Bibliografías temáticas / Road scene understanding

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Literatura académica sobre el tema "Road scene understanding"

Autor: Grafiati
Publicado: 10 de marzo de 2023
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Road scene understanding"

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Zhou, Wujie, Sijia Lv, Qiuping Jiang, and Lu Yu. "Deep Road Scene Understanding." IEEE Signal Processing Letters 26, no. 4 (2019): 587–91. http://dx.doi.org/10.1109/lsp.2019.2896793.

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Huang, Wenqi, Fuzheng Zhang, Aidong Xu, Huajun Chen, and Peng Li. "Fusion-based holistic road scene understanding." Journal of Engineering 2018, no. 16 (2018): 1623–28. http://dx.doi.org/10.1049/joe.2018.8319.

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Naudé, August J., and Herman C. Myburgh. "Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention." Sensors 23, no. 17 (2023): 7355. http://dx.doi.org/10.3390/s23177355.

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Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demon
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Wang, Chao, Huan Wang, Rui Li Wang, and Chun Xia Zhao. "Robust Zebra-Crossing Detection for Autonomous Land Vehicles and Driving Assistance Systems." Applied Mechanics and Materials 556-562 (May 2014): 2732–39. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2732.

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Road scene understanding is critical for driving assistance systems and autonomous land vehicles. The main function of road scene understanding is robustly detecting useful visual objects existing in a road scene. A zebra crossing is a typical pedestrian crossing used in many countries around the world. When detecting a zebra crossing, an autonomous lane vehicle is normally required to automatically slow down its speed and to trigger a path-planning strategy for passing the zebra crossing. Also, most of driving assistance systems can send an early-warning signal to remind drivers to be more ca
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Elhenawy, Mohammed, Huthaifa I. Ashqar, Andry Rakotonirainy, Taqwa I. Alhadidi, Ahmed Jaber, and Mohammad Abu Tami. "Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding." Electronics 14, no. 7 (2025): 1282. https://doi.org/10.3390/electronics14071282.

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Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analyses o
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Xu, Zhaosheng, Zhongming Liao, Xiaoyong Xiao, Suzana Ahmad, Norizan Mat Diah, and Azlan Ismail. "Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology." International Journal for Simulation and Multidisciplinary Design Optimization 16 (2025): 6. https://doi.org/10.1051/smdo/2025004.

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Understanding road scenes is crucial to the safe driving of autonomous vehicles, and object detection in road scenes is necessary to develop driving assistance systems. Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city traffic. Using Improved Multi-Scale Adaptive Feature Fusion Technology (IMSAFFT), this work suggests a real-time traffic information identification method to fix the issues of low detection accuracy of road scenes and high false detection rates in panoramic video images. In addition,
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Liu, Huajun, Cailing Wang, and Jingyu Yang. "Vanishing points estimation and road scene understanding based on Bayesian posterior probability." Industrial Robot: An International Journal 43, no. 1 (2016): 12–21. http://dx.doi.org/10.1108/ir-05-2015-0095.

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Purpose – This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification. Design/methodology/approach – The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient
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Deepa Mane, Et al. "A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2027–38. http://dx.doi.org/10.17762/ijritcc.v11i10.8886.

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Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, t
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Yasrab, Robail. "ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding." Journal of Imaging 4, no. 10 (2018): 116. http://dx.doi.org/10.3390/jimaging4100116.

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This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer m
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Qin, Yuting, Yuren Chen, and Kunhui Lin. "Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2437. http://dx.doi.org/10.3390/ijerph17072437.

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Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitt
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Tesis sobre el tema "Road scene understanding"

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Habibi, Aghdam Hamed. "Understanding Road Scenes using Deep Neural Networks." Doctoral thesis, Universitat Rovira i Virgili, 2018. http://hdl.handle.net/10803/461607.

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La comprensió de les escenes de la carretera és fonamental per als automòbils autònoms. Això requereix segmentar escenes de carreteres en regions semànticament significatives i reconèixer objectes en una escena. Tot i que objectes com ara cotxes i vianants han de segmentar-se amb precisió, és possible que no sigui necessari detectar i localitzar aquests objectes en una escena. Tanmateix, detectar i classificar objectes com ara els senyals de trànsit és fonamental per ajustar-se a les regles del camí. En aquesta tesi, primer proposem un mètode per classificar senyals de trànsit amb atributs vis
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Lee, Jong Ho. "Understanding the Visual Appearance of Road Scenes Using a Monocular Camera." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/795.

