Academic literature on the topic 'Imagerie RGB'

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Journal articles on the topic "Imagerie RGB"

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Vigneau, Nathalie, Corentin Chéron, Aleixandre Verger, and Frédéric Baret. "Imagerie aérienne par drone : exploitation des données pour l'agriculture de précision." Revue Française de Photogrammétrie et de Télédétection, no. 213 (April 26, 2017): 125–31. http://dx.doi.org/10.52638/rfpt.2017.203.

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La technologie des drones devenant plus accessible et les réglementations nationales encadrant les vols des drones commençant à émerger, de nombreuses sociétés utilisent désormais des drones pour réaliser des acquisitions d'images.Parmi celles-ci AIRINOV a choisi de se spécialiser dans l'agriculture et offre ses services aux agriculteurs ainsi qu'aux expérimentateurs. AIRINOV exploite les drones eBee de senseFly. Le drone a une envergure d'1 m pour un poids de 700 g charge comprise et son vol est entièrment automatique. Le vol est programmé à l'avance puis contrôlé par unauto-pilote connecté à
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Shen, Xin, Lin Cao, Bisheng Yang, Zhong Xu, and Guibin Wang. "Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries." Remote Sensing 11, no. 7 (2019): 800. http://dx.doi.org/10.3390/rs11070800.

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Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral an
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Priyankara, Prabath, and Takehiro Morimoto. "UAV Based Agricultural Crop Canopy Mapping for Crop Field Monitoring." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-303-2019.

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<p><strong>Abstract.</strong> Nowadays, mapping of agricultural crop canopy in different growing stages are vital data for crop field monitoring than field-based observations in large scale agricultural crop fields. By mapping agricultural crop canopy, it is very easy to analyse the status of an agricultural crop field by using different vegetation indices. Further, the data can be used to estimate the yield. These information are timely and reliable spatial information to the farmers and decision makers. Mapping of crop canopy in an agricultural crop field in different growi
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Purwanto, Anang Dwi, and Wikanti Asriningrum. "IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (2019): 63. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3097.

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The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites
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Chhatkuli, S., T. Satoh, and K. Tachibana. "MULTI SENSOR DATA INTEGRATION FOR AN ACCURATE 3D MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 103–6. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-103-2015.

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The aim of this paper is to introduce a novel technique of data integration between two different data sets, i.e. laser scanned RGB point cloud and oblique imageries derived 3D model, to create a 3D model with more details and better accuracy. In general, aerial imageries are used to create a 3D city model. Aerial imageries produce an overall decent 3D city models and generally suit to generate 3D model of building roof and some non-complex terrain. However, the automatically generated 3D model, from aerial imageries, generally suffers from the lack of accuracy in deriving the 3D model of road
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Argyrou, Argyro, Athos Agapiou, Apostolos Papakonstantinou, and Dimitrios D. Alexakis. "Comparison of Machine Learning Pixel-Based Classifiers for Detecting Archaeological Ceramics." Drones 7, no. 9 (2023): 578. http://dx.doi.org/10.3390/drones7090578.

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Recent improvements in low-altitude remote sensors and image processing analysis can be utilised to support archaeological research. Over the last decade, the increased use of remote sensing sensors and their products for archaeological science and cultural heritage studies has been reported in the literature. Therefore, different spatial and spectral analysis datasets have been applied to recognise archaeological remains or map environmental changes over time. Recently, more thorough object detection approaches have been adopted by researchers for the automated detection of surface ceramics.
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Mawardi, Sonny, Emi Sukiyah, and Iyan Haryanto. "Morphotectonic Characteristics Of Cisadane Watersshed Based On Satellite Images Analysis." Jurnal Geologi dan Sumberdaya Mineral 20, no. 3 (2019): 175. http://dx.doi.org/10.33332/jgsm.geologi.v20i3.464.

