Master in Software and Systems

Image Processing and Analysis

Lecturer (Coordinator):
José Crespo
jcrespo@fi.upm.es
Lecturer:
Raúl Alonso
ralonso@fi.upm.es

Semester

First semester

Credits

4 ECTS

Outline

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

Syllabus

  1. Theoretical and mathematical foundations
    1. Introduction
    2. Foundations: linear vs. non-linear processing, image processing and analysis and the artificial vision problem
    3. Image data format
  2. Preprocessing: image operators and filters
    1. Introduction to image operators and filters
    2. Some aspects of linear filters
    3. Erosions, dilations, openings, closings. Other morphological operators and filters
  3. Image segmentation and analysis
    1. Introduction to image segmentation
    2. Edge-based, region-based and hybrid methods
    3. The watershed method and region-merging methods
    4. Applications

Recommended Reading

Prerequisites

Assessment Method

The weight of Assignment 5 (Presentation and Report) is 15 %. The weight of the written or oral exam is: 10 %.

Normal 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 in July:

(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.

Tuition language

English

Subject-Specific Competences

More information:

This table shows the code, description and proficiency level for each subject-specific competence

Code Competence Proficiency Level
SSC7 Evaluation and application of diverse mathematical and statistical theories, and available knowledge extraction and discovery processes, methods and techniques for large data volumes 3
SSC8 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 4

Learning Outcomes

More information:

This table shows the code, description and proficiency level for each subject learning outcome

Code Learning Outcome Associated competences Proficiency level
RA-APDI-8 Understand the theoretical foundations of image data processing and analysis SSC7,SSC8 3
RA-APDI-9 Be able to apply and comparatively evaluate image processing techniques considering their efficient implementation and be familiar with image data warehousing system problems SSC7,SSC8 4
RA-APDI-10 Be able to apply and comparatively evaluate image processing methods for segmenting regions of interest and obtaining characteristic parameters, considering their efficient implementation SSC7,SSC8 4

Learning Guide

Subject learning guide for Image Processing and Analysis