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Artykuły w czasopismach na temat „Imagerie fUS”

Autor: Grafiati

Data publikacji: 28 września 2024

Data aktualizacji: 31 lipca 2025

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1

Schirmer dos Santos, César. "Autenticidade na memória experiencial:." Filosofia Unisinos 26, no. 1 (2025): 1–20. https://doi.org/10.4013/fsu.2025.261.04.

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Neste texto, exploro a questão da acurácia das memórias experienciais, focando na questão do ajuste entre os elementos imagísticos de uma memória experiencial e a experiência passada que é lembrada. Meu ponto de partida é a taxonomia da memória, com vistas a deixar claro que focarei apenas em memórias de longa-duração que envolvem imageria mental. Tendo feito este esclarecimento, início uma discussão sobre requisitos normativos da memória, focando no requisito de que uma lembrança se ajuste ao que antes foi experienciado pelo sujeito. Discuto, então, o modo como este requisit

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Yang, Jie, Jihong Gu, Jingyu Xin, Zhou Cong, and Dazhi Ding. "LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries." Remote Sensing 16, no. 19 (2024): 3741. http://dx.doi.org/10.3390/rs16193741.

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Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is difficult to deploy, while a smaller one sacrifices accuracy. To address this challenge, the novel “LiOSR-SAR” lightweight recognizer is proposed for OSR in SAR imageries. It incorporates the compact attribute focusing and open-prediction modules, which collectively optimize its lightweight s

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Pascual, Javier, Ander Ramos, and Carmen Vidaurre. "Classifying motor imagery with FES induced EEG patterns." Neuroscience Letters 500 (July 2011): e48. http://dx.doi.org/10.1016/j.neulet.2011.05.209.

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Choi, Inchul, Gyu Hyun Kwon, Sangwon Lee, and Chang S. Nam. "Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation." Brain Sciences 10, no. 8 (2020): 512. http://dx.doi.org/10.3390/brainsci10080512.

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Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conduc

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Inalpulat, Melis. "Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov–FLUS Model." Sustainability 16, no. 19 (2024): 8456. http://dx.doi.org/10.3390/su16198456.

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Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predi

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Jahani, Babak, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak. "Algorithm for continual monitoring of fog based on geostationary satellite imagery." Atmospheric Measurement Techniques 18, no. 8 (2025): 1927–41. https://doi.org/10.5194/amt-18-1927-2025.

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Abstract. This study presents an algorithm for the detection of fog and low stratus (FLS) over Europe based on the infrared bands of the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) instrument on board the Meteosat Second Generation geostationary satellites. As the method operates based on the SEVIRI infrared observations only, it is expected to be stationary in time and thus can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle. The algorithm is based on a gradient boosted tree machine learning model that is trained with ground truth ob

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Savic, A., N. Malešević, and M. B. Popovic. "11. Motor imagery based BCI for control of FES." Clinical Neurophysiology 124, no. 7 (2013): e11-e12. http://dx.doi.org/10.1016/j.clinph.2012.12.020.

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Boone, C. D., P. F. Bernath, and M. Lecours. "Version 5 retrievals for ACE-FTS and ACE-imagers." Journal of Quantitative Spectroscopy and Radiative Transfer 310 (December 2023): 108749. http://dx.doi.org/10.1016/j.jqsrt.2023.108749.

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Zhou, Jing, Huawei Mou, Jianfeng Zhou, et al. "Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning." Plant Phenomics 2021 (June 28, 2021): 1–13. http://dx.doi.org/10.34133/2021/9892570.

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Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and bioti

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Riquelme-Aguado, Víctor, Silvia Di-Bonaventura, María Elena González-Álvarez, et al. "How Does Conditioned Pain Modulation Influence Motor Imagery Processes in Women with Fibromyalgia Syndrome? A Cross-Sectional Study Secondary Analysis." Journal of Clinical Medicine 13, no. 23 (2024): 7339. https://doi.org/10.3390/jcm13237339.

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Background/Objectives: Fibromyalgia syndrome (FMS) is a multifactorial pain syndrome not only characterized by widespread pain as the primary symptom but also accompanied by physical, psychological, and cognitive manifestations. Impairments in conditioned pain modulation (CPM) are common in this population; however, there is significant heterogeneity in the CPM response among women with FMS. The Left/Right Judgment Task (LRJT) is a validated method for studying motor imagery in chronic pain patients. Previous scientific evidence has not yet thoroughly investigated the relationship between CPM

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Sari, Dewi Mutiara, Bayu Sandi Marta, Muhammad Amin A, and Haryo Dwito Armono. "The Analysis of Underwater Imagery System for Armor Unit Monitoring Application." International Journal of Artificial Intelligence & Robotics (IJAIR) 5, no. 1 (2023): 1–12. http://dx.doi.org/10.25139/ijair.v5i1.5918.

