## 2 Summary of the Contributions
### Computer Vision  
Image processing and analysis are a basis of computer vision problems such as semantic segmentation, object classification, localization, and detection, optical character recognition, facial recognition etc. An appropriate representation of image content is a crucial problem. In [1], to deal with this problem, a novel type of representation is proposed where an image is reduced to a set of highly sparse matrices representing detected keypoints. The authors express intensity of features extracted from a dedicated convolutional neural network (CNN) autoencoder. The new features have many advantages such as they are not manually designed but learned, they are expected to minimize information loss and they are relatively easy to interpret.  
In [2], a fast-self-adaptive digital camouflage method based on deep learning is proposed. It is designed for the new generation of adaptive optical camouflage which can change with the environment in real-time. The system is composed of a YOLOv3 model that identifies military targets, a pre-trained deepfillv1 model that designs the preliminary camouflage texture, and a k-means algorithm for standardization of the texture.  
The experimental results show that the camouflage pattern designed by the proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance.  
A problem of classification of remote sensing images for disaster investigation, traffic control, and land-use resource management is considered in [3]. A new remote sensing scene classification network is proposed and a two-stage cyclical learning is developed to speed up model training and enhance accuracy. A t-distributed stochastic neighbor embedding algorithm was used to verify the effectiveness of the proposed model, and a local interpretable model-agnostic explanation algorithm was applied to improve the results.  
In [4], tracking pedestrian workers on construction sites is considered to improve efficiency and safety management. Vision-based tracking approaches, suitable in this case, require a large amount of data originating from construction sites. These data are hardly available, so the authors propose to use a small general dataset and combine a deep learning detector with an approach based on classical ML techniques. They use YOLOv3 detector for identifying workers and compare its performance with an approach based on a soft cascaded classifier. They found that both approaches generally yield satisfying tracking performances but feature different characteristics. To augment a self-recorded real world dataset for learning the vision-based tracking system, in [5] virtual construction site scenarios are modeled using 3D computer graphics software. The detector's performance is examined when using synthetic data of various environmental conditions for training. The findings showed that a synthetic extension is beneficial for otherwise small datasets. It is an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.  
In [6], a problem of environment classification for unmanned aerial vehicles (UAV) is addressed. Images obtained from video and photographic cameras mounted on a UAV are recognized to detect ground, sky, and clouds. The proposed recognition system includes CNN trained with a dataset generated by both, a human expert and a Support Vector Machine (SVM) to capture context and precise localization.