Search Results (153)

Nephrotic syndrome (NS) is characterized by proteinuria, hypoalbuminemia, edema, and hyperlipidemia. Apart from the traditional causes of NS, such as minimal change disease, focal segmental glomerulosclerosis, diabetes, infections, malignancies, autoimmune conditions, and nephrotoxic agents, there are also rare causes of NS, whose knowledge is of the utmost importance. The aim of this article was to highlight the less well-known causes that have a significant impact on diagnosis and treatment. Genetic syndromes such as Schimke immuno-osseous dysplasia, familial lecithin-cholesterol acyltransferase deficiency with two clinical variants (fish-eye Disease and the p.Leu364Pro mutation), lead to NS through mechanisms involving podocyte and lipid metabolism dysfunction. Congenital disorders of glycosylation and Nail–Patella Syndrome emphasize the role of deranged protein processing and transcriptional regulation in glomerular injury. The link of NS with type 1 diabetes, though rare, suggests an etiology on the basis of common HLA loci and immune dysregulation. Histopathological analysis, particularly electron microscopy, shows mainly podocyte damage, mesangial sclerosis, and alteration of the basement membrane, which aids in differentiating rare forms. Prompt recognition of these novel etiologies by genetic analysis, renal biopsy, and an interdisciplinary panel is essential to avoid delays in diagnosis and tailored treatment. Full article

Wide-angle images often suffer from severe radial distortion, compromising geometric accuracy and challenging image correction and real-time stitching, especially in resource-constrained embedded environments. To address this, this study proposes a wide-angle image correction and stitching framework based on a Swin Transformer, optimized for lightweight deployment on edge devices. The model integrates multi-scale feature extraction, Thin Plate Spline (TPS) control point prediction, and optical flow-guided constraints, balancing correction accuracy and computational efficiency. Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. To validate practical applicability, the model is deployed on a Jetson TX2 NX device, and a real-time dual-camera stitching system is built using C++ and DeepStream. The system achieves 15 FPS at 1400 × 1400 resolution, with a correction latency of 56 ms and stitching latency of 15 ms, demonstrating efficient hardware utilization and stable performance. This study presents a deployable, scalable, and edge-compatible solution for wide-angle image correction and real-time stitching, offering practical value for applications such as smart surveillance, autonomous driving, and industrial inspection. Full article

Aquatic floating debris detection is a key technological foundation for ecological monitoring and integrated water environment management. It holds substantial scientific and practical value in applications such as pollution source tracing, floating debris control, and maritime navigation safety. However, this field faces ongoing challenges due to water surface polarization. Reflections of polarized light produce intense glare, resulting in localized overexposure, detail loss, and geometric distortion in captured images. These optical artifacts severely impair the performance of conventional detection algorithms, increasing both false positives and missed detections. To overcome these imaging challenges in complex aquatic environments, we propose a novel YOLOv8-based detection framework with integrated polarized light suppression mechanisms. The framework consists of four key components: a fisheye distortion correction module, a polarization feature processing layer, a customized residual network with Squeeze-and-Excitation (SE) attention, and a cascaded pipeline for super-resolution reconstruction and deblurring. Additionally, we developed the PSF-IMG dataset (Polarized Surface Floats), which includes common floating debris types such as plastic bottles, bags, and foam boards. Extensive experiments demonstrate the network’s robustness in suppressing polarization artifacts and enhancing feature stability under dynamic optical conditions. Full article

Object detection is a mature problem in autonomous driving, with pedestrian detection being one of the first commercially deployed algorithms. It has been extensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near-field sensing. The standard bounding-box representation fails in fisheye cameras due to heavy radial distortion, particularly in the periphery. In this paper, a generic object detection framework is implemented using the base YOLO (You Only Look Once) detector to systematically explore various object representations using the public WoodScape dataset. First, we implement basic representations, namely the standard bounding box, the oriented bounding box, and the ellipse. Secondly, we implement a generic polygon and propose a novel curvature-adaptive polygon, which obtains an improvement of 3 mAP (mean average precision) points. A polygon is expensive to annotate and complex to use in downstream tasks; thus, it is not practical to use it in real-world applications. However, we utilize it to demonstrate that the accuracy gap between the polygon and the bounding box representation is very high due to strong distortion in fisheye cameras. This motivates the design of a distortion-aware optimal representation of the bounding box for fisheye images, which tend to be banana-shaped near the periphery. We derive a novel representation called a curved box and improve it further by leveraging vanishing-point constraints. The proposed curved box representations outperform the bounding box by 3 mAP points and the oriented bounding box by 1.6 mAP points. In addition, the camera geometry tensor is formulated to provide adaptation to non-linear fisheye camera distortion characteristics and improves the performance further by 1.4 mAP points. Full article

Product engagement detection in retail environments is critical for understanding customer preferences through nonverbal cues such as gaze and hand movements. This study presents a system leveraging a 360-degree top-view fisheye camera combined with two perspective cameras, the only sensors required for deployment, effectively capturing subtle interactions even under occlusion or distant camera setups. Unlike conventional image-based gaze estimation methods that are sensitive to background variations and require capturing a person’s full appearance, raising privacy concerns, our approach utilizes a novel Transformer-based encoder operating directly on 3D skeletal keypoints. This innovation significantly reduces privacy risks by avoiding personal appearance data and benefits from ongoing advancements in accurate skeleton estimation techniques. Experimental evaluation in a simulated retail environment demonstrates that our method effectively identifies critical gaze-object and hand-object interactions, reliably detecting customer engagement prior to product selection. Despite yielding slightly higher mean angular errors in gaze estimation compared to a recent image-based method, the Transformer-based model achieves comparable performance in gaze-object detection. Its robustness, generalizability, and inherent privacy preservation make it particularly suitable for deployment in practical retail scenarios such as convenience stores, supermarkets, and shopping malls, highlighting its superiority in real-world applicability. Full article

The evolution of photogrammetry has been significantly influenced by advancements in camera technology, particularly the emergence of spherical cameras. These devices offer extensive photographic coverage and are increasingly utilised in many photogrammetry applications due to their significant user-friendly configuration, especially in their low-cost versions. Despite their advantages, these cameras are subject to high image distortion. This necessitates specialised calibration solutions related to fisheye images, which represent the primary geometry of the raw files. This paper evaluates fisheye calibration processes for the effective utilisation of low-cost spherical cameras, for the purpose of 3D reconstruction and the verification of geometric stability. Calibration optical parameters include focal length, pixel positions, and distortion coefficients. Emphasis was placed on the evaluation of solutions for camera calibration, calibration network design, and the assessment of software or toolboxes that support the correspondent geometry and calibration for processing. The efficiency in accuracy, correctness, computational time, and stability parameters was assessed with the influence of calibration parameters based on the accuracy of the 3D reconstruction. The assessment was conducted using a previous case study of graffiti on an underpass in Wiesbaden, Germany. The robust calibration solution is a two-step calibration process, including a pre-calibration stage and the consideration of the best possible network design. Fisheye undistortion was performed using OpenCV, and finally, calibration parameters were optimized with self-calibration through bundle adjustment to achieve both calibration parameters and 3D reconstruction using Agisoft Metashape software. In comparison to 3D calibration, self-calibration, and a pre-calibration strategy, the two-step calibration process has demonstrated an average improvement of 2826 points in the 3D sparse point cloud and a 0.22 m decrease in the re-projection error value derived from the front lens images of two individual spherical cameras. The accuracy and correctness of the 3D point cloud and the statistical analysis of parameters in the two-step calibration solution are presented as a result of the quality assessment of this paper and in comparison with the 3D point cloud produced by a laser scanner. Full article

The diameter at breast height (DBH) is a fundamental index used to characterize trees and establish forest inventories. The conventional method of measuring the DBH involves using steel tape meters, rope, and calipers. Alternatively, this study has shown that it can be calculated automatically using image-based algorithms, thus reducing time and effort while remaining cost-effective. The method consists of three main steps: image acquisition using a fisheye lens, 3D point cloud generation using structure-from-motion (SfM)-based image processing, and improved DBH estimation. The results indicate that this proposed methodology is comparable to traditional urban forest DBH measurements, with a root-mean-square error ranging from 0.7 to 2.4 cm. The proposed approach has been evaluated using real-world data, and it has been determined that the F-score assessment metric achieves a maximum of 0.91 in a university garden comprising 74 trees. The successful automated DBH measurements through SfM combined with fisheye lenses demonstrate the potential to improve urban tree inventories. Full article

LiDAR and camera systems are frequently used together to gain a more complete understanding of the environment in different fields, such as mobile robotics, autonomous driving, or intelligent surveillance. Accurately calibrating the extrinsic parameters is crucial for the accurate fusion of the data captured by both systems, which is equivalent to finding the transformation between the reference systems of both sensors. Traditional calibration methods for LiDAR and camera systems are developed for pinhole cameras and are not directly applicable to fisheye cameras. This work proposes a target-based calibration method for LiDAR and fisheye camera systems that avoids the need to transform images to a pinhole camera model, reducing the computation time. Instead, the method uses the spherical projection of the image, obtained with the intrinsic calibration parameters and the corresponding point cloud for LiDAR–fisheye calibration. Thus, unlike a pinhole-camera-based system, a wider field of view is provided, adding more information, which will lead to a better understanding of the environment itself, as well as enabling using fewer image sensors to cover a wider area. Full article

Despite the widespread use of street view imagery for Green View Index (GVI) analyses, variations in sampling methodologies across studies and the potential impact of these differences on the results, including associated errors, remain largely unexplored. This study aims to investigate the effectiveness of various GVI calculation methods, with a focus on analyzing the impact of sampling point selection and coverage angles on GVI results. Through a systematic review of the extensive relevant literature, we synthesized six predominant sampling methods: the four-quadrant view method, six-quadrant view method, eighteen-quadrant view method, panoramic view method, fisheye view method and pedestrian view method. We further evaluated the strengths and weaknesses of each approach, along with their applicability across different research domains. In addition, to address the limitations of existing methods in specific contexts, we developed a novel sampling technique based on three 120° street view images and experimentally validated its feasibility and accuracy. The results demonstrate the method’s high reliability, making it a valuable tool for acquiring and analyzing street view images. Our findings demonstrate that the choice of sampling method significantly influences GVI calculations, underscoring the necessity for researchers to select the optimal approach based on a specific research context. To mitigate errors arising from initial sampling angles, this study introduces a novel concept, the “Green View Circle”, which enhances the precision and applicability of calculations through the meticulous segmentation of observational angles, particularly in complex urban environments. Full article

Thermal comfort studies are paramount in enhancing future urban living conditions, and hemispherical photography has emerged as a widely employed field measurement technique in outdoor thermal comfort research. This comprehensive review systematically analyzed 142 outdoor thermal comfort studies conducted over the past decade using hemispherical photography methods, revealing that its primary application lies in objectively describing environmental information and constructing associated indices. In contrast, the number of studies focusing on subjectively assessing environmental factors remains relatively low; however, it is rapidly increasing due to its demonstrated effectiveness and convenience compared to other methodologies within this domain. Overall, despite certain limitations, such as higher labor costs and limited temporal/spatial coverage when describing environmental information, hemispherical photography still retains its advantage of providing accurate data acquisition for outdoor thermal comfort research. In recent years, advancements in mobile measurement tools and techniques have enhanced the richness and versatility of acquired information while leveraging the image specificity inherent to hemispherical photography, which continues to play a pivotal role in subjective assessments related to human perception of outdoor thermal comfort. Full article

Costs are increasing due to the addition of alloying elements such as V, W, and Mo to prevent damage to Cr-Mo steel for fastening bolts, but field tests have shown that it is not an appropriate solution for improving physical properties through heat treatment. In this study, the characteristics of fatigue cracks using Cr-Mo steel for fastening bolts before and after UNSM (ultrasonic nanocrystal surface modification) treatment were studied using fracture mechanics and fracture analysis methods. Specifically, using untreated and UNSM-treated materials: (1) the characteristics of small surface fatigue cracks existing on the surface, and (2) the surface fatigue cracks in the depth direction were observed and analyzed. The microstructure of Cr-Mo steel was refined by severe plastic deformation (SPD) from the surface to a depth of about 100 μm according to the static load of UNSM, and the fatigue limit increased by 30% as a large compressive residual stress was formed. Additionally, like the untreated materials, fisheye cracks did not occur in UNSM-treated materials, even when inclusions were present, and all specimens fractured while forming surface cracks. Accordingly, one or more of the multiple small surface fatigue cracks (MSFC) grew and developed into a major crack that determines the fatigue life, and a major ridge was formed among the many micro-ridges in the internal direction. In other words, this major crack grew and developed a major ridge in the internal direction, determining the lifespan of the test specimen. Full article

