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Image Analysis II

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-4107/01
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Choice-compulsory
Level of Course Unit *Second Cycle
Year of Study *Second Year
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites
PrerequisitiesCourse Unit CodeCourse Unit Title
460-4080Image Analysis I
Name of Lecturer(s)Personal IDName
FUS032Ing. Radovan Fusek, Ph.D.
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).
Learning Outcomes of the Course Unit
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).
Course Contents
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. Convolutional neural networks (basic principles, layers).
7. Modern types of convolutional neural networks (e.g. VGGNet, GoogLeNet, ResNet).
8. Object localization using convolutional neural networks (e.g. R-CNN, Faster R-CNN, YOLO).
9. Optical systems in autonomous vehicles.
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. Application of convolutional neural networks.
8. Experiments with parameters of convolutional neural networks.
9. Experiments with different types of convolutional neural networks (e.g. VGGNet, GoogLeNet, ResNet).
10. Experiments with created detectors, comparison of the results.
11. Object recognition in IR images. Image enhancements and subsequent processing.
12. Depth image processing (RealSense, Kinect).
13. Reserve.
14. Credit.
Recommended or Required Reading
Required Reading:
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
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 Reading:
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
1. E. Sojka, J. Gaura, and M. Krumnikl, Matematické základy digitálního zpracování obrazu. Ostrava, Plzeň: VŠB-TU Ostrava (Fakulta elektrotechniky a informatiky), Západočeská univerzita v Plzni, 2. vydání ed., 2011.
2. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011
3. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013
4. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Credit and ExaminationCredit and Examination100 (100)51
        CreditCredit45 20
        ExaminationExamination55 6