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Cisadane Watershed is one of the most rapidly growing areas and infrastructure development, and has developed as a residential, industrial, administrative centers and other economic activities. The purpose of this paper is to use remote sensing satellite imageries to identify the morphotectonic characteristics of the Cisadane watershed both qualitatively and quantitatively. Processing stereomodel, stereoplotting and stereocompilation on TerraSAR-X Digital Surface Model (DSM) and SPOT 6 imageries, produced the Digital Terrain Model (DTM) image, which has not been affected by land cover. Fusion
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Semah, Franck. "Imagerie médicale et épilepsies." Revue Générale Nucléaire, no. 4 (August 2001): 36–37. http://dx.doi.org/10.1051/rgn/20014036.

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Simes, Tomás, Luís Pádua, and Alexandra Moutinho. "Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery." Remote Sensing 16, no. 1 (2023): 30. http://dx.doi.org/10.3390/rs16010030.

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Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hy
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Vanbrabant, Yasmin, Stephanie Delalieux, Laurent Tits, Klaas Pauly, Joke Vandermaesen, and Ben Somers. "Pear Flower Cluster Quantification Using RGB Drone Imagery." Agronomy 10, no. 3 (2020): 407. http://dx.doi.org/10.3390/agronomy10030407.

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High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bott
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Dissertations / Theses on the topic "Imagerie RGB"

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Lefévre, Soizic. "Caractérisation de la qualité des raisins par imagerie." Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS017.

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L’identification des états sanitaires du raisin au moment de la vendange est un enjeu majeur afin de produire des vins de qualité. Pour répondre à cet enjeu, des données sont acquises par spectrométrie, imagerie hyperspectrale et imagerie RGB sur des échantillons de raisin au cours des vendanges.Plusieurs prétraitements adaptés à chaque type de données sont appliqués tels que la normalisation, la réduction, l’extraction de vecteurs de caractéristiques et la segmentation de zones utiles. D’un point de vue imagerie, la reconstitution en fausses couleurs des images hyperspectrales, éloignée de la
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Kacete, Amine. "Unconstrained Gaze Estimation Using RGB-D Camera." Thesis, CentraleSupélec, 2016. http://www.theses.fr/2016SUPL0012/document.

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Dans ce travail, nous avons abordé le problème d’estimation automatique du regard dans des environnements utilisateur sans contraintes. Ce travail s’inscrit dans la vision par ordinateur appliquée à l’analyse automatique du comportement humain. Plusieurs solutions industrielles sont aujourd’hui commercialisées et donnent des estimations précises du regard. Certaines ont des spécifications matérielles très complexes (des caméras embarquées sur un casque ou sur des lunettes qui filment le mouvement des yeux) et présentent un niveau d’intrusivité important, ces solutions sont souvent non accessib
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Kadkhodamohammadi, Abdolrahim. "3D detection and pose estimation of medical staff in operating rooms using RGB-D images." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD047/document.

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Dans cette thèse, nous traitons des problèmes de la détection des personnes et de l'estimation de leurs poses dans la Salle Opératoire (SO), deux éléments clés pour le développement d'applications d'assistance chirurgicale. Nous percevons la salle grâce à des caméras RGB-D qui fournissent des informations visuelles complémentaires sur la scène. Ces informations permettent de développer des méthodes mieux adaptées aux difficultés propres aux SO, comme l'encombrement, les surfaces sans texture et les occlusions. Nous présentons des nouvelles approches qui tirent profit des informations temporell
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Devanne, Maxime. "3D human behavior understanding by shape analysis of human motion and pose." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10138/document.

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L'émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l'étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l'environnement. Dans cette thèse, nous nous concentrons d'abord sur le problème de la reconnaissance d'actions en représentant la trajectoire du corps
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Tykkälä, Tommi. "Suivi de caméra image en temps réel base et cartographie de l'environnement." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00933813.

