Artificial Vision & Pattern Recognition

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Credits
4.5
Types
Elective
Requirements
This subject has not requirements
Department
URV;CS
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
1.7
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
4.8

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


Master class

Theoretical and practical explanation of the main concepts of this course
Objectives: 2 3 5 1 4 6
Theory
30h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

Lab

Implementation of practical cases
Objectives: 2 3 1 4 6
Theory
0h
Problems
0h
Laboratory
15h
Guided learning
0h
Autonomous learning
28h

Teaching methodology

Introductory activities: Introduction to the course: motivation, objectives, contents, teaching methods, bibliography and evaluation.
IT-based practicals in computer rooms: Practical use of simulators related to course content and developing new functionalities.
Presentations / oral communications: Students perform oral presentation of their work going in depth into specific topics of the subject. Assessment by the teacher.
Lecture: Explanation of theoretical contents by the teacher.
Problem solving, exercises in the classroom: Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Final evaluation by the teacher.
Personal attention: Personal attention to each student by the teacher during the teacher's office hours.

Evaluation methodology

IT-based practicals in computer rooms:
Elaboration by the students of practical work related to the main topics of the course using the tools of computer vision explained in the practical classes. Elaboration of a report. 40%

Presentations / oral communications:
Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Oral presentation. Final evaluation by the teacher. 20%

Extended-answer tests:
Extended-answer tests. 20%

Short-answer objective tests:
Objective short-answer tests. 20%

Bibliography

Basic:

Addendum

Contents

There are no changes to the information published in the Academic Guide.

Teaching methodology

Due to COVID19, the course will start fully online. This methodology could last till the end of the first term. Only if the clinical and social situation stabilizes in a save condition and classes can turn back to face-to-face, the registered students will be asked to decide if they prefer to continue with online classes or move to in-room classes. No decision will be taken unilaterally by the professor, but as a result of an agreement between all the students. Face-to-face laboratories will start as virtual classes. Only if room classes are recovered because COVID19 situation allows it, the on-site laboratories will be reactivated in agreement with the students.

Evaluation methodology

There are no changes to the information published in the Academic Guide.

Contingency plan

There are no changes to the information published in the Academic Guide.