Image processing and its applications Person in charge: Wojciech PIECZYNSKI Objectives: Companies that develop and/or make use of image processing methods are extremely numerous and varied in terms of activities as in terms of size. To illustrate our statement, here is a nonexhaustive list of applications domains: medical and biological purposes, security and surveillance, multimedia, photo and video, robotic, biometrics and remote sensing. Moreover, if there exist lots of small and medium-sized businesses, including start-ups, (for example Timeat, Realviz, Theralys ...), many big companies can be easily identified thanks to their activities that require image technologies (like Philips, Canon, EADS, Thalès …). But it is often unknown that others great firms without any visible link with image processing, as automobile or optical industries (Renault, Essilor …), also work with images for some of their products or services. Have a look to a non exhaustive list here: http://www.int-evry.fr/citi/TAI/debouches.php. The program « Image Processing and its Applications » is intended for engineer students who wish for acquiring knowledge and skills in leading-edge image techniques. Those ones will be illustrated through three rising-up technologies: Biometrics, Remote Sensing and Virtual Reality.

Objectives: At the end of this program, the future engineers should be able - to grasp and model a large family of industrial problems staking digital images (coding, analysis, synthesis, classification, fusion); - to devise, implement and optimize appropriate solutions to these problems.

Organisation: This program is part of the Advanced Engineering Cycle which covers the 8th and 9th semesters of the Telecom INT curriculum. Its building bricks consist of 6 independent courses (each representing 45h lectures & labs and 90h homework) which define a complete graduate programme in multimedia engineering. The Image Processing and its Applications program is organized in the following Teaching Units: S8 : - IMA4508 : Image, video & 3D graphics compression - IMA4509: Visual content analysis S9 :

- IMA5511 : Pattern Recognition and Biometrics - IMA5512 : Remote Sensing - IMA5513 : Statistical image processing - IMA5514 : Multimedia interacting and Virtual reality

A final project (IMA5515), representing 225h homework, will stand during the whole 9 th semester and will be carried out by small group of one to three students.

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Content: Code: IMA4508

Image, video and 3D graphics compression

Period: S8 – P3

ECTS: 4

Organization:

Course work: 45h

Personal work: 45h

Lectures: 12 = 36h

Language: French Total: 90h Labs: 3 = 9h

Assessment: Two-student group project (P) (45h) linked to real industrial applications or to national/European research projects with oral defence (D). Final mark = Average (P, D) Objectives: After having taken this course, a student will: - Be aware of the new scientific challenges derived from the scalable coding and universal access paradigms. - Master the underlying fundamental principles and mathematical tools, as well as the major compression standards. - Implement these methods, algorithms and techniques in the framework of realistic industrial applications (e.g. digital TV, telesurveillance, robotics, 3D gaming...). Keywords: Image compression, mono / multi resolution coding techniques, transform-based approach, predictive methods, JPEG/MPEG standards, scalability, progressive transmission, technological convergence. Prerequisites: Basic programming knowledge in C/C++ Course outline: • • • • • • • • • • •

New challenges for multimedia compression: digital terrestrial TV, High-Definition TV, TV over ADSL, scalability and technological convergence Generic principles of image compression techniques Decorrelation techniques, predictive approaches, transform-based and hybrid methods Quantization techniques Binary coding: arithmetic coding, error resilient tools Multiresolution image coding: wavelet-based approaches Scalable compression techniques Fractal-based image coding The JPEG standards: from JPEG to MotionJPEG The MPEG standards: from MPEG-1 to MPEG-4 Compression for film distribution over the Internet

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• • • •

New challenges in multimedia compression Adaptive compression techniques Transcoding techniques Emerging standards: MPEG-4 AFX, MPEG-4 AVC (H-264), MPEG-4 SVC

Learning materials and literature: Documentation provided by lecturers. Selected references: - A.K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989. - Y.Q. Shi, H . Sun, Image and Video Compression for Multimedia Engineering, CRC, 2000. - M. Bosi, R. Golberg, Introduction to Digital Audio Coding and Standards, Kluwer, 2002. Person in charge: Dr. Titus ZAHARIA ([email protected]) Faculty: From INT - Dr. Marius PREDA - Prof. Françoise PRETEUX Guest lecturers - Dr. Gérard MOZELLE (Thomson)