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The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods.

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Buchholz, Jan, Jan Krieger, Claudio Bruschini, et al. "Widefield High Frame Rate Single-Photon SPAD Imagers for SPIM-FCS." Biophysical Journal 114, no. 10 (2018): 2455–64. http://dx.doi.org/10.1016/j.bpj.2018.04.029.

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Opasatian, Ithiphat, and Tofael Ahamed. "Driveway Detection for Weed Management in Cassava Plantation Fields in Thailand Using Ground Imagery Datasets and Deep Learning Models." AgriEngineering 6, no. 3 (2024): 3408–26. http://dx.doi.org/10.3390/agriengineering6030194.

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Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via mechanical weeders. Automation can support this difficult work for weed management via driveway detection. In this context, deep learning algorithms have the potential to train models to detect driveways through furrow image segmentation. Therefore, the purpose of this research was to develop an ima

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14

Han, Seongkyun, Jisang Yoo, and Soonchul Kwon. "Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery." Sensors 19, no. 18 (2019): 3958. http://dx.doi.org/10.3390/s19183958.

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Vehicle detection is an important research area that provides background information for the diversity of unmanned-aerial-vehicle (UAV) applications. In this paper, we propose a vehicle-detection method using a convolutional-neural-network (CNN)-based object detector. We design our method, DRFBNet300, with a Deeper Receptive Field Block (DRFB) module that enhances the expressiveness of feature maps to detect small objects in the UAV imagery. We also propose the UAV-cars dataset that includes the composition and angular distortion of vehicles in UAV imagery to train our DRFBNet300. Lastly, we p

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Xia, Haiyang, Baohua Yang, Yunlong Li, and Bing Wang. "An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery." Sensors 22, no. 8 (2022): 2850. http://dx.doi.org/10.3390/s22082850.

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For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to imp

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Vavoulis, Athanasios, Patricia Figueiredo, and Athanasios Vourvopoulos. "A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation." Signals 4, no. 1 (2023): 73–86. http://dx.doi.org/10.3390/signals4010004.

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Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been carried out in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them use offline data and are not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback syst

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Ezenwa, K. O., E. O. Iguisi, Y. O. Yakubu, and M. Ismail. "A SCS-CN TECHNIQUE FOR GEOSPATIAL ESTIMATION OF RUNOFF PEAK DISCHARGE IN THE KUBANNI DRAINAGE BASIN, ZARIA, NIGERIA." FUDMA JOURNAL OF SCIENCES 6, no. 1 (2022): 314–22. http://dx.doi.org/10.33003/fjs-2022-0601-901.

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The problem of soil loss is becoming widespread due to increasing unwholesome land use practices and population pressure on limited landscape. This study employed the integration of satellite imageries, rainfall and soil data and modern GIS technology to estimate runoff peak discharge in the Kubanni drainage basin. Some of the contributions of this study include the determination of the Hydrologic Soil Group (HSG) and Soil Conservation Service Curve Number (SCS CN) for the Kubanni drainage basin with a view to investigating runoff peak discharge using geospatial technology. Satellite images of

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Ijafiya, Danjuma Jijuwa, U. Y. Abubakar, and A. B. Liman. "PERCEPTIONS ON THE IMPACTS OF MORPHOLOGICAL CHANGES ON THE LOWER COURSE OF RIVER MAYO-INNE, YOLA SOUTH, ADAMAWA STATE, NIGERIA." FUDMA JOURNAL OF SCIENCES 7, no. 3 (2023): 146–51. http://dx.doi.org/10.33003/fjs-2023-0703-1858.

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Rivers are important natural resources that support the existence of humans and other living organisms right from time immemorial. Despite their significance to human livelihood; changes in their morphology can impact on the socio-economic, cultural and environmental values of the riparian environment. Therefore, this study focused on the perception of the impacts of morphological changes on the lower course of River Mayo-Inne, Yola South, Adamawa State, Nigeria. This was done with the view to examine the perceived impacts of morphological changes on riparian land uses; factors influencing mor

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Ren, Shixin, Weiqun Wang, Zeng-Guang Hou, Xu Liang, Jiaxing Wang, and Weiguo Shi. "Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 8 (2020): 1846–55. http://dx.doi.org/10.1109/tnsre.2020.3001990.

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Tai, Xuchuan, and Xinjun Zhang. "LMEC-YOLOv8: An Enhanced Object Detection Algorithm for UAV Imagery." Electronics 14, no. 13 (2025): 2535. https://doi.org/10.3390/electronics14132535.

