science disciplines related to digital images and their applications. To take this course, students must have a background in linear algebra as well as some understanding of continuous and sampled signal processing. Since the lab exercises are conducted in MATLAB, some programming skills in this language are desirable. Upon completion of this course, students will have mastered the fundamentals of image processing. They will understand the principles underlying most image analysis and editing software.
Hourly volumes:
Lectures: 16.5 hours
Tutorials: 12 hours
Lab sessions: 12 hours
Fieldwork: 0 hours
Required prerequisites:
Bachelor's degree in science or health sciences.
Recommended prerequisites:
No changes
More information
Assessment:
Final written exam with a second sitting (weighting: 0.6) + Practical exam based on a lab report (weighting: 0.4).
Course Syllabus:
Lecture:
1 – Course introduction, luminance images, color images, and the history of photography and images. (O. Strauss)
2 – Image Sensors in the Modern World. (P. Falgayrettes)
3 – Non-photonics imaging technology: ultrasound imaging, microscopic imaging, radar imaging, mechanical imaging, thermal imaging, etc. (P. Falgayrettes)
4 – Color representation in images, perceptual colors. Reconstructed images (tomography). (O. Strauss)
5 – Histograms and image binarization. (O. Strauss)
6 – Morphology, basic erosion/dilation operators. Extension to grayscale morphology. (O. Strauss)
7 – The image as a sampled and quantized signal. (P. Falgayrettes)
8 – Filtering grayscale images: principles 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 differentiation. (O. Strauss)
12 – Edge detection, detection of specific points (Harris). (O. Strauss)
13 – Contour chaining, hole removal … (P. Falgayrettes)
14&15 – Fourier transforms of images. (P. Falgayrettes) (3 hours)
16 – Physical interpretation of images (back to the model). (B. Wattrisse)
17 – Geometric transformations of images. (O. Strauss)
18 – Correlations and methods related to correlation. (O. Strauss)
19 – Object parameterization, geometric invariants, inertia matrices, … (P. Falgayrettes)
20 – Principles of Compression. (P. Falgayrettes)
21 – Segmenting images into regions. (O. Strauss)
Practical exercises
1 – Fundamentals of image processing, binarization, morphological operations, sampling, and quantization.
2 – Color representation: physical space, perceptual space.
3 – Fourier transform of images.
4 – Image filtering, discrete convolution.
5 – Sampling, interpolation, and geometric transformations.
6 – Image differentiation, edge detection, Harris points.
Contacts
Principal Investigator: O. Strauss – LIRMM, UM
Administrative contact(s): Claudie Fabry