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 disciplines related to engineering sciences concerning digital images and their use. To take this course, students must have a knowledge of 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:
A bachelor's degree in science or health sciences.
Recommended prerequisites:
All is well
More information
Knowledge Assessment:
Final written exam with a second sitting (weighting 0.6) + Practical exam based on a lab report (weighting 0.4).
Course Outline:
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 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/dilation operators. Extension to grayscale morphology. (O. Strauss)
7 – The Image as a Sampled and Quantized Signal. (P. Falgayrettes)
8 – Gray-scale image filtering: principles and convolution masks. (P. Falgayrettes)
9 – Image noise reduction, 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 Transform of Images. (P. Falgayrettes) (3:00)
16 – Physical Interpretation of Images (Back to the Model). (B. Wattrisse)
17 – Geometric Transformations on 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, morphology, sampling, quantization.
2 – Color representation: physical space, perceptual space.
3 – Fourier Transform on Images.
4 – Image filtering, discrete convolution.
5 – Sampling, interpolation, and geometric transformations.
6 – Image differentiation, contour extraction, Harris points.
Contacts
Principal Investigator: O. Strauss – LIRMM, UM
Administrative Contact(s): Claudie Fabry