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Image Analysis and Recognition

Code: PDEEC0040     Acronym: IAR

Keywords
Classification Keyword
OFICIAL Electrical and Computer Engineering

Instance: 2019/2020 - 2S Ícone do Moodle

Active? Yes
Web Page: http://moodle.fe.up.pt/
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Doctoral Program in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
PDEEC 14 Syllabus since 2015/16 1 - 7,5 70 202,5

Teaching Staff - Responsibilities

Teacher Responsibility
Jaime dos Santos Cardoso

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Jaime dos Santos Cardoso 3,00

Teaching language

English

Objectives

This graduate course on Image Analysis and Recognition aims to give the student the ability to understand and apply some of the recent advances in this rapid evolving field of Image Analysis and recognition. There is a text book together with a list of selected original research papers in order to allow the students to follow the advances in the addressed topics. The course main topics will allow the students to gain the competences in: image segmentation, tracking, Image registration and object and pattern recognition and matching. The course will discuss the use of the learned methods and techniques in applications such as visual inspection, document processing, biomedical and biometrics.

Learning outcomes and competences

The course main topics will allow the students to gain the competences in: image segmentation, tracking, Image registration and object and pattern recognition and matching. The course will discuss the use of the learned methods and techniques in applications such as visual inspection, document processing, biomedical and biometrics.

Working method

Presencial

Program

1. Image Enhancement a. Intensity, local and frequency methods b. Wavelets 2. From color to edges and textures a. Edge and corner detection b. Texture analysis 3. Segmentation a. Introduction b. Image Segmentation using basic clustering methods. Embedding local constrains. Mean Shift c. Segmentation by graph-theoretic clustering. Graphs. Affinity measures. Graph Cuts and normalized cuts. 4. Motion analysis a. Background subtraction b. Optical flow c. Tracking i. Tracking using linear dynamical models: Kalman filtering. (Examples: tracking people, tracking vehicles). ii. Tracking with non-linear dynamical models: Extended Kalman filtering and Particle filtering. 5. Image Registration a. Multi view geometry i. Projective transforms and invariance. ii. Photometric transforms and invariance. iii. Cameras and calibration. b. Strategies for image registration of rigid and non-rigid objects. c. Local invariant features and similarity measures.. 6. Image Recognition a. Machine learning tools b. Feature extraction and selection: i. Principal Component analysis. ii. Object and shape representation using invariant features. c. Object modeling i. Active appearance models. ii. Constellation model and the Implicit Shape Model. iii. Bag of visual term models. d. Recognition examples (face detection and recognition, pedestrian finding)

Mandatory literature

Forsyth and Ponce; Computer Vision. A Modern Approach,, Prentice Hall, 2002

Complementary Bibliography

R. O. Duda, P. E. Hart, D. G. Stork; Pattern classification, John Wiley & Sons, 2001

Teaching methods and learning activities

The course is organized in a set of 28 lectures together with oral presentation and  computer vision labs.

Software

Matlab 6 R12.1

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 30,00
Prova oral 10,00
Trabalho de campo 30,00
Trabalho laboratorial 30,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Elaboração de projeto 40,00
Estudo autónomo 84,00
Frequência das aulas 42,00
Trabalho de investigação 8,00
Trabalho laboratorial 18,00
Total: 192,00

Eligibility for exams

Not applicable

Calculation formula of final grade

Grading and evaluation is based on the following scheme: Assignments: 30% (10% for each one of the 3 assignments) Presentation of a selected research paper: (10%) Project: 30% Final exam: 30% Grading will be from 0 to 20. A PASSing grade corresponds to a minimum of 10.

 

Examinations or Special Assignments

The workload of the course consists of 3 assignments, presentation of one reading and a project. List of assignments: Assignment 1: Image Segmentation Assignment 2: Human Tracking in Videos Assignment 3: Image Registration/Recognition The assignments can be developed by groups of 2 students. However, the groups’ members must vary between assignments. A report with the full analysis of the applied methodologies and results must be presented by the students, which must include results, illustrations of results and a discussion of the results. Additionally, commented code (published html) must be presented of the methodologies’ implementation. The report must be presented in a pdf file. A small 10 minutes presentation of the performed work may also be requested. To each student will be assigned a reading of a selected research paper that will be presented during the classes during the semester. A project will be also assigned to a team of 2 students in the second half of the course. A report in IEEE PAMI format is required with a minimum of 6 pages and a maximum of 10 pages. Commented code and illustrative results must be reported in an additional pdf file. The projects will be presented in special sessions of the course. NOTE: it is recommended that the chosen project overlaps with the students’ existing specific interest areas. While the student may present a project of his/her choice the acceptance of such project requires the approval of the professors in charge. Late policy: The students must meet the deadlines. One week delay is allowed for the assignments and project with a 10% penalty on the corresponding grade.

Internship work/project

Not applicable

Special assessment (TE, DA, ...)

Not applicable

Classification improvement

Not applicable

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