Subject » BMEVIIIAC04
Industrial Image Processing
Ipari képfeldolgozás és képmegjelenítés
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
—
| Subject name (Hungarian, English) |
Ipari képfeldolgozás és képmegjelenítés
Industrial Image Processing
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIIIAC04 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
|
||||||||||||
| Assessment type | vizsga | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
DR. Szemenyei Márton
position: egyetemi docens
contact:
szemenyei.marton@vik.bme.hu
|
||||||||||||
| Responsible department |
Irányítástechnika és Informatika Tanszék
|
||||||||||||
| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
1. The functioning of human vision, three-dimensional perception. Components of spatial perception, the basics of monocular and binocular perception. The concept and mathematical properties of visual functions. Color systems. Mathematical model of spatial visualization. Correlation between intensity and distance data. The role of reflection models in image interpretation. Coordinate transformations, camera models and calibration procedures. Basic sensing devices. Interfacing issues of image input devices - case study. (6 hours of lectures + 2 hours of practice sessions)
2. Getting started: Basics of image information processing. Binary image processing. Mathematical morphological foundations. Measurement of geometric properties. Questions of real-time realization.. Analysis of topological properties. The concept of additive set property measure. Euler number concept. Industrial application examples. (6 hours of lectures + 2 hours of practice sessions)
3. Preparatory processing of the images. Fourier transform. Effect of sampling and quantization. Other spatial transformations. Histogram transformations. Filters in the space and frequency domain. Mathematical model of image segmentation. Segmentation based on level similarity. Segmentation procedures based on rapid changes. Hough transform. Motion-based segmentation. Texture segmentation. Safety and traffic application examples. (6 hours of lectures + 2 hours of practice sessions)
4. Fast object tracking methods. Optical flow. Tracking colors, edges, textures. SSD algorithm. Visual feedback. Navigation, user tracking application examples. (3 hours of lectures + 1 hours of practice sessions)
5. Property representation. Object recognition (classification) methods. Active vision. Image compression procedures,. Search in image databases. Teleoperation application examples. (6 hours of lectures + 2 hours of practice sessions)
6. Comparison of possibilities and application techniques of modern image processing program libraries (e.g. Halcon, Matlab, ITK, openCV, LabView). Presentation of alternative solutions to simpler image processing problems. (3 hours of lectures + 1 hours of practice sessions)
7. Modern image display devices (e.g. HMD, polar filter, anaglyph, shutter, holoTV) and applied rendering methods. 3D display, design and application of 3D displays. Conversion of "traditional" images for stereo rendering. Immersive virtual reality in teleoperation. Medical and telerobotics examples. (6 hours of lectures + 2 hours of practice sessions)
8. Simulator systems. Product design and testing supported by hardware in the loop simulation. Automotive application examples. (3 hours of lectures + 1 hours of practice sessions)
9. IP-based image transmission. DSP-based smart cameras. Real-time image processing. Real-time procedures and architectures. Manufacturing automation application examples. (3 hours of lectures + 1 hours of practice sessions)
With the development of computer technology, the automatic evaluation of image-based information has become a daily practice in quality control, process management, navigation, safety technology, medical diagnostics and many other fields. With the use of increasingly better display techniques, graphic simulation and teleoperation have become everyday technologies. The aim of the subject is to introduce the principles and application of modern computer image processing and display procedures at a skill level, to present the virtual techniques that play a key role in the management of remotely monitored autonomous industrial processes and autonomous warehouse management.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
The knowledge of the subject is presented in lectures, and the case studies are presented during practice sessions. For the exercises, each student works on a case study independently. During the semester, students prepare a homework assignment that contains a (partial) solution to a practical application.
Tanulástámogató anyagok
Online források
R. Gonzales: Digital Image Processing, Addison-Wesley, ISBN 0-201-50803-6; Besl, PJ: "Surfaces in range image understanding". Springer, 1988.
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Mathematics, Signals and systems, Programming, Image processing and basic knowledge of computer graphics
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Mathematics, Signals and systems, Programming, Image processing and basic knowledge of computer graphics
General rules
Requirements:
During the period of classes: case study prsenetation, homework assignment and midterm. The case study focuses on the study of literature, the homework is a independent programming task. The midterm checks the understanding of the theoretical material.
During the exam period: witten exam.
Additional possibilities:
The submission of the homework can be dealayed for the retake week. One retake opportunity is available for the modterm.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
—
Curriculum placement
No curriculum placements recorded for this subject version.