Module also offered within study programmes:
General information:
Name:
Vision techniques
Course of study:
2013/2014
Code:
RMS-1-607-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
First-cycle studies
Specialty:
-
Field of study:
Mechatronics with English as instruction languagege
Semester:
6
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr hab. inż. Kohut Piotr (pko@agh.edu.pl)
Academic teachers:
dr hab. inż. Kohut Piotr (pko@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively MS1A_K04, MS1A_K05 Presentation,
Report,
Execution of laboratory classes,
Activity during classes,
Oral answer,
Project,
Scientific paper,
Participation in a discussion,
Involvement in teamwork
Skills
M_U001 ability to develop documentation related to the completion of an engineering task and prepare text discussing the results of the task, in the form of presentation and reports as well MS1A_U09, MS1A_U04, MS1A_U03 Presentation,
Project,
Report,
Execution of laboratory classes,
Activity during classes,
Scientific paper
M_U002 ability to use high-level programming to develop image processing techniques and program vision systems MS1A_U09, MS1A_U14 Test,
Report,
Execution of laboratory classes,
Activity during classes,
Oral answer,
Presentation,
Project
M_U003 ability to develop image processing algorithms and to program vision systems and sensors as well, to verify its operations experimentally , taking into consideration the required useful criteria and based on using proper methods, techniques and tools MS1A_U08, MS1A_U07, MS1A_U20, MS1A_U09, MS1A_U12 Test,
Report,
Execution of laboratory classes,
Activity during classes,
Oral answer,
Presentation,
Project,
Scientific paper
M_U004 ability to select appropriate image processing methods taking into consideration the required useful criteria MS1A_U08, MS1A_U12 Project,
Report,
Execution of laboratory classes,
Activity during classes,
Test,
Oral answer,
Presentation,
Scientific paper
M_U005 ability to use data sheets and application notes to select image processing methods and algorithms MS1A_U01, MS1A_U13 Project,
Report,
Execution of laboratory classes,
Activity during classes,
Presentation,
Scientific paper
Knowledge
M_W001 Detailed knowledge of image processing methods, and basic knowledge of parameters and principle of operation of the selected sensors and vision systems MS1A_W06 Test,
Report,
Execution of laboratory classes,
Activity during classes,
Oral answer,
Project,
Scientific paper
M_W002 well-ordered in current state and recent development trends of vision systems and vision techniques used in mechatronics and techniques MS1A_W13 Project,
Report,
Activity during classes,
Presentation,
Scientific paper
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Others
Zaj. terenowe
Zaj. warsztatowe
E-learning
Social competence
M_K001 awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively - - + - - - - - - - -
Skills
M_U001 ability to develop documentation related to the completion of an engineering task and prepare text discussing the results of the task, in the form of presentation and reports as well - - + - - - - - - - -
M_U002 ability to use high-level programming to develop image processing techniques and program vision systems - - + - - - - - - - -
M_U003 ability to develop image processing algorithms and to program vision systems and sensors as well, to verify its operations experimentally , taking into consideration the required useful criteria and based on using proper methods, techniques and tools - - + - - - - - - - -
M_U004 ability to select appropriate image processing methods taking into consideration the required useful criteria - - + - - - - - - - -
M_U005 ability to use data sheets and application notes to select image processing methods and algorithms - - + - - - - - - - -
Knowledge
M_W001 Detailed knowledge of image processing methods, and basic knowledge of parameters and principle of operation of the selected sensors and vision systems + - + - - - - - - - -
M_W002 well-ordered in current state and recent development trends of vision systems and vision techniques used in mechatronics and techniques + - - - - - - - - - -
Module content
Lectures:

Basic definitions related to digital image processing. Human’s sense of sight. Application of vision techniques and systems in mechatronics and techniques. Structure of the vision system and its components characteristics.
Image acquisition methods and image formulation.
Image pre-processing methods ( point and local operation).
Image pre-processing methods ( local and global methods).
Feature detection and tracking methods.
Image segmentation and analysis methods.
Image features measurement, their representation and appropriate description.
Objects recognition techniques.
Camera calibration methods.
Motion analysis and reconstruction techniques of 3D object structure.
The vision systems of the industrial robots, their programming and developing of image processing methods.
Software tools and devices for objects reconstruction and motion analysis
Prototyping of image processing algorithms in various development environment and in real time systems

Laboratory classes:

Image processing with the use of selected libraries and packages dedicated vision techniques.
Image acquisition and processing with the use of openCV libraries, calibration.
Image acquisition and processing in Matlab/Simulink environment, calibration.
Vision systems of the industrial robots – vision algorithms development and programming. Calibration methods.
Prototyping of image processing algorithms in real time systems.
Objects’ reconstruction and motion analysis in 2d and 3D.
(Project) Work out of a selected issue/ problem related to vision techniques.
a) Analysis of current state and selection of vision methods related to a given problem.
b) Analysis of current state and work out a given problem with the use of image processing libraries.

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 120 h
Module ECTS credits 4 ECTS
Participation in lectures 30 h
Participation in laboratory classes 30 h
Preparation for classes 25 h
Preparation of a report, presentation, written work, etc. 25 h
Completion of a project 10 h
Additional information
Method of calculating the final grade:

Weighted average marks of laboratory exercises (including colloquium) and projects: Assessment of laboratory exercises (including colloquium) (60%) and assessment of a project (40%)

Prerequisites and additional requirements:

Knowledge of computer science issues;
Ability to work in a package Matlab / Simulink;
Basics of programming in C;

Recommended literature and teaching resources:

Castleman K. R.: Digital Image processing, Prentice Hall, Upper Saddle River, New Jersey, 1996
Gonzales R.C, Woods R.E.: Digital Image Processing using Matlab, Prentice Hall , 2004
Tadeusiewicz R . Korohoda P., Komputerowa analiza i przetwarzanie obrazów, Wyd.FPT, 1997
Wróbel Z., Koprowski R.: Praktyka przetwarzania obrazów w programie Matlab, EXIT, 2005
Hartley R., Zisserman A., Multiple view geometry in computer vision, Cambridge Univ. Press,2003
Jain R., Kasturi R., Schunck B., Machine vision, McGraw-Hill Inc. New York, 1996
Ma Y., Soatto S., Kosecka J., Sastry S., An Invitation to 3D Vision, Springer-Verlag NY, 2004
Davies E. R., Computer and Machine Vision: Theory, Algorithms, Practicalities, Academic Press, 2005

Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None