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Dans ce travail, méthodes d'estimation basées sur des images, également connu sous le nom de méthodes directes, sont étudiées qui permettent d'éviter l'extraction de caractéristiques et l'appariement complètement. L'objectif est de produire pose 3D précis et des estimations de la structure. Les fonctions de coût présenté minimiser l'erreur du capteur, car les mesures ne sont pas transformés ou modifiés. Dans la caméra photométrique estimation de la pose, rotation 3D et les paramètres de traduction sont estimées en minimisant une séquence de fonctions de coûts à base d'image, qui sont des non-l
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Alston, Laure. "Spectroscopie de fluorescence et imagerie optique pour l'assistance à la résection de gliomes : conception et caractérisation de systèmes de mesure et modèles de traitement des données associées, sur fantômes et au bloc opératoire." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1295/document.

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Les gliomes sont des tumeurs cérébrales infiltrantes difficilement curables, notamment à cause de la difficulté à visualiser toutes les infiltrations au bloc opératoire. Dans cette thèse, nous réalisons une étude clinique de spectroscopie de fluorescence de la protoporphyrine IX (PpIX) dans les gliomes de 10 patients selon l’hypothèse que les spectres collectés proviennent de la contribution de 2 états de la PpIX dont les proportions varient suivant la densité en cellules tumorales. Après avoir présenté le développement du système interventionnel proposant une excitation multi-longueurs d’onde
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Chakib, Reda. "Acquisition et rendu 3D réaliste à partir de périphériques "grand public"." Thesis, Limoges, 2018. http://www.theses.fr/2018LIMO0101/document.

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L'imagerie numérique, de la synthèse d'images à la vision par ordinateur est en train de connaître une forte évolution, due entre autres facteurs à la démocratisation et au succès commercial des caméras 3D. Dans le même contexte, l'impression 3D grand public, qui est en train de vivre un essor fulgurant, contribue à la forte demande sur ce type de caméra pour les besoins de la numérisation 3D. L'objectif de cette thèse est d'acquérir et de maîtriser un savoir-faire dans le domaine de la capture/acquisition de modèles 3D en particulier sur l'aspect rendu réaliste. La réalisation d'un scanner 3D
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Chiron, Guillaume. "Système complet d’acquisition vidéo, de suivi de trajectoires et de modélisation comportementale pour des environnements 3D naturellement encombrés : application à la surveillance apicole." Thesis, La Rochelle, 2014. http://www.theses.fr/2014LAROS030/document.

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Ce manuscrit propose une approche méthodologique pour la constitution d’une chaîne complète de vidéosurveillance pour des environnements naturellement encombrés. Nous identifions et levons un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à l’acquisition de séquences vidéo en milieu naturel, 2) au traitement d’images, 3) au suivi multi-cibles, 4) à la découverte et la modélisation de motifs comportementaux récurrents, et 5) à la fusion de données. Le contexte applicatif de nos travaux est la surveillance apicole, et en particulier, l’étude des trajectoires des abeil
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Muske, Manideep Sai Yadav. "To Detect Water-Puddle On Driving Terrain From RGB Imagery Using Deep Learning Algorithms." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21229.

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Background: With the emerging application of autonomous vehicles in the automotive industry, several efforts have been made for the complete adoption of autonomous vehicles. One of the several problems in creating autonomous technology is the detection of water puddles, which can cause damages to internal components and the vehicle to lose control. This thesis focuses on the detection of water puddles on-road and off-road conditions with the use of Deep Learning models. Objectives: The thesis focuses on finding suitable Deep Learning algorithms for detecting the water puddles, and then an expe
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Fernández, Gallego José Armando. "Image processing techniques for plant phenotyping using RGB and thermal imagery = Técnicas de procesamiento de imágenes RGB y térmicas como herramienta para fenotipado de cultivos." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/669111.

