Image Processing and Analysis

Lecturer (Coordinator):
José Crespo
Raúl Alonso


Second semester




This subject explores major image processing and analysis techniques. These days, image information availability is growing and adequate techniques and methods are needed to process and analyse the relevant information for this data type.

This course will stress morphological processing and analysis, which is particularly useful for image processing and analysis systems because it can satisfactorily account for image structure patterns within a sound and elegant formal framework based, above all, on set and lattice theory.

This subject will address both the filtering and the region of interest analysis and segmentation phases, discussing their distinctive features.

This subject will deal with algorithmic and implementation-related issues of some operators and techniques, examining efficient implementations. The use of queuing algorithms will be examined. Aspects of data structures, formats and storage will be commented.

Learning Goals

  • Understand the theoretical foundations of image data processing and analysis
  • Be aware of filtering techniques, including the qualitative differences between the different filter and operator classes
  • Study segmentation methods for separating regions of interest
  • Know how to apply and adapt techniques and methods in practical domains, relating what they have learned to research topics
  • Be aware of efficient implementations of major operators and techniques


  1. Introduction
  2. Filtering
    1. Introduction
    2. Morphological filtering
    3. Other techniques
  3. Segmentation and extraction of features and regions of interest
    1. Introduction to image segmentation and feature extraction
    2. Morphological approaches
    3. Morphological approaches
  4. Image classification
    1. Introduction
    2. Indexation of images
    3. Image features for clustering and learning
    4. Image search applications

Recommended Reading

  • Pierre Soille: "Morphological Image Analysis: Principles and Applications". Heidelberg: Springer, 2003
  • Jake VanderPlas: "Python Data Science Handbook". O'Reilly, 2016
  • Rafael C. González, Richard E. Woods: "Digital image processing". Prentice Hall, 2002
  • Francois Chollet: "Deep Learning with Python". Manning Publications, 2017


  • Programming knowledge.
  • Program development in a general purpose programming language as C or C++.

Assessment Method

The weight of assignments (Presentation and Report) is 85%. The weight of the written or oral exam is 15%.

Regular evaluation in January:

(1) Assignments, and (2) written or oral exam. It is necessary to pass both parts to successfully pass the course.

Extraordinary evaluation period:

(1) Assignments, and (2) written or oral exam as in the normal evaluation period. It is necessary to pass both parts to successfully pass the course.

Lecture Theatre


Tuition language


Subject-Specific Competences

Code, description and proficiency level for each subject-specific competence
Code Competence Proficiency Level
CEM7 Evaluation and application of diverse mathematical and statistical theories, and available knowledge extraction and discovery processes, methods and techniques for large data volumes A
CEM8 Application of the theoretical and mathematical foundations of heterogeneous functions and data processing and analysis and evaluation and design of related methods for application in practical domains S

Learning Outcomes

Code, description and proficiency level for each subject learning outcome
Code Learning Outcome Associated competences Proficiency level
RA-APDI-65 Understand the theoretical foundations of image data processing and analysis CEM7, CEM8 A
RA-APDI-66 Be able to apply and comparatively evaluate image processing techniques considering their efficient implementation and be familiar with image data warehousing system problems CEM7, CEM8 S
RA-APDI-67 Be able to apply and comparatively evaluate image processing methods for segmenting regions of interest and obtaining characteristic parameters, considering their efficient implementation CEM7, CEM8 S

Learning Guide

Learning Guide: Image Processing and Analysis