Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

The most important component of photorealism in Computer Graphics is given by a physically correct approximation of the light transport. Besides the direct illumination from light sources, there is an indirect illumination, produced by the reflections of the light rays on other surfaces of the scene. In Computer Graphics, the process of computing the illumination of a surface by considering both the direct and the indirect illumination is widely known as global illumination. This paper describes several classes of real-time global illumination techniques used in current game engines together with our own implementations of these approaches. All implementations were made in our own framework, specially designed with a multi-pass rendering architecture that allows fast implementation of rendering techniques and the reuse of functionalities. We analyze these classes based on the following criteria: the visual results produced by the indirect diffuse lighting, the ability to produce glossy reflections, shadows, ambient occlusion, subsurface scattering, translucency and volumetric lighting as well as the ability to simulate area lights. We present the quantitative results of our implementations, obtained with the same external parameters for all techniques, thanks to the unified implementations in the same framework. An important observation is that our analysis is focused on the techniques that are based on the rasterization pipeline, thus, the comparison does not include the techniques designed entirely for the ray-tracing pipeline.

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

This paper examines the effectiveness of virtual reality (VR) in training public speaking skills. The fear of public speaking (FPS) is a common problem that can have a significant impact on an individual’s professional and personal life. Traditional therapies for public speaking anxiety have been shown to be effective, but there is growing interest in the use of VR as an alternative or supplement to these therapies. This study aims to investigate the VR medium in improving public speaking skills and to explore the potential mechanisms underlying this effect. A framework was developed with the aim to investigate the possibility of improving public speaking skills through VR. Key features of this framework include the ability to adjust the audience size and alter the dimensions of the room. Additionally, it allows for the modification of initial audience behaviors. One of the innovative aspects is the inclusion of an evolving attention span in the virtual audience, adding a dynamic element to the VR experience. The framework excels in tracking various metrics in real time and has the audience react dynamically based on them. These metrics include movement and voice parameters. The system is designed to present this data as immediate feedback to the user, but also as a summary after a presentation has concluded. After an extensive two-phased testing, the results are discussed. These findings suggest that VR can be an effective means for improving public speaking skills and potentially helping in alleviating fear of public speaking.