460-4107/01 – 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 graduateRequirementOptional
Year2Semesterwinter
Study languageCzech
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFMT, FEIIntended 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
Part-time Credit and Examination 18+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. Chollet, F.: Deep Learning with Python. Manning, ISBN-13: 978-1617294433, 2017 2. Gonzalez, R. C., Woods, R. E.: Digital image processing, New York, NY: Pearson, ISBN-13: 978-0133356724, 2018 3. Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into Deep Learning, https://d2l.ai, 2020

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

Other requirements

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: * Basic concepts of object detection in images, sliding window method. * Methods of face detection in images. Haar type features. Local binary patterns, histograms of oriented gradients and their application to object analysis. Methods of pedestrian detection in images. * Convolutional neural networks (basic principles, description of layers). Modern variants of convolutional neural networks (e.g. VGGNet, GoogLeNet, ResNet). * Description of convolutional networks for object localization (e.g. R-CNN, Faster R-CNN, YOLO, SSD). * Description of generative networks (DCGAN), encoder-decoder networks, transformer networks. * Image descriptors (SIFT method). * Optical systems in the area of self-driving vehicles, IR image processing, LIDAR image processing, depth image analysis, use of depth sensors (RealSense, Kinect). Computer Labs: * Development of the detector for the selected object of interest, implementation of the sliding window method, preparation of data for the training and testing phases of the detector. * Detection based on Haar-type features, detection using local binary patterns, analysis of objects using gradients (HOG method), image descriptors (SIFT method). * Experiments with convolutional neural networks, exploring the parameters of convolutional networks. * Application of different types of convolutional networks (e.g. VGGNet, GoogLeNet, ResNet), comparison of detectors. * Practical usage of localization methods based on convolutional networks (e.g. R-CNN, Faster R-CNN, YOLO). * Practical usage of generative networks (DCGAN), encoder-decoder networks, transformer networks. * Analysis of objects in IR and depth images (RealSense, Kinect).

Conditions for subject completion

Full-time form (validity from: 2015/2016 Winter semester, validity until: 2021/2022 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  20
        Examination Examination 55  6 3
Mandatory attendence participation: The tasks that form the program of exercises must be worked out.

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Conditions for subject completion and attendance at the exercises within ISP:

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2022/2023 (N0613A140034) Computer Science DZO K Czech Ostrava 2 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science DZO P Czech Ostrava 2 Choice-compulsory type A study plan
2022/2023 (N0688A140014) Industry 4.0 P Czech Ostrava 2 Choice-compulsory type B study plan
2022/2023 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 2 Optional study plan
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2021/2022 (N0688A140014) Industry 4.0 P Czech Ostrava 2 Choice-compulsory type B study plan
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2020/2021 (N0688A140014) Industry 4.0 P Czech Ostrava 2 Choice-compulsory type B study plan
2020/2021 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
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2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics P Czech Ostrava 2 Optional study plan
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2019/2020 (N0541A170007) Computational and Applied Mathematics (S01) Applied Mathematics K Czech Ostrava 2 Optional study plan
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2019/2020 (N0688A140014) Industry 4.0 P Czech Ostrava 2 Choice-compulsory type B study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan

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