Artificial Vision & Pattern Recognition

• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography

Teachers

Person in charge

• Domenec Savi Puig Valls ( )

Weekly hours

Competences

Generic Technical Competences

Generic

• CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

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: CG1, CEP5, CEP6, CT5, 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.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• Computer vision : a modern approach - Forsyth, David A; Ponce, Jean, Pearson Education, cop. 2012. ISBN: 9780273764144
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003948569706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Handbook of pattern recognition and computer vision - Chen, C.H. (ed.), WSPC, 2020. ISBN: 9789811211065
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003766299706711&context=L&vid=34CSUC_UPC:VU1&lang=ca