The general objective of the course is to introduce students to the fundamental aspects of Artificial Vision and its challenges, as well as to provide methods and solutions in this field, with an emphasis on practical aspects of the subject. After completing the course, the student should:
- Understand the stages of an Artificial Vision System (AVS).
- Grasp the challenges and study the fundamental techniques of different stages (processing, segmentation, feature extraction, and pattern recognition).
- Be capable of applying the studied techniques to solve real problems of practical interest in Artificial Vision.
The instructor will present the fundamental elements involved in the field through lectures and discussions on key topics. During the course, exercises will be proposed, and significant ones, as well as those that students find challenging, will be solved. Students will choose a topic in Artificial Vision for which they will prepare a project containing a problem characterization, a description of the proposed solution, and an analysis of the obtained results.
Introduction
- The Human Visual Perception System.
- Light, Spectrum, and Visual Perception.
- What is Artificial Vision?
- Stages of an Artificial Vision System.
- Challenges in Artificial Vision.
- Applications.
Image Acquisition
- Digital Images.
- Characteristics of Digital Images.
- Data Repositories.
Image Processing
- Operators in the Spatial Domain.
- Operators in the Frequency Domain.
Image Segmentation
- Segmentation Using Thresholding.
- Segmentation Based on Edge Detection.
- Segmentation Based on Regions.
Morphological Transformations
- Morphological Operations.
- Morphology in Grayscale Images.
Feature Extraction and Selection
- Introduction.
- Feature Extraction.
- Feature Selection.
Pattern Recognition
- Introduction.
- Supervised Classification.
- Unsupervised Classification.
- Performance Evaluation.