IDENTIFYING DATA 2016_17
Subject (*) ARTIFICIAL VISION AND PATTERN RECOGNITION Code 17665209
Study programme
Computer Engineering: Computer Security and Intelligent Systems (2013)
 Cycle 2nd
Descriptors Credits Type Year Period
4.5 Optional 1Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
PUIG VALLS, DOMÈNEC SAVI
 E-mail susana.alvarez@urv.cat
domenec.puig@urv.cat
elnaz.jahani@urv.cat
Lecturers
ALVAREZ FERNANDEZ, SUSANA MARIA
PUIG VALLS, DOMÈNEC SAVI
JAHANI HERAVI, ELNAZ
Web http://consultar l'espai Moodle de l'assignatura
General description and relevant information To introduce the fundamental concepts of Computer Vision and further advanced topics related to problems of analysis and automatic recognition of complex images. Theoretical concepts and practical applications will be studied by means of well-known tools of Image Processing and Computer Vision.

Competences
Type A Code Competences Specific
 A1 Project, calculate and design products, processes and installations in all areas of computer engineering.
 A3 Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in all areas related to computer engineering.
 D1 Integrate the fundamental technology, applications, services and systems of computer engineering, in general, and in a broader, multidisciplinary context.
 T9 Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
Type B Code Competences Transversal
 B2 Aplicar el pensament crític, lògic i creatiu, demostrant capacitat d’innovació.
 B3 Treballar de forma autònoma amb responsabilitat i iniciativa.
Type C Code Competences Nuclear
 C2 Be advanced users of the information and communication technologies
 C3 Be able to manage information and knowledge
 C5 Be committed to ethics and social responsibility as citizens and professionals

Learning outcomes
Type A Code Learning outcomes
 A1 Integrate theoretical knowledge into the realities to which it may apply.
Are familiar with Spanish institutions and organisations related to the area studied.
 A3 Apply the techniques learned in a specific context.
 D1 Analyse the problems and their causes from a global focus in the medium and long term.
 T9 Know how to implement advanced computer vision techniques.
Type B Code Learning outcomes
 B2 Identify things that need to be improved in complex situations and contexts.
Apply innovative techniques and obtain results.
 B3 Take correct decisions at key moments confidently, consistently and systematically.
Type C Code Learning outcomes
 C2 Understand the operating system as a hardware manager and the software as a working tool.
 C3 Locate and access information effectively and efficiently.
 C5 Respect fundamental rights and equality between men and women.

Contents
Topic Sub-topic
Chapter 1.- Image Processing. Filtering, image compensation and image enhancement, morphological operations.
Chapter 2.- Geometrical Feature Extraction. Identification of corners, lines and basic geometrical shapes.
Chapter 3.- Color and Texture Analysis. Color models, texture types, extraction of textural features, geometric methods.
Chapter 4.- Image Segmentation and Classification. Unsupervised segmentation based on contours and regions, supervised classification, methods of decision theory, probabilistic methods, neural networks.
Chapter 5.- Stereoscopic Vision. Calibration of cameras and camera systems, epipolar geometry, image rectification, matching, triangulation.
Chapter 6.- Perception and 3D-modeling. Generation of depth maps, extraction of basic geometric elements,
automatic generation of scenes, scene recognition, geometric hashing.

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
A1
 1 1.5 2.5
Practicals using information and communication technologies (ICTs) in computer rooms
A1
D1
B3
C2
C3
C5
 10 15 25
Presentations / expositions
B2
B3
 1 1.5 2.5
Lecture
A1
A3
D1
T9
 25 33.5 58.5
Problem solving, classroom exercises
A3
D1
T9
B2
 4 6 10
Personal tuition
B2
B3
 1 0 1
 
Objective short-answer tests
T9
C2
C3
 1 5 6
Extended-answer tests
A3
T9
B2
 2 5 7
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities Introduction to the course: motivation, objectives, contents, teaching methods, bibliography and evaluation.
Practicals using information and communication technologies (ICTs) in computer rooms Practical use of simulators related to course content and developing new functionalities.
Presentations / expositions Students perform oral presentation of their work going in depth into specific topics of the subject. Assessment by the teacher.
Lecture Explanation of theoretical contents by the teacher.
Problem solving, classroom exercises Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Final evaluation by the teacher.
Personal tuition Personal attention to each student by the teacher during the teacher's office hours.

Personalized attention
Description
Enquiries /Tutorials: Resolution of theoretical and practical questions. Correction of practices. Exams review.

Assessment
Methodologies Competences Description Weight        
Practicals using information and communication technologies (ICTs) in computer rooms
A1
D1
B3
C2
C3
C5
 Elaboration by the students of practical work related to the main topics of the course using the tools of computer vision explained in the practical classes. Elaboration of a report. 40
Presentations / expositions
B2
B3
 Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Oral presentation. Final evaluation by the teacher. 20
Objective short-answer tests
T9
C2
C3
 Written multiple choice tests related to the theoretical concepts taught in the course. 20
Extended-answer tests
A3
T9
B2
 Extended-answer tests 20
Others  
 
Other comments and second exam session

Students who fail the continuous assessment can recover parts suspended or not presented in the second call.

In all written examinations can not bring any electronic device.


Sources of information

Basic D.A. Forsyth, Computer Vision: A Modern Approach, Pearson Education, 2012
R. Szeliski, Computer vision: algorithms and applications, Springer, 2011
N.J. Hackensack, Handbook of pattern recognition and computer vision, Imperial College Press, 2010
L. Shapiro, G. Stockman, Computer Vision, Prentice Hall, 2001
E. Trucco, A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998

Complementary E.R. Davies, Machine Vision: Theory, Algorithms, Practicalities, Academic Press, 1997
O. Faugeras, Three-Dimensional Computer Vision, MIT Press, 1993

Recommendations
(*)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.