Artificial Vision & Pattern Recognition

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Credits
4.5
Types
Elective
Requirements
This subject has not requirements

Department
URV
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.

Teachers

Person in charge

  • Domenec Puig ( )

Weekly hours

Theory
1.5
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5

Competences

Technical Competences of each Specialization

Academic

  • CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA14 - Capability to understand the advanced techniques of Vision, Perception and Robotics, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
  • CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.

Transversal Competences

Appropiate attitude towards work

  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Objectives

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

Contents

  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.