Identifying Data					2020/21
Subject (*)	Advanced Image Processing and Analysis			Code	614535002
Study programme	Máster Universitario en Visión por Computador
Descriptors	Cycle	Period	Year	Type	Credits
	Official Master's Degree	2nd four-month period	First	Obligatory	6
Language	English
Teaching method	Hybrid
Prerequisites
Department	Ciencias da Computación e Tecnoloxías da Información
Coordinador	Barreira Rodriguez, Noelia		E-mail	noelia.barreira@udc.es
Lecturers	Barreira Rodriguez, Noelia Rouco Maseda, Jose		E-mail	noelia.barreira@udc.es jose.rouco@udc.es
Web
General description	Esta materia contén temas avanzados en procesado e análise de imaxe e preséntase como a segunda parte doutra materia onde se tratan os temas fundamentais. Foi deseñada para proporcionar os fundamentos esenciais para estudantes que queiran continuar coa investigación nesta área. Ademais do estudo de técnicas avanzadas en procesado e análise de imaxe, estudaranse aplicacións nesta área para resolver problemas reais. Esta materia proporciona as ferramentas necesarias para aplicar os algoritmos estudados en casos prácticos así como para desenvolver novos algoritmos.
Contingency plan	1. Modificacións nos contidos - Non hay cambios 2. Metodoloxías *Metodoloxías docentes que se manteñen - Sesións maxistrais - Prácticas de laboratorio - Proba obxectiva *Metodoloxías docentes que se modifican 3. Mecanismos de atención personalizada ao alumnado - Correo electrónico: diariamente, para resolver dúbidas e programar encontros virtuais. - Moodle: diariamente, dependendo das necesidades dos estudantes. - Teams: diariamente, dependendo das necesidades dos estudantes e unha sesión en grupo semanal para asegurar o avance na aprendizaxe e o desenvolvemento das prácticas de laboratorio. 4. Modificacións na avaliación - Non hai cambios *Observacións de avaliación: 5. Modificacións da bibliografía ou webgrafía - Non hay cambios

Study programme competencies Code Study programme competences A1 CE1 - To know and apply the concepts, methodologies and technologies of image processing A3 CE3 - To know and apply the concepts, methodologies and technologies of image and video analysis A4 CE4 - To conceive, develop and evaluate complex computer vision systems A5 CE5 - To analyze and apply methods of the state of the art in computer vision B1 CB6 - To possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context B5 CB10 - That students possess the learning skills to enable them to continue studying in a largely self-directed or autonomous manner B7 CG2 - Ability to analyze a company's needs in the field of computer vision and determine the best technological solution for it B8 CG3 - Ability to develop computer vision systems depending on existing needs and apply the most appropriate technological tools B10 CG5 - Ability to identify unsolved problems and provide innovative solutions B12 CG7 - Ability to learn autonomously for specialization in one or more fields of study

Learning aims

Learning outcomes	Study programme competences
Study and application of advanced digital image processing techniques.	AC1	BC5 BC12
Study and application of advanced techniques of digital image analysis.	AC3	BC5 BC12
Analysis of real problems, and design and development of solutions based on advanced image processing and analysis technologies.	AC4 AC5	BC1 BC5 BC7 BC8 BC10 BC12
Evaluation of the adequacy of the methodologies applied in specific problems.	AC4

Contents

Topic	Sub-topic
Advanced denoising	Total variation
Advanced edge detection	Bilateral filter Anisotropic diffusion Phase congruence
Advanced segmentation	Deformable models Level-set methods Markov Random Fields Graph cuts
Learning-based segmentation	Active shape/appearance models
Salience and attention models
Selected topics on advanced image processing and analysis	Semantic segmentation Multi-view enhancement Superresolution Inpainting Coloring Photo stitching Background removal

Planning

Methodologies / tests	Competencies	Ordinary class hours	Student’s personal work hours	Total hours
Laboratory practice	A1 A3 A4 A5 B5 B7 B8 B10 B12	25	84	109
Objective test	B1 B8 B10	3	0	3
Guest lecture / keynote speech	A1 A3	14	24	38
Personalized attention		0		0
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies	Description
Laboratory practice	Analysis and resolution of practical cases using techniques learned in the lectures.
Objective test	Test with questions about the theoretical contents of the subject as well as practical problems.
Guest lecture / keynote speech	Oral presentation (using audiovisual material and student interaction) designed to transmit knowledge and encourage learning.

Personalized attention

Methodologies Laboratory practice

Description Teachers will answer the doubts during the laboratory practice and they will provide personal advising for the supervised projects.

Assessment

Methodologies	Competencies	Description	Qualification
Objective test	B1 B8 B10	Written test with theoretical questions and practical problems to be solved.	40
Laboratory practice	A1 A3 A4 A5 B5 B7 B8 B10 B12	Two assignments that consist of the development of image processing and computer vision applications. It will be assessed the suitability of the proposed solutions and the quality of the obtained results.	60
Assessment comments

Sources of information

Basic	Simon J.D. Prince (2012). Computer Vision: Models, Learning, and Inference. Cambridge University Press David A. Forsyth, Jean Ponce (2002). Computer vision: a modern approach. Prentice - Hall Richard Szeliski (2010). Computer vision: algorithms and applications. Springer Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016). Deep learning. MIT Press Gary Bradski, Adrian Kaehler (2008). Learning OpenCV. O'Reilly
Complementary

Recommendations

Subjects that it is recommended to have taken before

Fundamentals of Machine Learning for Computer Vision /614535007 Fundamentals of Image Processing and Analysis /614535001 Image Description and Modeling/614535004

Subjects that are recommended to be taken simultaneously

Visual Recognition/614535005 Advanced Machine Learning for Computer Vision/614535008

Subjects that continue the syllabus

Other comments

(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.