Image Processing and Artificial Vision

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Home » Studies » Bachelor's degrees » Bachelor Degree in Data Science and Engineering » Curriculum » Syllabus » Image Processing and Artificial Vision
Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
TSC
This course provides an overview of the essential techniques of processing, analysis and interpretation of images. The course is structured according to the complexity of the information extracted from the images and the level of interpretation of the scene. The first two blocks present useful models for the initial extraction of information from a single image, in particular the models of vector space, mathematical morphology and level sets. The process of interpreting the images generally involves a step of grouping the previously extracted information. The techniques related to this medium/high vision process combine segmentation and detection algorithms that are the content of the third and fourth block of the subject. Finally, the last block presents the necessary tools to analyze information from video sequences.

Teachers

Person in charge

  • Javier Ruiz Hidalgo ( )

Others

  • Philippe Salembier Clairon ( )

Weekly hours

Theory
3
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
6

Competences

Technical Competences

Technical competencies

  • CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.

Transversal Competences

Transversals

  • CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT7 [Avaluable] - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

Basic

  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Generic Technical Competences

Generic

  • CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
  • CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.
  • CG4 - Identify opportunities for innovative data-driven applications in evolving technological environments.
  • CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.

Objectives

  1. Acquire basic knowledge of frequency representation and advanced image filters.
    Related competences: CE5, CT7, CG1, CG5, CB5,
  2. Understand the use of tools for geometric processing.
    Related competences: CE5, CT7, CG2, CB5,
  3. Understand how to use object segmentation and detection techniques.
    Related competences: CE5, CT6, CT7, CG1, CG2, CG4, CB5,
  4. Acquire the basic knowledge of motion estimation and tracking.
    Related competences: CE5, CT6, CT7, CG2, CG5, CB5,

Contents

  1. Filtering and Frequency Analysis
    Frequency representation: FT, DFT
    Advanced filters: linear, non-local, bilateral
    Multiscale image analysis I: Downsampling / Upsampling, Interpolation, pyramid, wavelets & CNNs
  2. Geometrical Processing
    Mathematical morphology and lattice
    Filters by reconstruction
    Variational model and level sets
  3. Region-based model
    Transition-based segmentation: Edge detection, Active contour
    Homogeneity-based segmentation: Classification, Region growing & Watershed
  4. Object-based model
    Object recognition: Local features, Bag of words Region proposals, Regression
  5. Video Analysis
    Motion estimation, Optical flow
    Tracking

Activities

Activity Evaluation act


Unit 1

Theory, exercise and laboratory classes corresponding to Unit 1
  • Theory: Theory classes corresponding to Unit 1
  • Laboratory: Laboratory classes corresponding to Unit 1
  • Guided learning: Driven learning corresponding to Unit 1
  • Autonomous learning: Autonomous learning corresponding to Unit 1
Objectives: 1
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
17.7h

Unit 2

Theory, exercise and laboratory classes corresponding to Unit 2
  • Theory: Theory classes corresponding to Unit 2
  • Laboratory: Laboratory classes corresponding to Unit 2
  • Guided learning: Driven learning corresponding to Unit 2
  • Autonomous learning: Autonomous learning corresponding to Unit 2
Objectives: 2
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
17.7h

Unit 3

Theory, exercise and laboratory classes corresponding to Unit 3
  • Theory: Theory classes corresponding to Unit 3
  • Laboratory: Laboratory classes corresponding to Unit 3
  • Guided learning: Driven learning corresponding to Unit 3
  • Autonomous learning: Autonomous learning corresponding to Unit 3
Objectives: 3
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
17.7h

Unit 4

Theory, exercise and laboratory classes corresponding to Unit 4
  • Theory: Theory classes corresponding to Unit 4
  • Laboratory: Laboratory classes corresponding to Unit 4
  • Guided learning: Driven learning corresponding to Unit 4
  • Autonomous learning: Autonomous learning corresponding to Unit 4
Objectives: 3
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
17.7h

Unit 5

Theory, exercise and laboratory classes corresponding to Unit 5
  • Theory: Theory classes corresponding to Unit 5
  • Laboratory: Laboratory classes corresponding to Unit 5
  • Guided learning: Driven learning corresponding to Unit 5
  • Autonomous learning: Autonomous learning corresponding to Unit 5
Objectives: 4
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
17.7h

Teaching methodology

The subject is based on classroom theory classes and laboratory. The theory classes follow the program defined in this teaching guide. Within the lectures, the dialogue between professors and students is promoted by proposing exercises and activities to be carried out jointly based on particular aspects of the topic being dealt with. The laboratory classes exemplify the contents developed in the theory classes.

Evaluation methodology

Final mark is obtained from:

- Parcial exam: P (20%)
- Final exam: F (50%)
- Laboratory: L (30%)

Grade = max (0.5F+0.2P+0.3L ; 0.7F+0.3L)

With the re-evaluation (R) , the final mark is:

Grade = 0.7R+0.3L

Bibliography

Basic:

Previous capacities

The knowledge acquired in the subjects of the degree in previous courses.