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World cereal stocks need to increase in order to meet growing demands. Currently, maize, rice, wheat, are the main crops worldwide, while other cereals such as barley, sorghum, oat or different millets are also well placed in the top list. Crop productivity is affected directly by climate change factors such as heat, drought, floods or storms. Researchers agree that global climate change is having a major impact on crop productivity. In that way, several studies have been focused on climate change scenarios and more specifically abiotic stresses in cereals. For instance, in the case of heat st
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Books on the topic "Imagerie RGB"

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King, Jane Valerie. VALIDATION OF A SPECIFIC TECHNIQUE OF RELAXATION WITH GUIDED IMAGERY (RGI) ON STATE ANXIETY IN GRADUATE NURSING STUDENTS. 1987.

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Book chapters on the topic "Imagerie RGB"

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Lorenzo-Navarro, Javier, Modesto Castrillón-Santana, and Daniel Hernández-Sosa. "An Study on Re-identification in RGB-D Imagery." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35395-6_28.

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Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, and Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." In Cyber-Physical Security for Critical Infrastructures Protection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_6.

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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed
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Matos, João Pedro, Artur Machado, Ricardo Ribeiro, and Alexandra Moutinho. "Automatic People Detection Based on RGB and Thermal Imagery for Military Applications." In Robot 2023: Sixth Iberian Robotics Conference. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59167-9_17.

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Pádua, Luís, Nathalie Guimarães, Telmo Adão, et al. "Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle." In Progress in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30241-2_22.

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Sukkar, Abdullah, and Mustafa Turker. "Tree Detection from Very High Spatial Resolution RGB Satellite Imagery Using Deep Learning." In Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43218-7_34.

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Rozanda, Nesdi Evrilyan, M. Ismail, and Inggih Permana. "Segmentation Google Earth Imagery Using K-Means Clustering and Normalized RGB Color Space." In Computational Intelligence in Data Mining - Volume 1. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2205-7_36.

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Niu, Qinglin, Haikuan Feng, Changchun Li, et al. "Estimation of Leaf Nitrogen Concentration of Winter Wheat Using UAV-Based RGB Imagery." In Computer and Computing Technologies in Agriculture XI. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06179-1_15.

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Tejasri, N., Sam Mathew Betson, P. Rajalakshmi, M. Balram, and Uday B. Desai. "Deep Networks Based Approach for Automatic Counting Panicles on UAV Captured Paddy RGB Imagery." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74633-8_20.

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Guarin, Arnold, Homero Ortega, and Hans Garcia. "Acquisition System Based in a Low-Cost Optical Architecture with Single Pixel Measurements and RGB Side Information for UAV Imagery." In Applications of Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36211-9_13.

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Zefri, Yahya, Imane Sebari, Hicham Hajji, and Ghassane Aniba. "A Channel-Based Attention Deep Semantic Segmentation Model for the Extraction of Multi-type Solar Photovoltaic Arrays from Large-Scale Orthorectified UAV RGB Imagery." In Intelligent Sustainable Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7660-5_36.

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Conference papers on the topic "Imagerie RGB"

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Rustom, Estephan, Henrique Cabral, Sreeraj Rajendran, and Elena Tsiporkova. "ShadowScout: Robust Unsupervised Shadow Detection for RGB Imagery." In 20th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013254900003912.

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Perez, Fabian, and Hoover Rueda-Chacón. "Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00034.

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Li, Xiangjie, Shuxiang Xie, Ken Sakurada, Ryusuke Sagawa, and Takeshi Oishi. "Implicit Neural Fusion of RGB and Far-Infrared 3D Imagery for Invisible Scenes." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802441.

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Lan, Nguyen Thi, Cao Truong Tran, Nguyen Hai Dang, and Tran Thi Toi. "Integrating Transformer and Enhanced YOLOv10 for Improved Small Object Detection in RGB and Infrared Remote Sensing Imagery." In 2024 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2024. https://doi.org/10.1109/rivf64335.2024.11009090.

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Hussain, Muhammad Hamza, and Syed Atif Mehdi. "Spatial Recognition of Citrus Trees, Seedlings, and Gaps: A Deep Learning and Geometric Approach on Satellite-Based RGB Imagery." In 2024 Horizons of Information Technology and Engineering (HITE). IEEE, 2024. https://doi.org/10.1109/hite63532.2024.10777150.

