Integrated Architecture Model of Tourism Information Service in Smart Scenic Spots Based on Hybrid Cloud

Tourism industry can promote the prosperity of the local economy. In addition, the booming tourism business in recent years has also led to the development of related industries and services. According to high-tech technologies such as cloud computing, hybrid cloud, and panoramic image, we will plan to design a comprehensive and integrated architecture model of scenic spots that integrates all tourism information and various services, strive to create smart scenic spots, bring the best play experience to tourists, and strive to attract more attention and attention. The results show that (1) the overall average satisfactory approval of UI test is as high as 93%, which can meet the requirements. (2) After the improved panoramic image mosaic method, the correct rate is as high as 93.90%, the effect is 4.90% better than that before the improvement, and the algorithm is more efficient. (3) The scenic spot configuration management effect is excellent, and various situations in the scenic spot are effectively monitored. (4) The average response time per request of the original system is about 161 ms. Compared with the original system, the response time of the new system is reduced by about 57%, and the access success rate is 100%. The system in the experiment runs well and satisfies the integration of tourism information resources and services in scenic spots.

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.

Mapping of a multilayer panoramic radiography device

Objective: To map the shape, location, and thickness of the focal trough of a panoramic radiography device with a multilayer imaging program. Methods: An acrylic plate (148 × 148 × 3 mm) containing 1156 holes distributed in a matrix of 34 × 34 rows was placed in the OP300 Maxio at the levels of the maxilla and mandible. 20 metal spheres (3.5 mm in diameter) were placed on the holes of the plate under 15 different arrangements and panoramic images were acquired for each arrangement at 66 kV, 8 mA, and an exposure time of 16 s. The resulting panoramic radiographs from the five image layers were exported, the horizontal and vertical dimensions of the metal spheres were measured in all images using the Image J software, and the magnification and distortion rates of the spheres were calculated. All metal spheres presenting a magnification rate lower than 30% in both vertical and horizontal dimensions and a distortion rate lower than 10% were considered to map the focal troughs of each of the five image layers. Results: All panoramic image layers had a curved shape ranging from 39° to 51° for both dental arches and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to the anterior region of the mandible for all layers. Image layers are thicker at the level of the mandible than those at the level of the maxilla; also, inner layers were thinner and outer layers were thicker. Conclusion All image layers in the studied panoramic radiography device had a curved shape and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to that of the mandible for all layers.

Cosmetic “Gold Thread Therapy”: A Nuisance That Decreases the Diagnostic Quality of a Dental Panoramic Image

During routine imaging of the craniofacial region, it is recognised that some “cosmetic” procedures with metallic insertions can be revealed radiographically. These objects however make it difficult to obtain a good interpretation of anatomical structures for management of diseases. A 58-year-old female patient visited a private dental facility in Kuala Lumpur for prosthodontic replacement of missing teeth. The dental panoramic image revealed generalized bone loss and numerous unusual multiple thread-like radioopacities. These gold threads made radiographic evaluation difficult and complicated the process of treatment planning for dental implant placement advocated for this patient.

Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm

Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT algorithm to eliminate the feature points with small intensity value; When establishing the feature descriptor, the 128 dimension of the original algorithm is reduced to 64 dimensions to reduce the amount of calculation. In the feature point registration process, the feature descriptor is reduced to 32 dimensions, the feature point pairs are roughly extracted by the optimal node first BBF algorithm, and the feature point pairs are registered and screened by RANSAC; Finally, the image transformation matrix is obtained to realize panoramic image mosaic. The experimental results show that the proposed algorithm not only ensures the panoramic mosaic effect, but also extracts the feature points in 11% of the time of the traditional SIFT algorithm, and the feature point registration speed is 27.17% of the traditional SIFT algorithm.

The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study

Introduction: Cone-beam computed tomography (CBCT) has been applied to implant dentistry. The increasing use of this technology produces a critical number of images that can be used for training artificial intelligence (AI). Objectives: To investigate the learning curve of the developed AI for dental implant planning in the posterior maxillary region. Methods: A total of 184 CBCT image sets of patients receiving posterior maxillary implants were processed with software (DentiPlan Pro version 3.7; NECTEC, NSTDA, Thailand) to acquire 316 implant position images. The planning software image interfaces were anonymously captured with full-screen resolution. Three hundred images were randomly sorted to create six data sets, including 1–50, 1–100, 1–150, 1–200, 1–250, and 1–300. The data sets were used to develop AI for dental implant planning through the IBM PowerAI Vision platform (IBM Thailand Co., Ltd., Bangkok, Thailand) by using a faster R-CNN algorithm. Four data augmentation algorithms, including blur, sharpen, color, and noise, were also integrated to observe the improvement of the model. After the testing process with 16 images that were not included in the training set, the recorded data were analyzed for detection and accuracy to generate the learning curve of the model. Results: The learning curve revealed some similar patterns. The curve trend of the original and blurred augmented models was in a similar pattern in the panoramic image. In the last training set, the blurred augmented model improved the detection by 12.50%, but showed less accuracy than the original model by 18.34%, whereas the other three augmented models had different patterns. They were continuously increasing in both detection and accuracy. However, their detection dropped in the last training set. The colored augmented model demonstrated the best improvement with 40% for the panoramic image and 18.59% for the cross-sectional image. Conclusion: Within the limitation of the study, it may be concluded that the number of images used in AI development is positively related to the AI interpretation. The data augmentation techniques to improve the ability of AI are still questionable.

Developing the Smart Sorting Screw System Based on Deep Learning Approaches

The deep learning technique has turned into a mature technique. In addition, many researchers have applied deep learning methods to classify products into defective categories. However, due to the limitations of the devices, the images from factories cannot be trained and inferenced in real-time. As a result, the AI technology could not be widely implemented in actual factory inspections. In this study, the proposed smart sorting screw system combines the internet of things technique and an anomaly network for detecting the defective region of the screw product. The proposed system has three prominent characteristics. First, the spiral screw images are stitched into a panoramic image to comprehensively detect the defective region that appears on the screw surface. Second, the anomaly network comprising of convolutional autoencoder (CAE) and adversarial autoencoder (AAE) networks is utilized to automatically recognize the defective areas in the absence of a defective-free image for model training. Third, the IoT technique is employed to upload the screw image to the cloud platform for model training and inference, in order to determine if the defective screw product is a pass or fail on the production line. The experimental results show that the image stitching method can precisely merge the spiral screw image to the panoramic image. Among these two anomaly models, the AAE network obtained the best maximum IOU of 0.41 and a maximum dice coefficient score of 0.59. The proposed system has the ability to automatically detect a defective screw image, which is helpful in reducing the flow of the defective products in order to enhance product quality.

“Golden Valleys”: On the New Novel by Roman Senchin

The article presents one of the first responses to the literary novelty of Roman Senchin’s novel “Golden Valleys” (2021). The socio-psychological aspects of the problem, the images of the heroes, the panoramic image of the regional life of the country over the past decades are considered. Special attention is paid to the consciousness of the central character of the 20-year-old geologist student as penetrating analyst of personal, family and social experience.