Visual content analysis

Catalogue des cours de Télécom SudParis

Code

IMA 4509

Niveau

Graduate (M1)

Période

Spring (P4)

Domaine

Image

Langue d'enseignement

Anglais

Crédits ECTS

4

Heures programmées / Charge de travail

45 / 90

Responsable(s)

  • ROUGON Nicolas

Département

- Advanced Research and Techniques for Multidimensional Imaging Systems

Equipe pédagogique

  • FETITA Catalin
  • ROUGON Nicolas

Objectifs

-To master the core techniques for low-level image & video analysis as a preliminary step to interpretation and content-based access.
-To understand related technological challenges and gain insight into emerging application issues.
-To turn into practice computer vision applications (e.g. human motion analysis, object detection, scene activity monitoring…) by means of image & video analysis, exploiting the industry-standard Matlab platform capabilities.

Contenu

> Digital imaging products, vision (sub)systems and visual media-based services: current industrial issues and technological challenges of image & video processing and understanding

> Image & video analysis: paradigms and models
- Computational vision paradigms: hierarchical processing, low/mid/high-level vision, visual features, Gestalt principles
- Image & video models: functional, stochastic, statistical, algebraic

> Still image analysis
- Characterizing and exploiting global image properties : histogram techniques, frequency filtering
- Extracting image local geometry: edge and singularity detection
- Binary and grey-level mathematical morphology
- Inverse problems in image analysis: deterministic and stochastic regularization
-Deterministic image segmentation: variational methods and graph cuts
• Active contours and level set methods
• The Mumford-Shah model, deterministic & statistical region competition, multi-feature variational segmentation
- Scale-space and PDE image filtering
- Bayesian methods, Markov Random Fields
- Texture modeling and analysis

> Video analysis
- Motion measurement and optical flow estimation
- Spatio-temporal segmentation and object tracking

Prérequis

None

Mots-clés

Visual feature extraction; denoising, enhancement & restoration; segmentation & grouping; motion estimation & tracking; shape analysis.

Evaluation

The assessment pattern involves 3 components: continuous evaluation via homework on selected topics (”coursework”) (CW), lab assignments (L), and a two-student group written final exam (E). The final grade is a weighted average of individual component grades. The 2nd session consists of a study with an oral defense (O).
-1st session = Weighted Average (CW, L, E)) (S1)
-2nd session = O (S2)
-Final grade = Max (SE1, SE2)

Approches pédagogiques

 

Programme

Programme Ingénieur

Fiche mise à jour : 20/12/2016 14:58:20