Study programme 2018-2019 | Français | ||
Image Analysis and Pattern Recognition | |||
Programme component of Master's Degree in Computer Science à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
US-M1-SCINFO-072-M | Optional UE | GOSSELIN Bernard | F105 - Théorie des circuits et Traitement du signal |
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Anglais | 24 | 24 | 0 | 0 | 0 | 4 | 4.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-TCTS-005 | Image Analysis and Pattern Recognition | 24 | 24 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component |
---|
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
develop an applied pattern recognition system, together with a critical analysis of the problem;
apply image analysis and segmentation techniques
apply data processing techniques (feature extraction, feature selection);
apply classification techniques and train classifiers (Gaussian models, Clustering, Artificial Neural Networks, Dynamic Time Warping, Hidden Markov Models, Combining Classifiers);
estimate performances of classifiers.
Content of UE
Image Processing: Image acquisition; lowlevel processing, filtering, transforms; image segmentation and registration;
Pattern Recognition: SPR scheme, feature extraction, classifiers, combining classifiers; neural networks:feed-forward neural networks, training MLP, Deep Neural Nets; support vector machines; dynamic systems: dynamic time warping, hidden Markov models
Prior Experience
fundamentals of signal processing; probability and statistics
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
Not applicable
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Not applicable
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
---|---|
I-TCTS-005 |
|
Mode of delivery
AA | Mode of delivery |
---|---|
I-TCTS-005 |
|
Required Reading
AA | Required Reading |
---|---|
I-TCTS-005 | Copie de présentation - Partie 1 - IAPR - Part I: Image Processing - Matei Mancas Copie de présentation - Partie 2 - IAPR - Part II: Pattern Recognition - Bernard Gosselin |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
I-TCTS-005 | Not applicable |
Recommended Reading
AA | Recommended Reading |
---|---|
I-TCTS-005 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
I-TCTS-005 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
I-TCTS-005 | Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras, "Introduction to pattern recognition - A MATLAB approach", 9780123744869 T. Dutoit & F. Marques, “Applied Signal Processing”, Springer, 2009 R.O. Duda & P.E. Hart, "Pattern Classification and Scene Analysis", John Wiley & Sons, 1973 (2000). K. Fukunaga, "Introduction to Statistical Pattern Recognition", Academic Press, San Diego, 1990 |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
---|---|
I-TCTS-005 | Unauthorized |