Due to recent advances in 3D reconstruction from RGB images, it is now possible to create photorealistic representations of real-world scenes that only require minutes to be reconstructed and can be rendered in real time. In particular, 3D Gaussian splatting shows promising results, outperforming preceding reconstruction methods while simultaneously reducing the overall computational requirements. The main success of 3D Gaussian splatting relies on the efficient use of a differentiable rasterizer to render the Gaussian scene representation. One major drawback of this method is its underlying pinhole camera model. In this paper, we propose an extension of the existing method that removes this constraint and enables scene reconstructions using arbitrary camera optics such as highly distorting fisheye lenses. Our method achieves this by applying a differentiable warping function to the Gaussian scene representation. Additionally, we reduce overfitting in outdoor scenes by utilizing a learnable skybox, reducing the presence of floating artifacts within the reconstructed scene. Based on synthetic and real-world image datasets, we show that our method is capable of creating an accurate scene reconstruction from highly distorted images and rendering photorealistic images from such reconstructions. Full article

In this paper, we propose a novel approach for egocentric 3D human pose estimation using fisheye images captured by a head-mounted display (HMD). Most studies on 3D pose estimation focused on heatmap regression and lifting 2D information to 3D space. This paper addresses the issue of depth ambiguity with highly distorted 2D fisheye images by proposing the SegDepth module, which jointly regresses segmentation and depth maps from the image. The SegDepth module distinguishes the human silhouette, which is directly related to pose estimation through segmentation, and simultaneously estimates depth to resolve the depth ambiguity. The extracted segmentation and depth information are transformed into embeddings and used for 3D joint estimation. In the evaluation, the SegDepth module improves the performance of existing methods, demonstrating its effectiveness and general applicability in improving 3D pose estimation. This suggests that the SegDepth module can be integrated into well-established methods such as Mo2Cap2 and xR-EgoPose to improve 3D pose estimation and provide a general performance improvement. Full article

It has been reported that the multiphase structure with martensite and a mixed structure obtained by the quenching and tempering of bearing steel shows high strength and ductility. However, there appears to be no study on the effects of very-high-cycle fatigue (VHCF) and ultrasonic nanocrystal surface modification (UNSM) of a new bearing steel required for the durability design of next-generation bearing steel. This study analyzed the characteristics of microstructure transformation associated with heat treatment cycles and studied and evaluated the fatigue strength characteristics by the UNSM. Fisheye cracks occur from small inclusions inside the specimen only in martensitic microstructure materials in the long-life range. And the characteristic of a double S-N curve in which the fatigue limit decreased in two stages appeared. However, fisheye cracks and double S-N curves did not appear in pearlite and mixed-structure materials. Full article

Neural radiance fields (NeRF) have become an effective method for encoding scenes into neural representations, allowing for the synthesis of photorealistic views of unseen views from given input images. However, the applicability of traditional NeRF is significantly limited by its assumption that images are captured for object-centric scenes with a pinhole camera. Expanding these boundaries, we focus on driving scenarios using a fisheye camera, which offers the advantage of capturing visual information from a wide field of view. To address the challenges due to the unbounded and distorted characteristics of fisheye images, we propose an edge-aware integration loss function. This approach leverages sparse LiDAR projections and dense depth maps estimated from a learning-based depth model. The proposed algorithm assigns larger weights to neighboring points that have depth values similar to the sensor data. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets, which are public and real-world datasets of driving scenarios using fisheye cameras. Experimental results demonstrated that the proposed method is effective in synthesizing novel view images, outperforming existing approaches. Full article