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Gui, Ziming, Yan Zhang, Xu Lei, Ruixiang Zhang, and Wen Yang. "AODet: Anti-Occlusion for Enhanced Small Object Detection in Drone-Based RGBT Imagery." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642567.

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Han, Yiding, Austin Jensen, and Huifang Dou. "Programmable Multispectral Imager Development as Light Weight Payload for Low Cost Fixed Wing Unmanned Aerial Vehicles." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87741.

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In this paper, we have developed a light-weight and cost-efficient multispectral imager payload for low cost fixed wing UAVs (Unmanned Aerial Vehicles) that need no runway for takeoff and landing. The imager is band-reconfigurable, covering both visual (RGB) and near infrared (NIR) spectrum. The number of the RGB and NIR sensors is scalable, depending on the demands of specific applications. The UAV on-board microcomputer programs and controls the imager system, synchronizing each camera individually to capture airborne imagery. It also bridges the payload to the UAV system by sending and rece
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Perez, Fabian, and Hoover Rueda-Chacon. "Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2024. Journal of LatinX in AI Research, 2024. http://dx.doi.org/10.52591/lxai202406178.

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In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the
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Taylor, Camillo, and Anthony Cowley. "Parsing Indoor Scenes Using RGB-D Imagery." In Robotics: Science and Systems 2012. Robotics: Science and Systems Foundation, 2012. http://dx.doi.org/10.15607/rss.2012.viii.051.

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LaRocque, Armand, Brigitte Leblon, Melanie-Louise Leblanc, and Angela Douglas. "Surveying Migratory Waterfowl using UAV RGB Imagery." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553747.

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Reports on the topic "Imagerie RGB"

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Bhatt, Parth, Curtis Edson, and Ann MacLean. Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS). Michigan Technological University, 2022. http://dx.doi.org/10.37099/mtu.dc.michigantech-p/16366.

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Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we dev
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Shawler, Justin, Aleksandra Ostojic, Joseph Harwood, Christopher Macon, Molly Reif, and Aaron Schad. Geomorphic Monitoring of Coastal Marsh Restoration Sites: Insights from Field and Remote Sensing Approaches in Louisiana, USA. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/48401.

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Restoration of coastal marshes presents unique geomorphic monitoring challenges because these sites are often remote and/or inaccessible, and time and financial resources for field-based geomorphic monitoring may be limited. Yet, the geomorphic trajectory of coastal marshes controls the overall system health and longevity. The expansion of Unmanned Aircraft System (UAS) technology and new satellite platforms offer opportunities to complement ground-based geomorphic monitoring and overcome the challenges associated with traditional field methods. Here, we compare field-based and remote sensing
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Shin, Sang-Yeop, Hanjin Kim, Arnav Goel, Jinha Jung, and Ayman Habib. Development of Web Portal for the Management, Visualization, and Analysis of Collected Mobile LiDAR Data along Indiana’s Transportation Corridors. Purdue University, 2025. https://doi.org/10.5703/1288284317846.

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Efficient road management requires collecting and analyzing various geospatial data related to transportation corridors. Mobile Mapping Systems (MMS), integrated with GNSS/INS units, RGB cameras, and LiDAR technology, enable the collection of geo-tagged imagery and 3D point cloud data across roadway networks. Acquired data helps in the inventory and management of transportation networks. However, users face challenges in accessing high data volumes, especially when considering hardware and software requirements. To address these challenges, the Purdue research team developed a web portal to ef
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.

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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2
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Ley, Matt, Tom Baldvins, David Jones, Hanna Pilkington, and Kelly Anderson. Vegetation classification and mapping: Gulf Islands National Seashore. National Park Service, 2023. http://dx.doi.org/10.36967/2299028.

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The Gulf Islands National Seashore (GUIS) vegetation inventory project classified and mapped vegetation on park-owned lands within the administrative boundary and estimated thematic map accuracy quantitatively. The project began in June 2016. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetin
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