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Introduction to Human and Computer Vision

Code: 44772
Credits: 6
2026/2027
Degree programme Type Course
Computer Vision OB 1

Contact lecturer

Name :
Maria Vanrell Martorell
Email :
maria.vanrell@uab.cat

Teaching staff

Javier Vazquez Corral
Javier Ruiz Hidalgo
Ramon Morros Rubio

Group languages

You can consult this information at the end of the document.

Prerequisites

Degree in Engineering, Maths, Physics or similar

Objectives

Module Coordinator: Dr. Ramon Morros


The aim of this module is introduce the students to computer vision including basics of human visual system and image perception, acquisition and processing. In terms of processing, the module deals with low-level pixel-based transforms, linear, nonlinear and morphological filtering, Fourier analysis, multi-scale representations, extraction of simple features and image descriptions. Furthermore, elementary grouping, segmentation and classification strategies will be discussed as well as quality and assessment methodologies for image processing algorithms. To put into practice the algorithms and techniques, the students will work on a concrete project along the course. The aim is to provide an applied knowledge of a broad variety of Computer Vision techniques applied to solve a real-world vision problem. The project goal is to detect specific objects in images using basic CV techniques such as linear and non-linear filtering segmentation, grouping, template matching, modelling, etc. The knowledge obtained can be used in a wide variety of applications, for instance, quality control, generic object detection, security applications, etc.

Learning outcomes

  • CA06 (Achieve the objectives of a project of vision carried out in a team.) Achieve the objectives of a project of vision carried out in a team.
  • KA01 (Identify low level modules such as pre-processing, extraction or grouping of characteristics necessary for solving a problem of vision.) Identify low level modules such as pre-processing, extraction or grouping of characteristics necessary for solving a problem of vision.
  • KA08 (Select the best algorithms for use with each of the components defined to solve a low level problem of vision.) Select the best algorithms for use with each of the components defined to solve a low level problem of vision.
  • SA01 (Apply and evaluate low level processing techniques to solve a specific problem.) Apply and evaluate low level processing techniques to solve a specific problem.
  • SA07 (Apply data analysis and performance evaluation techniques for different problems.) Apply data analysis and performance evaluation techniques for different problems.
  • SA08 (Select the best software tools to code techniques to solve low level problems of vision.) Select the best software tools to code techniques to solve low level problems of vision.
  • SA15 (Prepare a report that describes, justifies and illustrates the development of a project of vision.) Prepare a report that describes, justifies and illustrates the development of a project of vision.
  • SA17 (Prepare oral presentations that allow debate of the results of a project of vision.) Prepare oral presentations that allow debate of the results of a project of vision.

Contents

  1. Human visual system and perception
  2. Image formation and color representation
  3. Image processing assessment and pixel-based processing
  4. Morphological and nonlinear filtering
  5. Space-frequency representation, Fourier transform and linear filtering (I)
  6. Space-frequency representation, Fourier transform and linear filtering (II)
  7. Scale-space theory and multi-scales image processing
  8. Feature extraction
  9. Grouping, Segmentation and Classification


Learning activities and methodology

Title Hours ECTS Learning outcomes
Lecture Sessions 20 0.8 CA06, KA01, KA08, SA01, SA07, SA08, SA15, SA17
Homework 113 4.52 CA06, KA01, KA08, SA01, SA07, SA08, SA15, SA17
Supervised sessions 8 0.32 CA06, KA01, KA08, SA01, SA07, SA08, SA15, SA17

Supervised sessions:  (some of these sessions could be online synchronous)
• Lecture Sessions, where the lecturers will explain general contents about the topics. Some of them will be used to solve the problems.

Directed sessions:
• Project Sessions, where the problems and goals of the projects will be presented and discussed, students will interact with the project coordinator about problems and ideas on solving the project (approx. 1 hour/week).
• Presentation Session, where the students give an oral presentation about how they have solved the project and a demo of the results.
• Exam Session, where the students are evaluated individually. Knowledge achievements and problem-solving skills

Autonomous work:
• Student will autonomously study and work with the materials derived from the lectures.
• Student will work in groups to solve the problems of the projects with deliverables:
    • Code
    • Reports
    • Oral presentations

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Project 55% 6 0.24 CA06, KA01, KA08, SA01, SA07, SA08, SA15, SA17
Exam 40% 2.5 0.1 KA01, KA08, SA01, SA07, SA08
Attendance 5% 0.5 0.02 CA06, KA01, KA08, SA01, SA07, SA08, SA15, SA17

The final marks for this module will be computed with the following formula:

Final Mark = 0.4 x Exam + 0.55 x Project+ 0.05 x Attendance

where,

  • Exam: is the mark obtained in the Module Exam (must be >= 3).
  • Attendance: is the mark derived from the control of attendance at lectures (minimum 70%)
  • Project: is the mark provided by the project coordinator based on the weekly follow-up of the project and deliverables (must be >= 5). All accordingly with specific criteria such as:
  • Participation in discussion sessions and in team work (inter-member evaluations)
  • Delivery of mandatory and optional exercises.
  • Code development (style, comments, etc.)
  • Report (justification of the decisions in your project development)
  • Presentation (Talk and demonstrations on your project)

Only those students that fail (Final Mark < 5.0) can do a retake exam.

Bibliography

  1. Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 3rd Edition.
  2. David Marr, “Vision: A Computational Investigation into the Human Representation and Processing of Visual Information”, Freeman, 1982.
  3. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer-Verlag New York, Inc. New York, USA 2010.


Software

Tools for Python programming with special attention to computer vision and image processing libraries

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 1 English first semester afternoon