Curricular Unit:Code:
Computer Vision833VCOP
Year:Level:Course:Credits:
2MasterComputer Systems Engineering (Information Systems and Multimedia)5 ects
Learning Period:Language of Instruction:Total Hours:
Winter SemesterPortuguese/English65
Learning Outcomes of the Curricular Unit:
Competence levels (Dublin descriptors):1. Knowledge and understanding:
1.1. The evolution of key VC technologies and systems and their impact
1.2. Of the most important VC algorithms.
1.3. Knowledge of basic VC techniques.
1.4. Knowledge of the skills required to develop VC applications in general
2. Applying knowledge and understanding2.1. Integrate VC application development teams.
2.2. Use, integrate and implement algorithms studied in the resolution of problems related to digital image processing and VC
3. Making judgements:3.1. Choice of means, formats and algorithms appropriate to specific situations
3.2. Participate in decision-making in the development of VC projects.
4. Communication:
4.1. Work in a group.
4.2. Argue orally and in writing
5. Autonomy and learning skills:
5.1. Autonomy to overcome difficulties inherent to new problems
Syllabus:
1. Introduction to Computer Vision
1.1. Intro to Computational Vision (VC)
1.2. VC Application Domains
2. Fundamentals of image
2.1. Introduction
2.2. Visual perception
2.3. Image formation
3. Morphological image processing
3.1. Introduction
3.2. Mathematical morphology
3.3. Morphological algorithms
4. Image filtering and enhancement
4.1. Filtering in the spatial domain
4.2. Filtering in the frequency domain
5. Color Image Processing
5.1. Introduction
5.2. Color spaces
5.3. Color-based targeting
6. Image segmentation
6.1. Intro do image segmentation
6.2. Corner detection
7. Representation and description
7.1. Description and representation of images
7.2. Description and representation of regions
8. Object recognition
8.1. Introduction
8.2. Pattern Recognition
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
The syllabus presented are consistent with the learning objectives of the curricular unit since there is a large convergence between the table of contents and the knowledge that the student is supposed to acquire in each of the program topics.
The fundamental concepts about computer vision, image fundamentals and image formation are presented in the introductory chapters, in the following chapters are presented various concepts, techniques and algorithms related to image processing in gray scale and color. Are still covered algorithms and segmentation techniques, image description and recognition of visual objects.
The learning objectives are achieved by supplementing the theoretical concepts with concrete examples and exercises run in lab environment using appropriate software
Teaching Methodologies (Including Evaluation):
Theoretical-practical (TP) and practical-laboratory (PL) components. In TP classes, concepts are presented interspersed with examples and exercises. In PL classes are posed and solved exercises and problems, eventually using software.
Final_Grade_VC = (1/2) x Grade_TP + (1/2) x Grade_PL
Required:
 Grade_TP> = 10
 Grade_PL> = 10
Grade_PL: compulsory for continuous evaluation and is not subject to examination. It consists of a programming project carried out during the semester. If student does not submit the project will have zero and can only carry out the project in a subsequent edition of the UC.
 Grade_PL = Practical project grade
Grade_TP:
 In continuous evaluation, two frequencies F1 and F2, Grade_TP = (50%) x F1 + (50%) x F2
 On exam, Grade_TP = Grade_TP_Examination
If only TP or PL is positive, it is valid for a maximum period of two academic years after the year in which the student has obtained approval in this component. During this period the UC is not completed (NC)
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The teaching/learning methodology applied in this curricular unit as well as its evaluation system is perfectly aligned with the objectives to be attained by the students at the end of the term. The theoretical concepts are presented, discussed, applied and evaluated in the context of lectures, which guarantees students a solid foundation to understand the challenges facing this area of knowledge. On the other hand, so that the study is not restricted to conceptual models, in the practical lessons are presented concrete case studies and implemented solutions for real problems using appropriate software. This combination guarantees training for students that allows them to meet the scientific goals, essential to a good understanding of the theme, as well as the ability to adapt to technological changes. The evaluation process consists of theoretical tests and practical work also guarantees a correct balance between the efforts dedicated to both components. The objective is to train professionals’ specialized in state-of-the-art techniques and tools but also ensure its ability to follow future developments. In this curriculum unit the major computer vision concepts and techniques are presented and evaluated in theoretical component. These concepts are then applied in the resolution of the worksheets and practical work in the context of practical classes
Reading:
[1] Linda Shapiro, George Stockman, "Computer Vision", Prentice Hall, 2001
[2] Rafael Gonzalez, Richard Woods, "Digital Image Processing (3rd Edition)", Prentice Hall, 2007
[3] Richard Szeliski, "Computer Vision: Algorithms and Applications", Springer, 2010 (http://szeliski.org/Book/)
[4] Jain, Kasturi, Schunck, "Machine Vision", McGraw-Hill, 1995
[5] Kaehler, Bradski, "Learning OpenCV: Computer Vision in C++ with the OpenCV Library", O'Reilly Media, 2nd edition, 2012
[6] Forsyth, Ponce, "Computer Vision: A Modern Approach", Pearson, 2002, ISBN 0130851981