Computer Vision

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Credits
6
Types
  • MIRI: Elective
  • MDS: Elective
  • MEI: Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
ESAII
Computer vision is a field within computer science focused on extracting meaningful information from images or sequences of images. Its range of applications is continuously expanding and includes facial recognition, early diagnosis of diseases, object and person detection and localization, gesture-based interaction with systems, robot navigation, and autonomous driving.

By the end of the course, students will be able to analyze, design, implement, and evaluate image analysis methods and techniques, meeting requirements related to response time, reliability, and cost/efficiency.

Teachers

Person in charge

  • Manel Frigola Bourlon ( )

Objectives

  1. Understand the limitations and capabilities of computer vision algorithms.
    Related competences: CTR6,

Contents

  1. Fundamentals of digital imaging
    Types of images according to the different areas. Intensity images. Color images. 3D image for tomography, MRI, ultrasound images, etc. Color Spaces.
  2. Digital image processing
    Gray level transformations. Linear operators. Convolution. Image enhancement and smoothing. Contour detection. Nonlinear operators. Morphological filters. Geometric transformations.
  3. Image segmentation.
    Image binarization: global, local. Image segmentation: watershed, k-means, grouping by color.
  4. Image descriptors
    Numerical shape descriptors, regions, color histograms, Fourier descriptors, singular points, Haar.
  5. Image recognition using Machine Learning
    Image recognition and classification using descriptor vectors. Perceptual hash of images.
  6. Image recognition using Deep Learning
    Main deep neural networks for object detection and localization in images.

Activities

Activity Evaluation act


Development of topic 1 of the subject



Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Development of topic 2 of the subject



Theory
0h
Problems
8h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Development of topic 3 of the subject



Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Development of topic 4 of the subject



Theory
0h
Problems
8h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Development of topic 5 of the subject



Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Development of topic 6 of the subject



Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Development of a real computer vision project


Objectives: 1
Theory
0h
Problems
16h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

Presentation of the computer vision project



Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
1.9h

Teaching methodology

The teaching methodology will generally be deductive in nature. An attempt will be made to avoid the expository/lecture method.
The approach will be based on:
- proposing a problem
- trying to solve it
- adding the necessary pieces of theory to be able to solve the problem adequately.

During the practices, cooperative learning will also be worked on, for solving the problem as a team.

Evaluation methodology

The subject will be evaluated continuously. Throughout the course, a series of exercises will be requested that will serve to evaluate the student. There will be no final exam.

The final grade for the subject (NF) will be obtained from the practices that are compulsorily done in class in person (LAB) and from the submissions of the practices that the student must work on at home (HW). Some exercises will be solved in groups and some individually. In group exercises the grade will be unique for all its components.
The final grade will be calculated as follows:

NF = Average(HW) * 0.5 Average(LAB) * 0.5

Where, HW and LAB represent the vector of grades for the work done at home and in the laboratory respectively.

Bibliography

Basic:

Previous capacities

Basic statistics, elementary programming, algebra.