Introduction

This course explores techniques to extract meaningful information from digital images (30 hours). Topics range from image formation and processing to feature description, matching, and classification. Students will gain hands-on experience with widely used tools such as Python, OpenCV, and Scikit-learn.

  • Luis Baumela, 15h
  • Roberto Valle, 15h

Unit 1: Introduction to the Discipline

  • Foundations and motivation behind image analysis.
  • Practical applications.
  • Overview of image capture technologies.

Unit 2: Image Formation

  • Color formation processes.
  • Camera modeling and projection.
  • Physical principles of image acquisition.

Unit 3: Digital Image Processing

  • Point-wise transformations (e.g., brightness, contrast).
  • Window-based transformations (e.g., filters, convolution operations).

Unit 4: Image Description

  • Keypoint detection (e.g., corners, edges).
  • Feature extraction and description techniques (e.g., SIFT, SURF).

Unit 5: Feature Matching

  • Geometric transformations between images.
  • Estimation of transformation parameters and image registration.

Unit 6: Practical Applications

  • Image classification using machine learning.
  • Use cases in computer vision and object recognition.