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Despite the rapid development of UAV (Unmanned Aerial Vehicle) technology, its application for object detection in complex scenarios faces challenges regarding the small target sizes and environmental interference. This paper proposes an improved algorithm, LMEC-YOLOv8, based on YOLOv8n, which aims to enhance the detection accuracy and real-time performance of UAV imagery for small targets. We propose three key enhancements: (1) a lightweight multi-scale module (LMS-PC2F) to replace C2f; (2) a multi-scale attention mechanism (MSCBAM) for optimized feature extraction; and (3) an adaptive pyrami

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Shi, Wenxu, Qingyan Meng, Linlin Zhang, Maofan Zhao, Chen Su, and Tamás Jancsó. "DSANet: A Deep Supervision-Based Simple Attention Network for Efficient Semantic Segmentation in Remote Sensing Imagery." Remote Sensing 14, no. 21 (2022): 5399. http://dx.doi.org/10.3390/rs14215399.

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Semantic segmentation for remote sensing images (RSIs) plays an important role in many applications, such as urban planning, environmental protection, agricultural valuation, and military reconnaissance. With the boom in remote sensing technology, numerous RSIs are generated; this is difficult for current complex networks to handle. Efficient networks are the key to solving this challenge. Many previous works aimed at designing lightweight networks or utilizing pruning and knowledge distillation methods to obtain efficient networks, but these methods inevitably reduce the ability of the result

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Boone, C. D., P. F. Bernath, D. Cok, S. C. Jones, and J. Steffen. "Version 4 retrievals for the atmospheric chemistry experiment Fourier transform spectrometer (ACE-FTS) and imagers." Journal of Quantitative Spectroscopy and Radiative Transfer 247 (May 2020): 106939. http://dx.doi.org/10.1016/j.jqsrt.2020.106939.

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Oh, Dong Sik, and Jong Duk Choi. "Effects of Motor Imagery Training on Balance and Gait in Older Adults: A Randomized Controlled Pilot Study." International Journal of Environmental Research and Public Health 18, no. 2 (2021): 650. http://dx.doi.org/10.3390/ijerph18020650.

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The aim of this study was to demonstrate the effects of motor imagery training on balance and gait abilities in older adults and to investigate the possible application of the training as an effective intervention against fall prevention. Subjects (n = 34) aged 65 years and over who had experienced falls were randomly allocated to three groups: (1) motor imagery training group (MITG, n = 11), (2) task-oriented training group (TOTG, n = 11), and (3) control group (CG, n = 12). Each group performed an exercise three times a week for 6 weeks. The dependent variables included Path Length of center

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Niu, Shanwei, Zhigang Nie, Guang Li, and Wenyu Zhu. "Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning." Drones 8, no. 5 (2024): 198. http://dx.doi.org/10.3390/drones8050198.

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In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs)

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Kamlun, Kamlisa U., and Mui-How Phua. "Anthropogenic influences on deforestation of a peat swamp forest in Northern Borneo using remote sensing and GIS." Forest Systems 33, no. 1 (2024): eSC02. http://dx.doi.org/10.5424/fs/2024331-20585.

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Aim of study: To study the anthropogenic factors that influence the fire occurrences in a peat swamp forest (PSF) in the northern part of Borneo Island. Area of study: Klias Peninsula, Sabah Borneo Island, Malaysia. Material and methods: Supervised classification using the maximum likelihood algorithm of multitemporal satellite imageries from the mid-80s to the early 20s was used to quantify the wetland vegetation change on Klias Peninsula. GIS-based buffering analysis was made to generate three buffer zones with distances of 1000 m, 2000 m, and 3000 m based on each of three anthro

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Bernini, Marco. "Affording innerscapes: Dreams, introspective imagery and the narrative exploration of personal geographies." Frontiers of Narrative Studies 4, no. 2 (2018): 291–311. http://dx.doi.org/10.1515/fns-2018-0024.

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AbstractThe essay presents an interdisciplinary theory of what it will call “innerscapes”: artefactual representations of the mind as a spatially extended world. By bringing examples of innerscapes from literature (Kafka’s short story The Bridge), radio plays (Samuel Beckett’s Embers), and a creative documentary about auditory-verbal hallucinations (a voice-hearer’s short film, Adam + 1), it suggests that these spatial renditions of the mind are constructed by transforming the quasi-perceptual elements of inner experience into affording ecologies. In so doing, they enable an enactive explorati

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Mukhopadhyay, Anirban, and Ujjwal Maulik. "Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach." Fundamenta Informaticae 86, no. 4 (2008): 411–28. https://doi.org/10.3233/fun-2008-86402.

