- Course
- 2020-2021
- Semester
- 1
- ECTS
- 6
- Type
- Compulsory
- University
- UDC and USC
Subject objectives
This subject covers the fundamental topics of image processing and analysis and is presented as the first part of another subject that introduces more advanced topics. In addition to the study and application of fundamental techniques, practical applications of these techniques will be studied to solve real problems. This course provides the necessary tools to apply the algorithms used in practical cases, as well as the bases to develop new algorithms and continue studying more advanced methods.
Learning outcomes:
* Understand the basic concepts and techniques of digital image processing
* Understand the basic concepts and techniques of digital image analysis
* Ability to apply different basic techniques to computer vision problems
* Knowing how to assess the adequacy of methodologies applied to specific problems
Contents
Part 1 (UDC)
* Perception and color
* Preprocessed: normalization and enhancement
* Image restoration
* Edge detection
* Image transformations
* Morphological operators
* Template matching
Part 2 (USC)
* Extraction of global image properties (keypoints, blobs, corners, MSERs)
* Extraction of Invariant Properties at Scale (SIFT)
* Segmentation through thresholding
* Segmentation by fitting to a model (Hough transform)
* Segmentation through division and growth of regions
* Other segmentation techniques
Basic and complementary bibliography
Basic bibliography:
Gonzalez y Woods. Digital image processing. ISBN: 0-20-118075-8.
Complementary bibliography:
D.A. Forsyth y J. Ponce. Computer Vision. ISBN 0-13-085198-1.
Steger & Wiedemann. Machine Vision Algorithms and Applicacions. ISBN 978-3-527-4073.
Competencies
CT1. Practice the profession with a clear awareness of its human, economic, legal and ethical dimension and with a clear commitment to quality and continuous improvement.
CG2. Ability to analyze the needs of a company in the field of computer vision and determine the best technological solution for it.
CG4. Capacity for critical analysis and rigorous evaluation of technologies and methodology.
CG5. Ability to identify unsolved problems and provide innovative solutions.
CG7. Autonomous learning ability for specialization in one or more fields of study.
CE1. Know and apply the concepts, methodologies and technologies of image processing.
CE3. Know and apply the concepts, methodologies and technologies of image and video analysis.
Teaching methodology
The methodology followed uses the Virtual Campus of the USC-UDC as a basic platform. In the virtual classroom of the subject, the students will have all the information (theory material, class slides, practice scripts, etc.)
* Master sessions: oral exposition complemented with the use of audiovisual media and the introduction of questions for the students, in order to transmit knowledge and facilitate learning.
* Laboratory practices: Practical resolution of different image problems through the application of image processing techniques explained during the master sessions.
* Research: proposal of two practical situations in image analysis that require students to identify the problem under study, formulate it accurately, develop the relevant procedures, interpret the results and draw the appropriate conclusions from the work done.
The CT1, CG2, CG4, CG5 and CG7 competences are developed mainly in the development of research projects, and the CE1 and CE2 competences are developed in master classes, carrying out exercises and research projects.
IN CASE THE USC DETERMINES THE STEP TO SCENARIO 2 (CLEARANCE) OR SCENARIO 3 (CLOSURE OF FACILITIES), THE TEACHING METHODOLOGY WILL BE MODIFIED ACCORDING TO THE CONTINGENCY PLAN INDICATED IN THE SECTION «OBSERVATIONS»
Evaluation system
The evaluation of the subject consists of two parts that must be passed independently:
40%: The part related to the presentation of the master sessions will be evaluated by means of a final written test with theoretical questions and practical problems. Alternatively, this part can be overcome through the continuous evaluation of laboratory practices, which will assess the adequacy of the proposed solutions to the problems, the quality of the results obtained and the understanding of the techniques used.
It is used to assess CE1 and CE3 competencies mainly.
60%: Resolution of two practical cases (research project). The adequacy of the proposed solutions to the problems, the quality of the results obtained and the understanding of the techniques used will be assessed.
It is used to evaluate the CT1, CG2, CG4, CG5 and CG7 competencies mainly.
In the case of fraudulent performance of exercises or tests, the provisions of the Regulations for academic performance of students and review of qualifications will apply.
In application of the ETSE regulations on plagiarism (approved by the ETSE Council on 12/19/2019) , the total or partial copy of some practice or theory exercise will mean failure on both occasions of the course, with a grade of 0.0 in both cases.
IN CASE THE USC DETERMINES THE STEP TO SCENARIO 2 (CLEARANCE) OR SCENARIO 3 (CLOSURE OF FACILITIES), THE EVALUATION SYSTEM WILL BE MODIFIED ACCORDING TO THE CONTINGENCY PLAN INDICATED IN THE SECTION «OBSERVATIONS»
Studying time and personal work
This subject has 6 ECTS credits, corresponding to a total workload of 150h (presentiality of 7h / credit). This time can be broken down into the following sections:
PRESENTIAL WORK IN CLASSROOM:
* Master classes: 14 hours
* Laboratory practices: 15 hours
* Research (project): 10 hours
* Objective test: 3 hours
Total hours of classroom work in the classroom: 42 hours
PERSONAL WORK OF THE STUDENTS:
* Autonomous study: 24 hours
* Laboratory practices: 44 hours
* Research (project): 40 hours
Total: 108 hours
Subject study recommendations
It is recommended to bring the matter up to date and the use of tutorials to clarify doubts and advise in the development of the project.
Observations
The virtual campus of the UDC and the USC will be used for each of the parties.
The subject will be taught in English.
Contingency plan:
In the event that the health situation advises establishing a Scenario 2 (distancing):
1) all the expository classes will be taught online (synchronously by Microsoft Teams or asynchronously through the publication of videos recorded by the teaching staff),
2) interactive classes will be taught in person in a computer room,
3) the weight of the different parts of the subject and the requirements to overcome it will remain unchanged,
4) The final test will be done in person.
In the event that the health situation advises establishing a Scenario 3 (closure of facilities):
1) all the expository classes will be taught online (synchronously by Microsoft Teams or asynchronously through the publication of videos recorded by the teaching staff),
2) all interactive classes will be taught online (synchronously by Microsoft Teams or asynchronously through the publication of videos recorded by teachers),
3) the weight of the different parts of the subject and the requirements to overcome it will remain unchanged,
4) The final test will be done in person, using Microsoft Teams and the tools of the Moodle virtual classroom.