460-4107/02 – Image Analysis II (ANO II)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorIng. Radovan Fusek, Ph.D.Subject version guarantorIng. Radovan Fusek, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year2Semesterwinter
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
FUS032 Ing. Radovan Fusek, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Combined Credit and Examination 10+0

Subject aims expressed by acquired skills and competences

The goal of the course is to get the student acquainted with modern methods of image analysis that can be used in the area of object detection and recognition. An integral part is also application of this methods in the real world (e.g. detection and recognition of faces, localization of pedestrians, detection of cars).

Teaching methods

Lectures
Tutorials

Summary

The following topics are covered: Modern methods of object detection and object recognition. Typically, the approaches are based on the image descriptors that are combined with the machine learning methods. The principles and aplications of deep learning and convolutional neural networks are also covered (detection of vehicles, pedestrians, faces).

Compulsory literature:

1. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, Prentice Hall, ISBN-10: 013168728X, ISBN-13: 978-0131687288, 2007 2. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010

Recommended literature:

1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011 2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013 3. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017

Way of continuous check of knowledge in the course of semester

Conditions for granting the credit: The tasks that form the program of exercises must be worked out. Exam - written test.

E-learning

Další požadavky na studenta

No further requirements are imposed on student.

Prerequisities

Subject codeAbbreviationTitleRequirement
460-4080 ANO I Image Analysis I Recommended

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Main ideas behind object detection in images, a sliding window method. 2. Face detection methods, Haar-like features. 3. Local binary patterns for object detection. 4. Pedestrian detection methods, histograms of oriented gradients. 5. Keypoint detectors and descriptors (SIFT). 6. Main ideas behind object recognition. 7. Convolutional neural network. 8. AdaBoost method for recognising the objects in images. 9. The method of support vector machines and its use for object classification. 10. Detecting the background by the Gaussian mixture method. 11. Processing the images in IR spectrum and multispectral images. 12. Depth image processing (RealSense, Kinect). 13. LIDAR image processing. 14. Summary of lecture themes. Computer Labs: 1. Implementation of basic template for object detection in images. 2. Implementation of a sliding window method. 3. Preparing data for training and testing. 4. Object detection using Haar-like features. 5. Object detection using local binary patterns. 6. Keypoint detectors and descriptors (SIFT). 7. Convolutional neural network. 8. Object recognition with AdaBoost using histogram of oriented gradients descriptor. 9. Object recognition with Support Vector Machine using local binary pattern. 10. Background subtraction using Gaussian mixture and its acceleration on a GPU. 11. Object recognition in IR images. Image enhancements and subsequent processing. 12. Depth image processing (RealSense, Kinect). 13. Reserve. 14. Credit.

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
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  20
        Examination Examination 55  6
Mandatory attendence parzicipation:

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Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava 2 Compulsory study plan
2019/2020 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava 2 Optional study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P English Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K English Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner