Panoramic Images Research Articles

Gastrointestinal image stitching based on improved unsupervised algorithm.

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network's ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.

Spatio-temporal breather dynamics in microcomb soliton crystals

Solitons, the distinct balance between nonlinearity and dispersion, provide a route toward ultrafast electromagnetic pulse shaping, high-harmonic generation, real-time image processing, and RF photonic communications. Here we uniquely explore and observe the spatio-temporal breather dynamics of optical soliton crystals in frequency microcombs, examining spatial breathers, chaos transitions, and dynamical deterministic switching – in nonlinear measurements and theory. To understand the breather solitons, we describe their dynamical routes and two example transitional maps of the ensemble spatial breathers, with and without chaos initiation. We elucidate the physical mechanisms of the breather dynamics in the soliton crystal microcombs, in the interaction plane limit cycles and in the domain-wall understanding with parity symmetry breaking from third-order dispersion. We present maps of the accessible nonlinear regions, the breather frequency dependences on third-order dispersion and avoided-mode crossing strengths, and the transition between the collective breather spatio-temporal states. Our range of measurements matches well with our first-principles theory and nonlinear modeling. To image these soliton ensembles and their breathers, we further constructed panoramic temporal imaging for simultaneous fast- and slow-axis two-dimensional mapping of the breathers. In the phase-differential sampling, we present two-dimensional evolution maps of soliton crystal breathers, including with defects, in both stable breathers and breathers with drift. Our fundamental studies contribute to the understanding of nonlinear dynamics in soliton crystal complexes, their spatio-temporal dependences, and their stability-existence zones.

Pivot: Panoramic-image-based VR User Authentication against Side-Channel Attacks

With metaverse attracting increasing attention from both academic and industry, the application of virtual reality (VR) has extended beyond 3D immersive viewing/gaming to a broader range of areas, such as banking, shopping, tourism, education, etc., which involves a growing amount of sensitive and private user data into VR systems. However, with current password-based user authentication schemes in mainstream VR devices, studies demonstrate that side-channel attacks can pose a severe threat to VR user privacy. To mitigate the threat, we propose a novel Panoramic-image-based VR user authentication system, i.e., Pivot , to defend against such attacks, yet maintain high usability. Specifically, in Pivot , we design an image-random-pivoting-based user interaction mechanism to assist users in quickly and securely selecting memorable points of interest in a panoramic image. Then an image region segmentation algorithm is designed to automatically scatter the points to regions to form the customized graphic password for the user, which could ensure a sufficiently large password space and also reduce the near-region point misclicks. Afterward, the region indexes are used to generate the hashed password for authentication. Both theoretical security analysis and extensive user studies demonstrate that Pivot is secure and user-friendly in practice.

Ewing’s Sarcoma Presenting in the Paranasal Sinus – A Case Report

Abstract Rationale: The aim of this study is to raise awareness among dentists regarding a potential outcome associated with the rapid growth of facial swellings. Patient Concerns: We present a case of Ewing’s sarcoma (ES) in a 23-year-old male who presented with a rapidly progressive swelling in the left maxillary region over six weeks. Diagnosis: There was no significant intraoral dental finding other than a missing left upper first molar. Panoramic imaging initially suggested chronic sinusitis, but the swelling persisted despite treatment. A subsequent cone-beam computed tomography (CBCT) scan revealed a soft-tissue tumour affecting multiple sinuses, confirmed by an open biopsy as a small round cell tumour. Immunohistochemistry was used for precise diagnosis due to biopsy sample limitations and tumour variability. Treatment: The patient’s case was reviewed by our institution’s tumour board. One month after diagnosis, chemotherapy began with alternating drugs for a total of 6 cycles. Outcomes: A follow-up Positron Emission Tomography (PET)/Computed Tomography (CT) scan after two months of chemotherapy showed resolution of the hypermetabolic mass on the left side without evidence of metastatic disease. The patient is currently under regular follow-up. Take-Away Lessons: Dentists should be vigilant for rapidly expanding oral or facial swellings. Early and accurate diagnosis can improve clinical management and survival rates for ES patients.

Performance Evaluation of Deep Learning Models for Dental Caries Classification via Panoramic Radiograph Images

Objective: The purpose of this study is to evaluate the ability of deep learning models to classify mandibular molar teeth according to the presence and proximity of caries to the dental pulp. This research summarizes the progress of artificial intelligence and potential dental problems in diagnosis, treatment, and disease prediction in medicine. It discusses data limitations, computational power, ethical considerations, and their implications for dentists. This can lay the groundwork for future research in this rapidly expanding field. Methods: The dataset used in this study consists of 1200 panoramic radiographs, which have been evaluated and classified into three categories: free of dental caries, coded as (H); enamel-dentin caries lesions treated with restorative filling, coded as (R); and deep dental caries that underwent root canal treatment, coded as (E). The images are prepared for the training-testing process using the k-fold crossevaluation technique and then fed into the pre-trained deep learning models for classification. Results: The VGG-19 model achieved superior results compared to the other models, with macro-average scores of 0.9111 for precision, 0.9127 for recall, and 0.9115 for f1-score, respectively. Conclusion: The promising results obtained in this study give confidence in endorsing the use of deep learning models in the dental treatments sector.

Large-Scale Indoor Visual-Geometric Multimodal Dataset and Benchmark for Novel View Synthesis.

The accurate reconstruction of indoor environments is crucial for applications in augmented reality, virtual reality, and robotics. However, existing indoor datasets are often limited in scale, lack ground truth point clouds, and provide insufficient viewpoints, which impedes the development of robust novel view synthesis (NVS) techniques. To address these limitations, we introduce a new large-scale indoor dataset that features diverse and challenging scenes, including basements and long corridors. This dataset offers panoramic image sequences for comprehensive coverage, high-resolution point clouds, meshes, and textures as ground truth, and a novel benchmark specifically designed to evaluate NVS algorithms in complex indoor environments. Our dataset and benchmark aim to advance indoor scene reconstruction and facilitate the creation of more effective NVS solutions for real-world applications.

UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain, as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning. The conventional method of watermelon yield estimation relies heavily on manual labor, which is both time-consuming and labor-intensive. To address this, this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle (UAV) videos for detection and counting of watermelons. This pipeline uses You Only Look Once version 8 s (YOLOv8s) with panorama stitching and overlap partitioning, which facilitates the overall number estimation of watermelons in field. The watermelon detection model, based on YOLOv8s and obtained using transfer learning, achieved a detection accuracy of 99.20 %, demonstrating its potential for application in yield estimation. The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications compared with the video tracking based detection and counting method. The counting accuracy reached over 96.61 %, proving a promising application for yield estimation. The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.

Image stitching algorithm for super-resolution localization microscopy combined with fluorescence noise prior.

Super-resolution panoramic pathological imaging provides a powerful tool for biologists to observe the ultrastructure of samples. Localization data can maintain the essential ultrastructural information of biological samples with a small storage space, and also provides a new opportunity for stitching super-resolution images. However, the existing image stitching methods based on localization data cannot accurately calculate the registration offset of sample regions with no or few structural points and thus lead to registration errors. Here, we proposed a stitching framework called PNanoStitcher. The framework fully utilizes the distribution characteristics of the background fluorescence noise in the stitching region and solves the stitching failure in sample regions with no or few structural points. We verified our method using both simulated and experimental datasets, and compared it with existing stitching methods. PNanoStitcher achieved superior stitching results on biological samples with no structural and few structural regions. The study provides an important driving force for the development of super-resolution digital pathology.

Mapping building facades using panoramic images captured by Unmanned Aerial Vehicle (UAV)

This work aims to verify the building pathologies of DTIC’s building front (Diretoria de Tecnologia da Informação e Comunicação at Federal University of Pampa – Alegrete/RS, Brazil) by using an Unmanned Aerial Vehicle (UAV). The study was conducted before and six months after its maintenance. Images and inspection records were used to elaborate the Damage Maps and the Grid Method for pathologies quantifications. The comparison of the Damage Maps before and after maintenance has revealed that the intervention (paint) was not efficient, since it has disguised temporarily the anomalies without correcting them. The use of UAV proved to be a good tool for building inspections, providing effective diagnosis, time-saving and security for the operator without working at height.

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

Identifying fine-scale archaeological features using KH-9 HEXAGON mapping and panoramic camera images: evidence from Liangzhu Ancient City

ABSTRACT Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications.

Regiomontanus angle maximization for catadioptric sensors with paraboloidal mirrors

Dioptric cameras with conventional perspective projection have well established analytical properties. However, they suffer from perspective distortions and only have a limited field of view. Catadioptric cameras offer panoramic imaging. Their extensive field of view together with projection specific image analysis, can simplify many computer vision tasks. Several properties of catadioptric projection for geometric primitives such as points and lines have been addressed and have also been used for calibration. However, higher order geometric properties are yet to be investigated. Such analysis is complicated by the specifics of the warping of the scene by catadioptric projection. One such property, that is the subject of this work, is the Regiomontanus angle maximization relative to the effective viewpoint of the sensor. This work considers catadioptric sensors with paraboloidal mirrors, that is, paracatadioptric sensors. Analytical ray tracing of a simplified 1D world object gives its projection in the image and an expression for its length. The optimization of the length of the projection results in a third degree equation for the Regiomontanus distance that can be solved explicitly. The Khayyam geometric solution of this equation provides the Regiomontanus distance of maximum subtended projection for these cameras. Applications of these results in various contexts are presented and discussed.

Design of hexapod robot equipped with omnidirectional vision sensor for defect inspection of pipeline’s inner surface

Abstract Defect detection of inner surface of precision pipes is a crucial aspect of ensuring production safety. Currently, pipeline defect detection primarily relies on recording video for manual recognition, with urgent need to improve automation, quantification and accuracy. This paper presents a hexapod in-pipe robot with carrying capacity designed to transport the omnidirectional vision sensor to specified location within unreachable pipelines. The feasibility of the robot’s mechanical design and sensor load-carrying module is analyzed using theory calculations, motion simulations and finite element method. To address the challenges of small pixel ratio and weak background changes in panoramic images, a tiny defect segmentor (TDS) based on ResNet is proposed for detecting tiny defects on the inner surface of pipelines. The hardware and software systems are implemented, and the motion performance of the pipeline robot is validated through experiments. The results demonstrate that the robot achieves stable movement at a speed of over 0.1m/s and can adapt to pipe diameter ranging from of 110 to 130 mm. The novelty of the robot lies in providing stable control of the loaded vision sensor, with control precision of the rotation angle and the displacement recorded at 1.84% and 0.87%, respectively. Furthermore, the proposed method achieves a detection accuracy of 95.67% for tiny defects with a diameter less than 3 mm and provides defect location information. This pipeline robot serves as an essential reference for development of in-pipe 3D vision inspection system.

Seamless Panoramic Image Stitching Based onInvariant Feature Detector and Image Blending

Seamless Panoramic Image Stitching Based onInvariant Feature Detector and Image Blending

A matching method of underwater panoramic image based on ORB-GMS

A matching method of underwater panoramic image based on ORB-GMS

A new method with C-means segmentation for non-uniform image coordinate system definition in panoramic imaging employing Ladybug2 camera

A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

Detection of the Fixed Prostheses on Panoramic Images: An Artificial Intelligence Based Study.

To investigate the effectiveness of using YOLO-v5x in detecting fixed prosthetic restoration in panoramic radiographs. Descriptive study. Place and Duration of the Study: Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkiye from November 2022 to April 2023. For the labelling of fixed prosthetic restorations, 8,000 panoramic radiographs were evaluated using the YOLO-v5x architecture. In creating the dataset for this study, fixed prosthetic restorations were categorised as dental implant, pontic, crown, and implant-supported crown on dental panoramic radiographs. The labelled images were then randomly split into three groups: 80% for training, 10% for validation, and 10% for testing. The labelled panoramic images constituted the model's training dataset, and leveraging the knowledge acquired during this learning stage, the model generated predictions in the testing phase. The majority of labelling data were dedicated to crown restorations. The precision and sensitivity values of YOLOv5x were 0.99 and 0.98 for crowns, 0.98 and 0.99 for implants, 0.99 and 0.99 for pontics, and 0.99 and 0.99 for implant-supported crowns, respectively. The results obtained in this study demonstrate a satisfactory success rate of YOLO-v5x in detecting dental prosthetic restorations. The high precision and sensitivity of the model indicate its strong potential to enhance clinical professional performance and contribute to the development of more efficient dental health services. Artificial intelligence, Dentistry, Dental prosthesis, Panoramic radiography.

Reliability of Ultrasound Assessment of Hamstring Morphology, Quality, and Stiffness Among Healthy Adults and Athletes: A Systematic Review.

The incidence and recurrence rate of hamstring strain injuries remain persistently high, with recurrent injuries leading to increased time lost during play and extended recovery periods compared with initial injury. Ultrasound imaging assesses important factors such as hamstring fascicle length (FL), pennation angle (PA), cross-sectional area (CSA), muscle thickness (MT), echo intensity (EI), and shear wave elastography (SWE), all impacting athletic performance. However, its reliability must be established before employing any measurement tool in research or clinical settings. To determine the reliability and measurement error of ultrasound for assessing hamstring FL, PA, CSA, MT, EI, and SWE among healthy adults and athletes; to synthesize the information regarding the operationalization of ultrasound. A systematic literature search was done from January 1990 to February 5, 2023, to identify reliability and validity studies of hamstring ultrasound assessment published in peer-reviewed journals with identifiable methodology of outcome measures. Intraclass correlation coefficient measurement of 14 included studies reported moderate to excellent intrarater, interrater, and test-retest reliabilities of FL, PA, and MT regardless of the site of muscle testing, probe size, and setting, state of muscle, and use of different techniques in the extrapolation of FL. Good to excellent test-retest reliability rates for all hamstring anatomic CSA along midmuscle and different percentages of thigh length using panoramic imaging. Good intrarater reliability of EI regardless of gender and orientation of the probe but with excellent intrarater reliability in transverse scan using maximum region of interest. Good intrarater, interrater, and interday repeatability on SWE with the muscle in a stretched position. Evidence from studies with a predominantly low risk of bias shows that ultrasound is a reliable tool to measure hamstring FL, PA, CSA, MT, EI, and SWE in healthy adults and athletes under various experimental conditions.

Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.

Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.

Visualisation of dental anatomy in CBCT scans (Part 2).

To the trained eye, every tooth is different. An in-depth understanding of dental anatomy in cross-sectional images is a fundamental basic skill required when interpreting cone beam computed tomography (CBCT) scans. The conventional orthogonal planes of axial, coronal, and sagittal are not always the best planes in which to assess teeth because every tooth is at a slightly different angle to all these planes. In this chapter, the author demonstrates the characteristic anatomy of upper and lower incisors, canines, premolars, and molars in longitudinal and transverse sections, using carefully uprighted images. Some of these teeth are also shown in transverse section at various levels in the crown and root. The chronology of permanent teeth is shown. Developing teeth are discussed and incremental growth lines are mentioned.The author describes three well-known dental notation systems (Palmer, FDI, hybrid).There are four videos accompanying this chapter, including one fascinating video showing a scroll-through of all the cross-arch sections of a synthetic panoramic (curved multiplanar reconstruction) image that includes all the upper and lower teeth in a 16-year-old.