Journal of Imaging最新文献_第7页

Systematic and Individualized Preparation of External Ear Canal Implants: Development and Validation of an Efficient and Accurate Automated Segmentation System. 系统和个体化外耳道植入物制备：高效和准确的自动分割系统的开发和验证。

IF 2.7

Journal of Imaging Pub Date : 2025-08-08 DOI: 10.3390/jimaging11080264

Yanjing Luo, Mohammadtaha Kouchakinezhad, Felix Repp, Verena Scheper, Thomas Lenarz, Farnaz Matin-Mann

{"title":"Systematic and Individualized Preparation of External Ear Canal Implants: Development and Validation of an Efficient and Accurate Automated Segmentation System.","authors":"Yanjing Luo, Mohammadtaha Kouchakinezhad, Felix Repp, Verena Scheper, Thomas Lenarz, Farnaz Matin-Mann","doi":"10.3390/jimaging11080264","DOIUrl":"10.3390/jimaging11080264","url":null,"abstract":"External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. This study aimed to characterize individual EEC morphology and to develop a validated automated segmentation system for efficient implant preparation. Reference datasets were first generated by manual segmentation using 3D SlicerTM software version 5.2.2. Based on these, we developed a customized plugin capable of automatically identifying the maximal implantable region within the EEC and measuring its key dimensions. The accuracy of the plugin was assessed by comparing it with manual segmentation results in terms of shape, volume, length, and width. Validation was further performed using three temporal bone implantation experiments with 3D-Bioplotter©-fabricated EEC implants. The automated system demonstrated strong consistency with manual methods and significantly improved segmentation efficiency. The plugin-generated models enabled successful implant fabrication and placement in all validation tests. These results confirm the system's clinical feasibility and support its use for individualized and systematic EEC implant design. The developed tool holds potential to improve surgical planning and reduce postoperative restenosis in EEC stenosis treatment.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light. 基于线结构光的柑橘分离线视觉检测方法研究。

IF 2.7

Journal of Imaging Pub Date : 2025-08-08 DOI: 10.3390/jimaging11080265

Qingcang Yu, Song Xue, Yang Zheng

{"title":"A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light.","authors":"Qingcang Yu, Song Xue, Yang Zheng","doi":"10.3390/jimaging11080265","DOIUrl":"10.3390/jimaging11080265","url":null,"abstract":"The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation. sbe -YOLO：结构感知和边界增强的YOLO焊缝实例分割。

IF 2.7

Journal of Imaging Pub Date : 2025-08-06 DOI: 10.3390/jimaging11080262

Rui Wen, Wu Xie, Yong Fan, Lanlan Shen

{"title":"SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation.","authors":"Rui Wen, Wu Xie, Yong Fan, Lanlan Shen","doi":"10.3390/jimaging11080262","DOIUrl":"10.3390/jimaging11080262","url":null,"abstract":"Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50-95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the Impact of 2D MRI Slice Orientation and Location on Alzheimer's Disease Diagnosis Using a Lightweight Convolutional Neural Network. 利用轻量级卷积神经网络评估二维MRI切片方向和定位对阿尔茨海默病诊断的影响。

IF 2.7

Journal of Imaging Pub Date : 2025-08-05 DOI: 10.3390/jimaging11080260

Nadia A Mohsin, Mohammed H Abdulameer

{"title":"Evaluating the Impact of 2D MRI Slice Orientation and Location on Alzheimer's Disease Diagnosis Using a Lightweight Convolutional Neural Network.","authors":"Nadia A Mohsin, Mohammed H Abdulameer","doi":"10.3390/jimaging11080260","DOIUrl":"10.3390/jimaging11080260","url":null,"abstract":"Accurate detection of Alzheimer's disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative Magnetic Resonance Imaging and Patient-Reported Outcomes in Patients Undergoing Hip Labral Repair or Reconstruction. 定量磁共振成像和患者报告的髋关节唇部修复或重建的结果。

IF 2.7

Journal of Imaging Pub Date : 2025-08-05 DOI: 10.3390/jimaging11080261

Kyle S J Jamar, Adam Peszek, Catherine C Alder, Trevor J Wait, Caleb J Wipf, Carson L Keeter, Stephanie W Mayer, Charles P Ho, James W Genuario

