460-4107/02 – Image Analysis II (ANO II)
Gurantor department | Department of Computer Science | Credits | 4 |
Subject guarantor | Ing. Radovan Fusek, Ph.D. | Subject version guarantor | Ing. Radovan Fusek, Ph.D. |
Study level | undergraduate or graduate | Requirement | Optional |
Year | 2 | Semester | winter |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Follow-up Master |
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
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. 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.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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