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Over the past several decades, research efforts in the development of self-driving vehicles have drastically improved accompanying technologies. Since the challenges held by Defense Advanced Research Projects Agency, the autonomous driving industry has increased significantly, and almost all the automotive companies have started to develop the technologies to deploy autonomous driving vehicles in the real world. Even though a lot of companies have been making efforts to achieve fully automated vehicles, the current technologies are not mature enough to be deployed in the real world yet, becaus
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Wang, Fan. "How polarimetry may contribute to understand reflective road scenes : theory and applications." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0003/document.

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Les systèmes d'aide à la conduite (ADAS) visent à automatiser/ adapter/ améliorer les systèmes de transport pour une meilleure sécurité et une conduite plus sûre. Plusieurs thématiques de recherche traitent des problématiques autour des ADAS, à savoir la détection des obstacles, la reconnaissance de formes, la compréhension des images, la stéréovision, etc. La présence des réflexions spéculaires limite l'efficacité et la précision de ces algorithmes. Elles masquent les textures de l'image originale et contribuent à la perte de l'information utile. La polarisation de la lumière traduit implicit
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Kung, Wen Yao, and 龔芠瑤. "Road Scene Understanding with Semantic Segmentation and Object Hazard Level Prediction." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60494150880821921387.

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碩士<br>國立清華大學<br>資訊工程學系<br>104<br>We introduce a method for understanding road scenes and simultaneously predicting the hazard levels of three categories of objects in road scene images by using a fully convolutional network (FCN) architecture. In our approach, with a single input image, the multi-task model produces a _ne segmentation result and a prediction of hazard levels in a form of heatmap. The model can be divided into three parts: shared net, segmentation net, and hazard level net. The shared net and segmentation net use the encoder-decoder architecture provided by Badrinarayanan et al
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Schoen, Fabio. "Deep learning methods for safety-critical driving events analysis." Doctoral thesis, 2022. http://hdl.handle.net/2158/1260238.

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In this thesis, we propose to study the data of crash and near-crash events, collectively called safety-critical driving events. Such data include a footage of the event, acquired from a camera mounted inside the vehicle, and the data from a GPS/IMU module, i.e., speed, acceleration and angular velocity. First, we introduce a novel problem, that we call unsafe maneuver classification, that aims at classifying safety-critical driving events based on the maneuver that leads to the unsafe situation and we propose a two-stream neural architecture based on Convolutional Neural Networks that perfor
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Hummel, Britta [Verfasser]. "Description logic for scene understanding at the example of urban road intersections / von Britta Hummel." 2009. http://d-nb.info/1000324818/34.

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Libros sobre el tema "Road scene understanding"

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Voparil, Chris. Reconstructing Pragmatism. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197605721.001.0001.

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The figure of Richard Rorty stands in complex relation to the tradition of American pragmatism. On the one hand, his intellectual creativity, lively prose, and bridge-building fueled the contemporary resurgence of pragmatism. On the other, his polemical claims and selective interpretations function as a negative, fixed pole against which thinkers of all stripes define themselves. Virtually all pragmatists on the contemporary scene, whether classical or “new,” Deweyan, Jamesian, or Peircean, use Rorty as a foil to justify their positions. The resulting divisions and internecine quarrels threate
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Biel Portero, Israel, Andrea Carolina Casanova Mejía, Amanda Janneth Riascos Mora, et al. Challenges and alternatives towards peacebuilding. Edited by Ángela Marcela Castillo Burbano and Claudia Andrea Guerrero Martínez. Ediciones Universidad Cooperativa de Colombia, 2020. http://dx.doi.org/10.16925/9789587602388.

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Rural development and peacebuilding in Colombia have been highly prioritized by higher education institutions since the signing of the Peace Agreement between the National Government and the FARC-EP. This has resulted in the need to further analyze rural strategies that contribute towards a better life for the population of territories where armed conflict is coming to an end, whilst understanding the pressing uncertainty that this process implies; on the one hand, for the urgency of generating rapid and concrete responses to social justice and equity, and on the other, because fulfilling the
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Capítulos de libros sobre el tema "Road scene understanding"

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Maurya, Anamika, and Satish Chand. "Understanding Road Scene Images Using CNN Features." In Computational Intelligence in Analytics and Information Systems. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003332312-33.

