Center of Gravity Coordinates Estimation Based on an Overall Brightness Average Determined from the 3D Vision System

In advanced manufacturing technologies (including complex automated processes) and their branches of industry, perception and evaluation of the object parameters are the most critical factors. Many production machines and workplaces are currently equipped as standard with high-quality special sensing devices based on vision systems to detect these parameters. This article focuses on designing a reachable and fully functional vision system based on two standard CCD cameras usage, while the emphasis is on the RS 232C communication interface between two sites (vision and robotic systems). To this, we combine principles of the 1D photogrammetric calibration method from two known points at a stable point field and the available packages inside the processing unit of the vision system (as filtering, enhancing and extracting edges, weak and robust smoothing, etc.). A correlation factor at camera system (for reliable recognition of the sensed object) was set from 84 to 100%. Then, the pilot communication between both systems was proposed and then tested through CREAD/CWRITE commands according to protocol 3964R (used for the data transfer). Moreover, the system was proven by successful transition of the data into the robotic system. Since research gaps in this field still exist and many vision systems are based on PC processing or intelligent cameras, our potential research topic tries to provide the price–performance ratio solution for those who cannot regularly invest in the newest vision technology; however, they could still do so to stay competitive.

The image description reduction in the set of descriptors on informativeness metric criteria base

The subject of the research is the methods of image classification on a set of key point descriptors in computer vision systems. The goal is to improve the performance of structural classification methods by introducing indexed hash structures on the set of the dataset reference images descriptors and a consistent chain combination of several stages of data analysis in the classification process. Applied methods: BRISK detector and descriptors, data hashing tools, search methods in large data arrays, metric models for the vector relevance estimation, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using indexed hash structures, that speeds up the calculation dozens of times; the gain in computing time increases with an increase of the number of reference images and descriptors in descriptions; the peculiarity of the classifier is that not an exact search is performed, but taking into account the permissible deviation of data from the reference; experimentally verified the effectiveness of the classification, which indicates the efficiency and effectiveness of the proposed method. The practical significance of the work is the construction of classification models in the transformed space of the hash data representation, the efficiency confirmation of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems.

Special Issue on Field Robotics with Vision Systems

Field robotics has been undergoing rapid progress in recent years. It addresses a wide range of activities performed in outdoor environments, and its applications are being developed in areas where it was previously considered difficult to apply. This rapid progress is largely supported by AI-based improvements in computer vision systems with monocular cameras, stereo cameras, RGB-D cameras, LiDAR systems, and/or other sensors. Field robotics is impelled by an application-driven approach by its nature, and it contributes to the resolution of social problems and the creation of new innovations, including autonomous driving to reduce casualties, autonomous working machines/robots to resolve the problems of labor shortages or dangers, disaster-response robots to aid rescue parties, various kinds of aerial robots to do searches or make deliveries, underwater robots to perform search missions, etc. In this special issue on “Field Robotics with Vision Systems,” we highlight sixteen interesting papers, including one review paper, fourteen research papers, and one development report. They cover various application areas, ranging from underwater to space environments, and they propose interesting integration methods or element technologies to use in outdoor environments where vision systems and robot systems have great difficulty performing robustly. We thank all authors and reviewers, and we hope that this special issue contributes to future research and development in area of field robotics, which promises new innovations.

Internet of Things IoT Edge Computer Vision Systems on Drilling Rigs

This study focuses on the design and infrastructure development of Internet-of-Things (IoT) edge platforms on drilling rigs and the testing of pilot IoT-Edge Computer Vision Systems (ECVS) for the optimization of drilling processes. The pilot technology presented in this study, Well Control Space Out System (WC-SOS), reduces the risks associated with hydrocarbon release during drilling by significantly increasing the success and time response for shut-in a well. Current shut-in methods that require manual steps are prone to errors and may take minutes to perform, which is enough time for an irreversible escalation in the well control incident. Consequently, the WC-SOS enables the drilling rig crew to shut-in a well in seconds. The IoT-ECVS deployed for the WC-SOS can be seamlessly expanded to analyze drillstring dynamics and drilling fluid cuttings/solids/flow analysis at the shale shakers in real-time. When IoT-ECVSs communicate with each other, their value is multiplied, which makes interoperability essential for maximizing benefits in drilling operations.

Augmented Reality in Ophthalmology: Applications and Challenges

Augmented reality (AR) has been developed rapidly and implemented in many fields such as medicine, maintenance, and cultural heritage. Unlike other specialties, ophthalmology connects closely with AR since most AR systems are based on vision systems. Here we summarize the applications and challenges of AR in ophthalmology and provide insights for further research. Firstly, we illustrate the structure of the standard AR system and present essential hardware. Secondly, we systematically introduce applications of AR in ophthalmology, including therapy, education, and clinical assistance. To conclude, there is still a large room for development, which needs researchers to pay more effort. Applications in diagnosis and protection might be worth exploring. Although the obstacles of hardware restrict the development of AR in ophthalmology at present, the AR will realize its potential and play an important role in ophthalmology in the future with the rapidly developing technology and more in-depth research.