460-6013/02 – Image Analysis (ANO)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantordoc. Dr. Ing. Eduard SojkaSubject version guarantordoc. Dr. Ing. Eduard Sojka
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFEI, FS, HGF, USPIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
SOJ10 doc. Dr. Ing. Eduard Sojka
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Combined Examination 28+0

Subject aims expressed by acquired skills and competences

The aim of the course is to acquaint students of doctoral studies with the problems of image analysis, including analysis of images of 3D scenes.

Teaching methods

Lectures
Individual consultations

Summary

The course focuses on the following topics: Image segmentation methods: detecting the areas, detecting the edges, Canny edge detector, edge linking, Hough transform, corner detectors. Variational methods in image segmentation, level-set methods, mean-shift methods, watershed methods. Object features for recognition. Classification, discriminative functions, neural networks, adaptive boosting, support vector machines. Deep/convolutional neural networks. 3D reconstruction, determining the correspondences between images. Analysing the images varying in time, object tracking, human-action recognition.

Compulsory literature:

Skansi, S.: Introduction to Deep Learning, Springer 2018, ISBN 978-3-319-73004-2 C.M. Bishop, Pattern Recognition and Machine Learning, Springer 2006, (ISBN 0-387-31073-8, 978-0-387-31073-2). A Hornberg, Handbook of Machine Vision, Wiley VCH, 2006, (ISBN 978-3-527-40584-8). E.Sojka, Digitální zpracování a analýza obrazů, VŠB-TU Ostrava, 2000 (ISBN 80-7078-746-5).

Recommended literature:

Charu C. Aggarwal: Neural Networks and Deep Learning, Springer 2018, ISBN 978-3-319-94463-0 Hornberg A. (Editor): Handbook of Machine and Computer Vision: The Guide for Developers and Users, 2nd Edition, Wiley 2017, ISBN: 978-3-527-41339-3 Al Bovik, Handbook of Image and Video processing 2nd Edition, Elsevier Academic Press, 2005, (ISBN 0121197921, 978-0121197926). R.C. Gonzales and R.E. Woods, Digital Image Processing, (3rd edition), Prentice Hall, 2008, (ISBN 0-13-505267-X). W.K. Pratt, Digital image processing: PIKS inside (3rd edition), John Wiley and Sons, 2001 (ISBN 0-471-37407-5). K.R. Castleman: Digital Image Processing, Prentice Hall 1996 (ISBN 0-13-211467-4).

Way of continuous check of knowledge in the course of semester

A text describing the work of student in a choosen area should be carried out as a practical preparation for the examination; the expected length is approximately 10 pages A4

E-learning

Další požadavky na studenta

No additional requirements

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Image segmentation, edge detection (gradient and zero-crossing methods, Canny detector), key-point detection, Hough transform 2. Variational formulation of image segmentation, level-set approaches, graph approaches, mean-shift approaches 3. Measuring objects for recognition, hand-crafted global object descriptors, universal local descriptors (HOG, SIFT, SURF, LBP) 4. Classification, discriminant functions for classification, support vector machines, neural networks, adaptive boosting (AdaBoost) 5. Deep/convolutional neural networks 6. Reconstructing 3D coordinates from images, camera calibration, finding correspondence between images 7. Analysing images varying in time, object tracking, human action recognition from video sequences

Conditions for subject completion

Combined form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

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