Lectures:
- Basic concepts of object detection in images; Haar-like features (AdaBoost, Viola-Jones); Local Binary Patterns (LBP); Histograms of Oriented Gradients (HOG). Methods for pedestrian detection in images; methods for face detection in images. Keypoint detectors and descriptors (e.g. SIFT, SURF).
- Convolutional neural networks (basic principles, description of layers). Modern variants of convolutional neural networks (e.g. GoogLeNet, ResNet, EfficientNet).
- Description of convolutional networks for object localization (e.g. R-CNN, Faster R-CNN, YOLO, SSD).
- Description of generative networks (e.g. DCGAN, Diffusion-GAN).
- Transformer networks (especially Vision Transformer - ViT) and their use in image analysis.
- Convolutional neural networks for image segmentation (encoder-decoder networks, U-Net).
- Human pose estimation using deep learning.
- Optical systems in the area of self-driving vehicles, IR image processing, LiDAR data 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.
- Object analysis using Haar-like features, Local Binary Patterns (LBP), and Histograms of Oriented Gradients (HOG).
- Experiments with convolutional neural networks, exploring the parameters of convolutional networks.
- Application of different types of convolutional networks (e.g. GoogLeNet, ResNet, EfficientNet), comparison of detectors.
- Practical use of localization methods based on convolutional neural networks (e.g. R-CNN, Faster R-CNN, YOLO).
- Practical use of generative networks for data augmentation (DCGAN, Diffusion-GAN).
- Experiments with image segmentation using encoder-decoder networks (U-Net).
- Practical use of transformer networks for object analysis in images.
- Analysis of objects in IR and depth images (RealSense, Kinect).
- Basic concepts of object detection in images; Haar-like features (AdaBoost, Viola-Jones); Local Binary Patterns (LBP); Histograms of Oriented Gradients (HOG). Methods for pedestrian detection in images; methods for face detection in images. Keypoint detectors and descriptors (e.g. SIFT, SURF).
- Convolutional neural networks (basic principles, description of layers). Modern variants of convolutional neural networks (e.g. GoogLeNet, ResNet, EfficientNet).
- Description of convolutional networks for object localization (e.g. R-CNN, Faster R-CNN, YOLO, SSD).
- Description of generative networks (e.g. DCGAN, Diffusion-GAN).
- Transformer networks (especially Vision Transformer - ViT) and their use in image analysis.
- Convolutional neural networks for image segmentation (encoder-decoder networks, U-Net).
- Human pose estimation using deep learning.
- Optical systems in the area of self-driving vehicles, IR image processing, LiDAR data 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.
- Object analysis using Haar-like features, Local Binary Patterns (LBP), and Histograms of Oriented Gradients (HOG).
- Experiments with convolutional neural networks, exploring the parameters of convolutional networks.
- Application of different types of convolutional networks (e.g. GoogLeNet, ResNet, EfficientNet), comparison of detectors.
- Practical use of localization methods based on convolutional neural networks (e.g. R-CNN, Faster R-CNN, YOLO).
- Practical use of generative networks for data augmentation (DCGAN, Diffusion-GAN).
- Experiments with image segmentation using encoder-decoder networks (U-Net).
- Practical use of transformer networks for object analysis in images.
- Analysis of objects in IR and depth images (RealSense, Kinect).