{"title":"Quantitative Magnetic Resonance Imaging and Patient-Reported Outcomes in Patients Undergoing Hip Labral Repair or Reconstruction.","authors":"Kyle S J Jamar, Adam Peszek, Catherine C Alder, Trevor J Wait, Caleb J Wipf, Carson L Keeter, Stephanie W Mayer, Charles P Ho, James W Genuario","doi":"10.3390/jimaging11080261","DOIUrl":"10.3390/jimaging11080261","url":null,"abstract":"This study evaluates the relationship between preoperative cartilage quality, measured by T2 mapping, and patient-reported outcomes following labral tear treatment. We retrospectively reviewed patients aged 14-50 who underwent primary hip arthroscopy with either labral repair or reconstruction. Preoperative T2 values of femoral, acetabular, and labral tissue were assessed from MRI by blinded reviewers. International Hip Outcome Tool (iHOT-12) scores were collected preoperatively and up to two years postoperatively. Associations between T2 values and iHOT-12 scores were analyzed using univariate mixed linear models. Twenty-nine patients were included (mean age of 32.5 years, BMI 24 kg/m2, 48.3% female, and 22 repairs). Across all patients, higher T2 values were associated with higher iHOT-12 scores at baseline and early postoperative timepoints (three months for cartilage and six months for labrum; p < 0.05). Lower T2 values were associated with higher 12- and 24-month iHOT-12 scores across all structures (p < 0.001). Similar trends were observed within the repair and reconstruction subgroups, with delayed negative associations correlating with worse tissue quality. T2 mapping showed time-dependent correlations with iHOT-12 scores, indicating that worse cartilage or labral quality predicts poorer long-term outcomes. These findings support the utility of T2 mapping as a preoperative tool for prognosis in hip preservation surgery.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive RGB-D Semantic Segmentation with Skip-Connection Fusion for Indoor Staircase and Elevator Localization. 基于跳跃连接融合的自适应RGB-D语义分割用于室内楼梯和电梯定位。

IF 2.7

Journal of Imaging Pub Date : 2025-08-04 DOI: 10.3390/jimaging11080258

Zihan Zhu, Henghong Lin, Anastasia Ioannou, Tao Wang

{"title":"Adaptive RGB-D Semantic Segmentation with Skip-Connection Fusion for Indoor Staircase and Elevator Localization.","authors":"Zihan Zhu, Henghong Lin, Anastasia Ioannou, Tao Wang","doi":"10.3390/jimaging11080258","DOIUrl":"10.3390/jimaging11080258","url":null,"abstract":"Accurate semantic segmentation of indoor architectural elements, such as staircases and elevators, is critical for safe and efficient robotic navigation, particularly in complex multi-floor environments. Traditional fusion methods struggle with occlusions, reflections, and low-contrast regions. In this paper, we propose a novel feature fusion module, Skip-Connection Fusion (SCF), that dynamically integrates RGB (Red, Green, Blue) and depth features through an adaptive weighting mechanism and skip-connection integration. This approach enables the model to selectively emphasize informative regions while suppressing noise, effectively addressing challenging conditions such as partially blocked staircases, glossy elevator doors, and dimly lit stair edges, which improves obstacle detection and supports reliable human-robot interaction in complex environments. Extensive experiments on a newly collected dataset demonstrate that SCF consistently outperforms state-of-the-art methods, including PSPNet and DeepLabv3, in both overall mIoU (mean Intersection over Union) and challenging-case performance. Specifically, our SCF module improves segmentation accuracy by 5.23% in the top 10% of challenging samples, highlighting its robustness in real-world conditions. Furthermore, we conduct a sensitivity analysis on the learnable weights, demonstrating their impact on segmentation quality across varying scene complexities. Our work provides a strong foundation for real-world applications in autonomous navigation, assistive robotics, and smart surveillance.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Road Marking Damage Degree Detection Based on Boundary Features Enhanced and Asymmetric Large Field-of-View Contextual Features. 基于边界特征增强和非对称大视场上下文特征的道路标线损伤程度检测。

