Computer vision
BASIC DATA
course listing
A - main register
course code
ITS8030
course title in Estonian
Tehisnägemine
course title in English
Computer vision
course volume CP
-
ECTS credits
6.00
to be declared
yes
assessment form
Examination
teaching semester
spring
language of instruction
Estonian
English
Study programmes that contain the course
Structural units teaching the course
IT - Department of Software Science
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
* Anda ülevaade olulisematest põhimõtetest rasterkujutiste töötlemisel.
* Anda ülevaade tehisnägemises kasutatavatest kontseptsioonidest tunnuste tuvastamisel ja eraldamisel rasterkujutistest.
* Tutvuda valiku edukate tehisnägemissüsteemidega ning analüüsida nende piiranguid.
* Õppida tundma objektide tuvastuse, grupeerimise ja jälgimise printsiipe.
* Õppida tundma kujutiste klassifitseerimise lähenemisi.
* Õppida tundma kujutiste otsingu tööpõhimõtteid.
* Anda ülevaade tehisnägemises kasutatavatest kontseptsioonidest tunnuste tuvastamisel ja eraldamisel rasterkujutistest.
* Tutvuda valiku edukate tehisnägemissüsteemidega ning analüüsida nende piiranguid.
* Õppida tundma objektide tuvastuse, grupeerimise ja jälgimise printsiipe.
* Õppida tundma kujutiste klassifitseerimise lähenemisi.
* Õppida tundma kujutiste otsingu tööpõhimõtteid.
course aims in English
* To give an overview of most important principles in raster image processing.
* To give an overview of approaches and concepts in feature detection and extraction in machine vision.
* To learn to use a selection of successful applications of computer vision and understand their limitations.
* To learn the principles of object detection, grouping and tracking.
* To understand different approaches to classification of images.
* To understand the principles of image search.
* To give an overview of approaches and concepts in feature detection and extraction in machine vision.
* To learn to use a selection of successful applications of computer vision and understand their limitations.
* To learn the principles of object detection, grouping and tracking.
* To understand different approaches to classification of images.
* To understand the principles of image search.
learning outcomes in the course in Est.
Aine läbinud üliõpilane:
* tunneb objektide tuvastuse, grupeerimise ja jälgimise printsiipe;
* tunneb kujutise klassifitseerimise meetodeid;
* teab, kuidas toimib kujutiste otsing;
* oskab hinnata tehisnägemissüsteemide rakendatavust konkreetses kontekstis ja valida konkreetse ülesande lahendamise jaoks sobivat tehisnägemislahendust;
* oskab orienteeruda tehisnägemisalases teaduskirjanduses.
* tunneb objektide tuvastuse, grupeerimise ja jälgimise printsiipe;
* tunneb kujutise klassifitseerimise meetodeid;
* teab, kuidas toimib kujutiste otsing;
* oskab hinnata tehisnägemissüsteemide rakendatavust konkreetses kontekstis ja valida konkreetse ülesande lahendamise jaoks sobivat tehisnägemislahendust;
* oskab orienteeruda tehisnägemisalases teaduskirjanduses.
learning outcomes in the course in Eng.
Upon completion of the course, the student:
* Knows the principles of object detection, grouping and tracking;
* Knows the methods of image classification;
* Knows how image search is performed;
* Is able to evaluate the applicability of computer vision systems to particular applications and choose a suitable computer vision to a particular application;
* Is able to navigate computer vision research literature.
* Knows the principles of object detection, grouping and tracking;
* Knows the methods of image classification;
* Knows how image search is performed;
* Is able to evaluate the applicability of computer vision systems to particular applications and choose a suitable computer vision to a particular application;
* Is able to navigate computer vision research literature.
brief description of the course in Estonian
Kaamera mudelid
Valgus ja varjud
Värv
Lineaarsed filtrid
Lokaalsed kujutise tunnused
Tekstuur
Stereopilt
Struktuur liikumisest
Segmenteerimine klasterdamise kaudu
Grupeerimine ja mudeli sobitamine
Jälgimine
Objektide registreerimine
Kaugusinfo
Kujutiste klassifitseerimine
Objektide tuvastamine kujutistel
Kujutisepõhine modelleerimine ja renderdamine
Inimeste nägemine
Valgus ja varjud
Värv
Lineaarsed filtrid
Lokaalsed kujutise tunnused
Tekstuur
Stereopilt
Struktuur liikumisest
Segmenteerimine klasterdamise kaudu
Grupeerimine ja mudeli sobitamine
Jälgimine
Objektide registreerimine
Kaugusinfo
Kujutiste klassifitseerimine
Objektide tuvastamine kujutistel
Kujutisepõhine modelleerimine ja renderdamine
Inimeste nägemine
brief description of the course in English
Camera models
Light and shading
Color
Linear filters
Local image features
Texture
Stereopsis
Structure from motion
Segmentation by clustering
Grouping and model fitting
Tracking
Registering objects
Range data
Classifying images
Detection of objects in images
Image based modelling and rendering
Looking at people
Light and shading
Color
Linear filters
Local image features
Texture
Stereopsis
Structure from motion
Segmentation by clustering
Grouping and model fitting
Tracking
Registering objects
Range data
Classifying images
Detection of objects in images
Image based modelling and rendering
Looking at people
type of assessment in Estonian
-
type of assessment in English
-
independent study in Estonian
-
independent study in English
-
study literature
https://courses.cs.ttu.ee/pages/ITS8030
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
2.0
practices
-
exercises
0.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2024/2025 spring
Juhan-Peep Ernits, IT - Department of Software Science
English