Visión Artificial Avanzada

Créditos
4.5
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos , pero tiene capacidades previas
Departamento
CS;URV
Web
http://moodle.urv.cat/
This course aims at studying the fundamental techniques for image processing and advanced issues on machine vision related to the problems of automatic analysis and recognition of complex images. Practical applications will be developed on well-known machine vision software.

Horas semanales

Teoría
1.5
Problemas
0
Laboratorio
1
Aprendizaje dirigido
0
Aprendizaje autónomo
5

Objetivos

  1. To learn and practise the main algorithms and methods for image feature extaction.
    Competencias relacionadas: CEA6, CEA14,
  2. To learn and understand the main concepts of image processing.
    Competencias relacionadas: CEA6,
  3. To learn and practise the principal color and texture analysis methods.
    Competencias relacionadas: CEA14, CEP5, CB7,
  4. To learn and practise the main image segmetation and classification techniques.
    Competencias relacionadas: CEA14, CEP1, CEP5, CB7,
  5. To know some basics about stereoscopic vision and 3D models.
    Competencias relacionadas: CEA14, CEP1, CEP5,
  6. To be able to analyze a real computer vision problem, and propose effective solutions.
    Competencias relacionadas: CEP1, CEP5, CEP6, CT5, CB7,

Contenidos

  1. Chapter 1. Image Processing.
    Filtering and smoothing operations. Morphological techniques.
  2. Chapter 2. Feature Extraction.
    Lines and corners detection. Identification of basic geometrical structures.
  3. Chapter 3. Color and texture analysis.
    Color models, kinds of texture, texture feature extraction, geometrical methods.
  4. Chapter 4. Image Segmentation and Image Classification.
    Unsupervised segmentation based on regions and edges. Supervised classification, theoretical decision methods, statistical methods, neural networks.
  5. Chapter 5. Stereoscopic Vision.
    Camera calibration and camera systems, epipolar geometry, image rectification, search for correspondences, triangulation.
  6. Chapter 6. Perception and 3D Modeling.
    Range images generation, extraction of geometric elements, automatic scene generation, scene recognition, geometrical hashing.