This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.
Currently, in virtual reality simulations and laboratories much attention is focused on the development of a user interaction controller for better immersion and student experience. Of course, the visual experience is a base for the laboratory study of any students, but direct contact with the visualized environment is also important. This paper presents a virtual scalpel technique that simulates the experience of medical discipline student education using virtual reality (VR) and augmented reality (AR) laboratory software. User's hands and a pencil with a tracker marker are the main tracking system components. Due to the high sensitivity of hand sign recognition, the Leap Motion camera was chosen as the base device for tracking the interaction of the user's virtual scalpel and hand models. We used Unreal Engine to create and visualize such virtual laboratory. During the recognition "hand-pencil" system, the 3D scalpel model is activated in our VR and scene, with a collision in the proposed virtual blade area. The user manipulates such “hand-scalpel” system in VR and AR simulation process, where the collision area of blade interacts with an imitating an organic 3D object. In this paper we presented the sensitivity and efficiency of “hand-pencil” system inside the virtual scene. In addition, the comparison and application of the methodology were highlighted for VR and AR prototypes of the laboratory scene.
The article aimed to provide a sort of new education process including virtual reality based application. At present, in accordance with the established ways of archaeological research, archaeologists are forced to transfer the found samples for long-term storage. In such notation, there is a challenging issue to create a virtual museum with deepening experience user interaction. The modern approaches of the virtual reality were implemented by applying technologies such as the Unreal Engine (UE) and Leap Motion (LM). In the paper, we give the scheme of the implemented development workflow. The ability of interaction with objects using the interface and hand gestures on LM on UE was given.
In this study, the object recognition problem was solved using support plane method. The modelled SAR images were used as features vectors in the recognition algorithm. Radar signal backscattering of objects in different observing poses is presented in SAR images. For real time simulation, we used simple mixture model of Lambertian–specular reflectivity. To this end, an algorithm of ray tracing is extended for simulating SAR images of 3D man-made models. The suggested algorithm of support plane is very effective in objects recognition using SAR images and RCS diagrams.
In the paper we have proposed recognition of object by RCS diagrams method. For modeling the scattering field of 3D objects on underlying surface we had use widely known FDTD method. We have used for distance function in developing method conjugation indices with so-called support plane, is formed within feature vectors of recognition class. We have given the results of recognition experiments with three different methods: support vector method, correlation method with the average class vector and a new support plane method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.