A Square Equal-Area Map Projection with Low Angular Distortion, Minimal Cusps, and Closed-Form Solutions

A novel square equal-area map projection is proposed. The projection combines closed-form forward and inverse solutions with relatively low angular distortion and minimal cusps, a combination of properties not manifested by any previously published square equal-area projection. Thus, the new projection has lower angular distortion than any previously published square equal-area projection with a closed-form solution. Utilizing a quincuncial arrangement, the new projection places the north pole at the center of the square and divides the south pole between its four corners; the projection can be seamlessly tiled. The existence of closed-form solutions makes the projection suitable for real-time visualization applications, both in cartography and in other areas, such as for the display of panoramic images.

Comparativa de características de software para la creación de recorridos virtuales 360 en Web

The use of 360 virtual tours has become a technology that allows viewing from different angles, places, or spaces that, due to various circumstances, it is not possible to visit physically. Due to different restrictions existing today; of a physical, economic or health type, virtual visits to museums, archaeological sites, parks, and facilities of various kinds have become an alternative to disseminate, promote, or bring users closer to places, that through 360 panoramic images, make them feel immersed in them. Currently, creating a 360 virtual tour is available to more people, as the software and hardware tools used are increasingly accessible. However, deciding on the most appropriate software platform to publish virtual tours on the web can be a trial-and-error process, delaying obtaining these virtual products.

Radiographic Detection of the Relationship Between Tonsilloliths and Dental Plaque-Related Pathologies in a Series of Digital Panoramic Radiographs

Objective: The purpose of this study was to analyse the prevalence of tonsilloliths and to determine radiographically whether there is a relationship between tonsilloliths and dental plaque-related pathologies in a series of digital panoramic radiographs. Materials and Methods: This retrospective study included digital panoramic radiographs of 859 patients admitted for a routine dental examination. The panoramic images were examined for both the presence of tonsilloliths and the number of decayed, missing, restored tooth and apical periodontitis. Periodontal bone loss was also measured in thirds of optimal bone height according to the root length and finally a percentage of bone loss was obtained for each panoramic radiograph evaluated. Results: Tonsilloliths were observed in 141 (16.4%) of all individuals. While there was no significant difference regarding the number of decayed teeth and restored teeth between tonsillolith cases (TT) and cases without tonsillolith (TC), the number of missing teeth and apical periodontitis in TT was significantly higher than TC (p: 0.004, p: 0.030, respectively). There was a significant difference between the groups in terms of the mean percentage of bone loss (p: 0.001; p< 0.05). In addition, cases showing bone loss between one-third and two-thirds of the optimal bone height in the TT group (52.5%) were significantly higher than those in TC (45.5%) (p: 0.035; p< 0.05). Conclusion: The relationship between dental plaque-related pathologies and tonsilloliths, observed in this retrospective study, should be confirmed by Computed Tomography studies and randomized, prospective, clinical trials conducted in a multidisciplinary manner.

A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images

Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions.

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.

Prediction of eruption of the lower third molars depending on their position relative to the “stress axis”

The article discusses the important problem of the presence and location of the primordia of third molars and the possibility of their eruption without affecting the dentition. The problem is relevant and is being studied by domestic and foreign specialists from different angles with the use of various diagnostic techniques. This article is based on the analysis of the most accessible and most frequently used X-ray diagnostic method – the panoramic X-ray.The aim was to study the prediction of eruption of the lower third molars depending on their position relative to the “stress axis”.84 panoramic images of the patients aged 12‒25 years were analyzed. To study the OPTG, the method of Fishchev et al. (2012), which determines the position of the lower third molars relative to the “stress axis”, and Demirjian’s modified method (1973), which determines the stage of their formation, were used. The data were processed statistically.The results are presented in tables and diagrams, as well as in correlations. Their interpretation is discussed. These data allowed us to draw conclusions about a significant change only in the angle of inclination and the stages of formation of the roots of the third molars of the lower jaw, about the absence of changes in the gonial angle and the position of the studied teeth with age. At the same time, it was found that the location of the coronal part of the third molars below the “stress axis” does not worsen the prediction of their eruption.

Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments

Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user’s requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to feed the motion cueing algorithms. For this reason, this paper aims to present a new method for generating non-real-time gravito-inertial cues with motion platforms using a system fed both with computer-generated—simulation-based—images and video imagery. It is a hybrid method where part of the gravito-inertial cues—those with acceleration information—are generated using a classical approach through the application of physical modeling in a VR scene utilizing washout filters, and part of the gravito-inertial cues—the ones coming from recorded images and video, without acceleration information—were generated ad hoc in a semi-manual way. The resulting motion cues generated were further modified according to the contributions of different experts based on a successive approximation—Wideband Delphi-inspired—method. The subjective evaluation of the proposed method showed that the motion signals refined with this method were significantly better than the original non-refined ones in terms of user perception. The final system, developed as part of an international road safety education campaign, could be useful for developing further VR-based applications for key fields such as driving assistance, vehicle automation and road crash prevention.

Automatic Registration of Mobile Mapping System Lidar Points and Panoramic-Image Sequences by Relative Orientation Model

To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMSlidar points and panoramic-image sequence. The results show that three types of MMSdata sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods.