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).
* 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).