IF 2.7

Journal of Imaging Pub Date : 2025-08-04 DOI: 10.3390/jimaging11080259

Zheng Wang, Ryojun Ikeura, Soichiro Hayakawa, Zhiliang Zhang

{"title":"Road Marking Damage Degree Detection Based on Boundary Features Enhanced and Asymmetric Large Field-of-View Contextual Features.","authors":"Zheng Wang, Ryojun Ikeura, Soichiro Hayakawa, Zhiliang Zhang","doi":"10.3390/jimaging11080259","DOIUrl":"10.3390/jimaging11080259","url":null,"abstract":"Road markings, as critical components of transportation infrastructure, are crucial for ensuring traffic safety. Accurate quantification of their damage severity is vital for effective maintenance prioritization. However, existing methods are limited to detecting the presence of damage without assessing its extent. To address this limitation, we propose a novel segmentation-based framework for estimating the degree of road marking damage. The method comprises two stages: segmentation of residual pixels from the damaged markings and segmentation of the intact markings region. This dual-segmentation strategy enables precise reconstruction and comparison for severity estimation. To enhance segmentation performance, we proposed two key modules: the Asymmetric Large Field-of-View Contextual (ALFVC) module, which captures rich multi-scale contextual features, and the supervised Boundary Feature Enhancement (BFE) module, which strengthens shape representation and boundary accuracy. The experimental results demonstrate that our method achieved an average segmentation accuracy of 89.44%, outperforming the baseline by 5.86 percentage points. Moreover, the damage quantification achieved a minimum error rate of just 0.22% on the proprietary dataset. The proposed approach was both effective and lightweight, providing valuable support for automated maintenance planning, and significantly improving the efficiency and precision of road marking management.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Method for Analysing the Curvature of the Anterior Lens: Multi-Radial Scheimpflug Imaging and Custom Conic Fitting Algorithm. 一种分析前晶状体曲率的新方法：多径向Scheimpflug成像和自定义圆锥拟合算法。

IF 2.7

Journal of Imaging Pub Date : 2025-08-01 DOI: 10.3390/jimaging11080257

María Arcas-Carbonell, Elvira Orduna-Hospital, María Mechó-García, Guisela Fernández-Espinosa, Ana Sanchez-Cano

{"title":"A Novel Method for Analysing the Curvature of the Anterior Lens: Multi-Radial Scheimpflug Imaging and Custom Conic Fitting Algorithm.","authors":"María Arcas-Carbonell, Elvira Orduna-Hospital, María Mechó-García, Guisela Fernández-Espinosa, Ana Sanchez-Cano","doi":"10.3390/jimaging11080257","DOIUrl":"10.3390/jimaging11080257","url":null,"abstract":"This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) change during accommodation and whether these changes are age-dependent. A cross-sectional study was conducted on 104 right eyes from healthy participants aged 21-62 years. Sixteen radial images per eye were acquired using the Galilei Dual Scheimpflug Placido Disk Topographer under four accommodative demands (0, 1, 3, and 5 dioptres (D)). Custom software analysed lens curvature by calculating eccentricity in both meridians. Participants were analysed as a total group and by age subgroups. Accommodative amplitude and monocular accommodative facility were inversely correlated with age. Both PD and ACD significantly decreased with higher accommodative demands and age. Relative eccentricity decreased under accommodation, indicating increased lens curvature, especially in younger participants. Significant curvature changes were detected in the horizontal meridian only, although no statistically significant differences between meridians were found overall. The vertical meridian showed slightly higher eccentricity values, suggesting that it remained less curved. By enabling detailed, meridionally stratified in vivo assessment of anterior lens curvature, this novel method provides a valuable non-invasive approach for characterizing age-related biomechanical changes during accommodation. The resulting insights enhance our understanding of presbyopia progression, particularly regarding the spatial remodelling of the anterior lens surface.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Early Blight Detection in Potato Leaves Through ZeroShot Learning. 利用ZeroShot学习技术推进马铃薯叶枯病早期检测

IF 2.7

Journal of Imaging Pub Date : 2025-07-31 DOI: 10.3390/jimaging11080256

Muhammad Shoaib Farooq, Ayesha Kamran, Syed Atir Raza, Muhammad Farooq Wasiq, Bilal Hassan, Nitsa J Herzog

{"title":"Advancing Early Blight Detection in Potato Leaves Through ZeroShot Learning.","authors":"Muhammad Shoaib Farooq, Ayesha Kamran, Syed Atir Raza, Muhammad Farooq Wasiq, Bilal Hassan, Nitsa J Herzog","doi":"10.3390/jimaging11080256","DOIUrl":"10.3390/jimaging11080256","url":null,"abstract":"Potatoes are one of the world's most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"11 8","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Automated Method for Identifying Voids and Severe Loosening in GPR Images. GPR图像中空洞和严重松动的自动识别方法。

IF 2.7

Journal of Imaging Pub Date : 2025-07-30 DOI: 10.3390/jimaging11080255

Ze Chai, Zicheng Wang, Zeshan Xu, Ziyu Feng, Yafeng Zhao

引用次数: 0