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Holder, Christopher J., and Toby P. Breckon. "Encoding Stereoscopic Depth Features for Scene Understanding in off-Road Environments." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_48.

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Chatterjee, Soumick, Jiahua Xu, Adarsh Kuzhipathalil, and Andreas Nürnberger. "HaWANet: Road Scene Understanding with Multi-modal Sensor Data Using Height-Width-Driven Attention Network." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80607-0_8.

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Zhou, Hongkuan, Stefan Schimid, Yicong Li, Lavdim Halilaj, Xiangtong Yao, and Wei Cao. "Predicting the Road Ahead: A Knowledge Graph Based Foundation Model for Scene Understanding in Autonomous Driving." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94575-5_7.

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Kembhavi, Aniruddha, Tom Yeh, and Larry S. Davis. "Why Did the Person Cross the Road (There)? Scene Understanding Using Probabilistic Logic Models and Common Sense Reasoning." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15552-9_50.

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Kuhnimhof, Tobias, Hiroaki Miyoshi, Ayako Taniguchi, et al. "Setting the Scene for Automated Mobility: A Comparative Introduction to the Mobility Systems in Germany and Japan." In Acceptance and Diffusion of Connected and Automated Driving in Japan and Germany. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59876-0_2.

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AbstractThis chapter presents a comparative analysis of the mobility systems in Germany and Japan, providing insights into how these systems might influence the implementation of vehicle automation. This comparison begins by exploring the historical evolution of transport in both countries, noting that both have long-established infrastructures shaped by unique geographical and historical contexts. Germany’s transport system, for instance, developed within a landlocked nation with extensive rail networks, while Japan’s transport was influenced by its island geography and mountainous terrain. T
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Alvarez, Jose M., Felipe Lumbreras, Antonio M. Lopez, and Theo Gevers. "Understanding Road Scenes Using Visual Cues and GPS Information." In Computer Vision – ECCV 2012. Workshops and Demonstrations. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_70.

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Oeljeklaus, Malte. "5 Global Road Topology from Scene Context Recognition." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-38.

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Oeljeklaus, Malte. "7 Road Users from Bounding Box Detection." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-64.

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Oeljeklaus, Malte. "6 Drivable Road Area from Semantic Image Segmentation." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-50.

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Actas de conferencias sobre el tema "Road scene understanding"

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Zunair, Hasib, Shakib Khan, and A. Ben Hamza. "Rsud20K: a Dataset for Road Scene Understanding in Autonomous Driving." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10648203.

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Keskar, Aryan, Srinivasa Perisetla, and Ross Greer. "Evaluating Multimodal Vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00115.

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Guo, Ziang, Zakhar Yagudin, Artem Lykov, Mikhail Konenkov, and Dzmitry Tsetserukou. "VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenes." In 2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852498.

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Dhiman, Vikas, Quoc-Huy Tran, Jason J. Corso, and Manmohan Chandraker. "A Continuous Occlusion Model for Road Scene Understanding." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.469.

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Sun, Yuan, Hongbo Lu, and Zhimin Zhang. "RvGIST: A Holistic Road Feature for Real-Time Road-Scene Understanding." In 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2013. http://dx.doi.org/10.1109/snpd.2013.86.

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Tsukada, A., M. Ogawa, and F. Galpin. "Road structure based scene understanding for intelligent vehicle systems." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5653532.

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Venkateshkumar, Suhas Kashetty, Muralikrishna Sridhar, and Patrick Ott. "Latent Hierarchical Part Based Models for Road Scene Understanding." In 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2015. http://dx.doi.org/10.1109/iccvw.2015.25.

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Solanki, Vishist Singh, Abhishek Dewan, Hariom Singh, Ashutosh Kumar, and Er Parampreet Kaur. "U-Net Based Semantic Segmentation for Road Scene Understanding." In 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2023. http://dx.doi.org/10.1109/smart59791.2023.10428246.

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Jia, Peng, Jianwei Gong, Yahui Jiang, Yuchun Wang, Yubo Zhang, and Zhiyang Ju. "Structured Bird’s-Eye View Road Scene Understanding from Surround Video." In 2024 IEEE Intelligent Vehicle Symposium (IV). IEEE, 2024. http://dx.doi.org/10.1109/iv55156.2024.10588512.

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Sturgess, Paul, Karteek Alahari, Lubor Ladicky, and Philip H. S. Torr. "Combining Appearance and Structure from Motion Features for Road Scene Understanding." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.62.

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