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Code: IMA4509

Visual content analysis

Period: S8 – P4

ECTS: 4

Organization:

Course work: 45h

Personal work: 45h

Lectures: 12 = 36h

Language: French Total: 90h Labs: 3 = 9h

Assessment: The assessment pattern relies on supervised personal work, and consist of 3 short presentations (S1-S3), equivalent to 15h homework, and a 30h group project (2-4 student groups) resulting into a written report (E1). The following topics: - visual feature extraction - variational / morphological / stochastic / statistical image segmentation - dynamic segmentation and object tracking - deformation analysis and shape variability modelling will be used as building blocks for competence acquisition. The proposed subjects will be linked to industrial applications or to national/European research projects, and will focus on implementing, following a problem analysis step, the functionalities and technologies presented during the course. Final grade = Average (E1, Average (S1, S2, S3)) In 2007: 1st session = Average (E1, Average (S1, S2, S3)) (C1) 2nd session = 1 written exam (C2) Final grade = Max (C1, Average (C1, C2)) Objectives: -

To master the core techniques for low-level visual content (2D/3D still images and videos) analysis, as a preliminary structuring step towards interpretation and content-based access. To understand the related technological and economical challenges, and to gain insight into emerging application issues. To turn into practice computer vision applications by means of visual content analysis (human motion, object detection,…).

Keywords: Visual feature extraction, segmentation & grouping, motion estimation & tracking, shape analysis. Prerequisites: None Course outline: •

Visual content analysis: economical and industrial issues, technological challenges and new services in the Information and Communication Society

•

2D/3D modelling : - Low-level and high-level attributes - Geometric, deterministic, stochastic and fuzzy approaches

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• • •

Still image segmentation: - Global approaches: histogram techniques, frequency filtering - Differential approaches: edge and singularity detection - Mathematical morphology - Contour-based variational approaches : active contours and surfaces, level set methods - Region-based variational approaches: the Mumford-Shah model, region competition - Bayesian methods, Markov Random Fields Texture analysis and synthesis Video sequence analysis : motion estimation, dynamic segmentation and object tracking Hybrid approaches for multimedia data segmentation

Learning materials and literature: Documentation provided by lecturers. Selected references: - A. Bovik (Ed.). Handbook of Image & Video Processing. Academic Press, 2000. - L.G. Shapiro, J-C. Stockman. Computer Vision. Prentice Hall, 2001. - E.R. Davies. Machine Vision: Theory, Algorithms, Practicalities. Academic Press, 1997. - R. Jain, R. Kasturi, B.G. Schunck. Machine Vision. McGraw-Hill, 1995. Person in charge: Dr. Nicolas ROUGON ([email protected]) Faculty: From INT - Pr. Françoise PRETEUX - Dr. Catalin FETITA - Dr. Titus ZAHARIA Guest lecturers - Representatives of industry (DGA, Thalès, Philips)

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Code: IMA5511

Pattern Recognition and Biometrics

Period: S9 – P1

ECTS: 4

Organisation:

Class work: 45h Lectures: 15h

Home work: 45h Tutorial: 12h

Language: French Total workload: 90h Labs: 18h

The course is structured in two parts : the first, more theoretical, is focused on basic Pattern Recognition tools that are required to understand how identity verification is performed on the basis of a person’s biometric traits; the second is focused on the application of such tools to the field of identity verification. Assessment: Validation is based on 3 evaluated Lab sessions (Lab1, Lab2 and Lab3) and an oral exam (O). Final Mark = 1/3 [Average (Lab1, Lab2, Lab3) + 2*O] Objectives: - Master the tools for pattern recognition and data classification - Knowledge of the specific techniques of the different biometric modalities in terms of the general tool adaptation to each of them - Be able to implement a biometric system of identity verification Keywords: Biometrics, face recognition, on-line signature verification, iris recognition, speaker verification Prerequisites: Notions of Statistics and Probability Theory (Course “Introduction aux statistiques”, ST21) Course outline: First Part: Basics of Pattern Recognition Introduction Bayes Classifier The Linear Model The K Nearest Neighbor Rule Hidden Markov Models Principal Component Analysis, Discriminant Analysis Multilayer Perceptrons Kohonen Feature Maps Second Part: Application to Biometric Identity Verification Face Recognition Techniques On-line Signature Verification Techniques Iris Recognition Techniques Speaker Verification Techniques Learning materials and literature: - R.O. Duda, P. E. Hart, D.G. Stork, "Pattern Classification", Second Edition, John Wiley, 2001. - L. Rabiner, B.H. Juang, "Fundamentals of Speech Recognition", Prentice Hall Signal Processing VAP TAI

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Series, 1993. - S. Haykin, "Neural Networks", Second Edition, Prentice Hall International, 1999. - M. Volle, " Analyse des données ", 3ème édition, Economica. Person in charge: Dr. Sonia Salicetti ([email protected]) Instructors: - Prof. Bernadette Dorizzi - Dr. Dijana Petrovska - Dr. Sonia Salicetti

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Code : IMA5512

Remote Sensing

Period: S9 – P2

ECTS: 4

Organisation:

Class work: 45h Lectures: 30h

Home work: 45h Tutorial: 0h

Language: French Total workload: 90h Lab: 15h

Some lectures will be provided by an industry engineer. The labs sessions are carried out by student pairs during 3 hours and include the taking in hand of software like TeraVue and the ORFEO Toolbox from CNES. Assessment: Final grades in this class will be based on lab work reports. Final mark = Average (Lab works). Objectives: - Be able to implement and optimize the segmentation and classification technologies in the context of remote sensing - Master the characteristics of the different types of imaging sensors used in remote sensing applications - Knowledge of stakes and applications of remote sensing Keywords: Satellite and airborne images, SAR, hyper-spectral images, data fusion Prerequisites: Bayesian classification, notions of statistics (UV ST21), low-level image analysis Course outline: - Basis of satellite imaging: optical, radar, synthetic aperture radar sensors … o Applications: land-cover classification (SPOT), oil slick detection (SAR), extraction of vegetation indicates - Multi- and hyper-spectral imaging: data reduction (ICA, wavelets), spectral signature, texture characterisation and segmentation o Applications: galaxy detection, agricultural and environmental mapping (CASI images) - Change detection: detection theory, vector-machine support classification o Applications: glacier development following, devastated zone mapping after a disaster - Stereovision : epipolar geometry o Applications in urban area: 3D model construction of a urban scene - Introduction to geographical information systems (GIS) Learning materials and literature: - J-M. Monget, Initiation à la télédétection et son traitement, Editions de la Boyère – Valbonne, 2003. - P. J. Gibson, Introductory remote sensing: principles and concepts. Routledge, London, 2000 - P. J. Gibson and C.H. Power , Introductory remote sensing: digital image processing and applications, Routledge, London, 2000 VAP TAI

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Person in charge: Dr Annabelle Joannic-Chardin ([email protected])

Instructors: -

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Code : IMA5513

Statistical image processing

Period: S9 – P3

ECTS: 4

Organisation:

Class work: 45h Lectures: 24h

Home work: 45h Tutorial: 0h

Language: French Total workload: 90h Lab: 21h

The labs sessions are carried out by student pairs during 3 hours. Assessment: Final grades in this class will be based on lab work reports. Final mark = Average (Lab works). Objectives: - Master the main probabilistic modelling and newest statistical methods used in the treatment of data masses - Master their applications in data classification and image segmentation Keywords: Hidden Markov models, pairwise and triplet Markov models, Bayesian segmentation, theory of evidence, sensor fusion, multiresolution, unsupervised segmentation Prerequisites: Notions of Statistics and Probability Theory (Course “Introduction aux statistiques”, ST21) Course outline: - Hidden Markov models (Gibbs random fields, Markov random chains, Markov random trees) - Unsupervised statistical segmentation: o Bayesian segmentations (MAP and MPM) o learning methods (EM, SEM, ICE) - Unsupervised statistical fuzzy segmentation - Estimation of generalised mixing - Theory of evidence o Evidential hidden Markov models o Dempster-Shafer fusion and multi-sensor segmentation - Pairwise and triplet Markov models - Markov models and multiresolution (renormalisation group, multigrid algorithm, multiresolution analysis, models on hierarchical graphs) - Markov random trees and wavelets Learning materials and literature: - W. Pieczynski, "Méthodes statistiques en imagerie", Polycopié, 2005. - B. Chalmond, “Eléments de modélisation pour l’analyse d’images”, Springer, 2000 - X. Guyon, “Random fields on a network”, Springer-Verlag, 1995 Person in charge: Pr Wojciech Pieczynski ([email protected])

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Instructors: - Dr A. Joannic-Chardin - Pr W. Pieczynski

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Code: IMA5514

Multimedia interacting and Virtual reality

Period: S9 – P4

ECTS: 4

Organisation:

Class work: 45 h Lectures: 30 h

Home work: 45 h

Language: French Total workload: 90 h

Tutorials & Lab.: 15 h

Some lectures could be provided in english, while some others will be immediately followed by lab work on computers. All tutorials sessions include lab work by student pairs. Assessment: Final grades in this class will be based on lab work reports. Final mark = Average (Lab works). Objectives: This teaching unit will provide to future engineer the necessary skills for being able to: - Represent multimedia signals: image, sound and video - Analyse speech - Analyse fixed and moving images - Synthesize speech and images - Animate virtual objects - Interact in a virtual share environment (in network) - Understand augmented reality techniques Keywords: Coding, speech recognition, image synthesis, virtual reality, content based indexation Prerequisites: Signal processing (frequency representation…) and C/C++ programming basis. Course outlines: - Sound and speech coding. - Image coding: reminders and comparison between coding norms of images and videos - Speech recognition. - Image indexation. - Motion capture. - Speech synthesis from text. - Rendering from multiple views. - Image synthesis: 3D modelling, Gouraud and Phong shading, ray tracing. - 3D animation: key points, direct and inverse cinematic. - Languages and norms for Virtual reality: VRML, WEB 3D and Java3D. - Virtual collaborative environments and their applications. - Augmented reality. Learning materials and literature: Lecture notes given by instructors. Reference readings: - Jean-Paul Guillois, Techniques de compression des images, Hermès (1996). VAP TAI

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Jean-Paul Haton et al., Reconnaissance automatique de la parole : Du signal à son interprétation, Dunod (2006). Aaron E. Walsh & Mikael Bourges-Sévenier, CoreWeb3D, Prentice Hall (2000) ; http://www.coreweb3d.com. Philippe Fuchs & Guillaume Moreau, Le traité de la réalité virtuelle, 3ème édition en 4 volumes, Les Presses de l'École des Mines de Paris (2006) ; http://www.caor.ensmp.fr/interlivre.

Person in charge: Dr Patrick Horain ([email protected]).

Instructors: - André Bideau, - Dr Jérôme Boudy, - Dr Patrick Horain, - Dr William Navarro (Nortel Networks), - Pr Yannick Rémion (URCA)…

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Code: IMA5515

TAI Major’s project

Period: S9 Organisation:

ECTS: 8 Face to face: 27h

Personal work: 198h

Language: French Total workload: 225h

TAI major’s project is done during the whole semester 9. Each student must make a project with two or three other students. Planning time slots are dedicated to the project. Meeting with project manager take place about every other week. Three types of projects are proposed to students: Experimentation projects, study projects for companies or administrations, research projects. Assessment: The validation of this project is based on the writing of a report (R) and an oral presentation (P). Final score = Average (R, P) Samples of subjects: Person in charge: Pr Wojciech Pieczynski ([email protected]) Faculty: All lecturers of TAI major

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