Lectures:
* Basic concepts of object detection in images. Methods for face detection in images. Haar features (AdaBoost, Viola-Jones). Local Binary Patterns (LBP), Histograms of Oriented Gradients (HOG) and their use for object analysis. Methods for pedestrian 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. VGGNet, GoogLeNet, ResNet).
* 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).
* 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).
* 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 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. Methods for face detection in images. Haar features (AdaBoost, Viola-Jones). Local Binary Patterns (LBP), Histograms of Oriented Gradients (HOG) and their use for object analysis. Methods for pedestrian 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. VGGNet, GoogLeNet, ResNet).
* 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).
* 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).
* 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 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).