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A popular approach for landcover classification in remotely sensed satellite images is clustering the pixels in the spectral domain into several fuzzy partitions. It has been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. Motivated by this observation, in this article a two-stage fuzzy clustering algorithm is described that utilizes the concept of points having significant membership to multiple classes. The points situated in the overlapped regions of different clusters are first identified and excluded from consideration while

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Bell, K. "Literature and Painting in Quebec: From Imagery to Identity." French Studies 67, no. 3 (2013): 448–49. http://dx.doi.org/10.1093/fs/knt123.

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Fan, Xijian, Fan Lei, and Kun Yang. "Real-Time Detection of Smoke and Fire in the Wild Using Unmanned Aerial Vehicle Remote Sensing Imagery." Forests 16, no. 2 (2025): 201. https://doi.org/10.3390/f16020201.

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Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables rapid and accurate identification. This paper presents an integrated one-stage object detection framework designed for the simultaneous identification of wildfires and smoke in UAV imagery. By leveraging mixed data augmentation techniques, the framework enriches the dataset with small targets

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Balamuralidhar, Navaneeth, Sofia Tilon, and Francesco Nex. "MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms." Remote Sensing 13, no. 4 (2021): 573. http://dx.doi.org/10.3390/rs13040573.

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We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom datase

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Liu, Ye, Mingfen Li, Hao Zhang, et al. "A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training." Journal of Neuroscience Methods 222 (January 2014): 238–49. http://dx.doi.org/10.1016/j.jneumeth.2013.11.009.

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Tilby, M. "Imagery and Ideology: Fiction and Painting in Nineteenth-Century France." French Studies 63, no. 1 (2009): 103–4. http://dx.doi.org/10.1093/fs/knn199.

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Almanza Sepúlveda, Mayra Linné, Julio Llamas Alonso, Miguel Angel Guevara, and Marisela Hernández González. "Increased Prefrontal-Parietal EEG Gamma Band Correlation during Motor Imagery in Expert Video Game Players." Actualidades en Psicología 28, no. 117 (2014): 27–36. http://dx.doi.org/10.15517/ap.v28i117.14095.

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Abstract. The aim of this study was to characterize the prefrontal-parietal EEG correlation in experienced video game players (VGPs) in relation to individuals with little or no video game experience (NVGPs) during a motor imagery condition for an action-type video game. The participants in both groups watched a first-person shooter (FPS) gameplay from Halo Reach during five minutes. None of the participants was notified as to the content of the video before watching it. Only the VGPs showed an increased right intrahemispheric prefrontal-parietal correlation (F4-P4) in the gamma band (31-50 Hz

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Li, Shouliang, Jiale Han, Fanghui Chen, Rudong Min, Sixue Yi, and Zhen Yang. "Fire-Net: Rapid Recognition of Forest Fires in UAV Remote Sensing Imagery Using Embedded Devices." Remote Sensing 16, no. 15 (2024): 2846. http://dx.doi.org/10.3390/rs16152846.

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Forest fires pose a catastrophic threat to Earth’s ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model’s ability to perceive features at various scales, particularly small-scale fire sources in wild fore

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Troscianko, Emily T. "Samuel Beckett and Experimental Psychology: Perception, Attention, Imagery. By Joshua Powell." French Studies 75, no. 2 (2021): 283–84. http://dx.doi.org/10.1093/fs/knab002.

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BROWN, C. "Review. Building Resemblance: Analogical Imagery in the Early French Renaissance. Randall, Michael." French Studies 52, no. 3 (1998): 330. http://dx.doi.org/10.1093/fs/52.3.330.

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FOLKIERSKA, A. "Review. Bird Imagery in the Lyric Poetry of Tristan l'Hermite. Belcher, Margaret." French Studies 44, no. 1 (1990): 57. http://dx.doi.org/10.1093/fs/44.1.57.

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Li, Hui, Jianbo Ma, and Jianlin Zhang. "ELNet: An Efficient and Lightweight Network for Small Object Detection in UAV Imagery." Remote Sensing 17, no. 12 (2025): 2096. https://doi.org/10.3390/rs17122096.

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Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object detection model based on YOLOv12n. First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. Second, we propose a novel lightweight detection head, EPGHead, to alleviat

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Qiu, Yue, Fang Wu, Jichong Yin, Chengyi Liu, Xianyong Gong, and Andong Wang. "MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery." Remote Sensing 14, no. 16 (2022): 3914. http://dx.doi.org/10.3390/rs14163914.

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There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to signific

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Rodríguez-Puerta, Francisco, Carlos Barrera, Borja García, Fernando Pérez-Rodríguez, and Angel M. García-Pedrero. "Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery." Sensors 22, no. 9 (2022): 3269. http://dx.doi.org/10.3390/s22093269.

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Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have c

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Rodríguez-Puerta, Francisco, Carlos Barrera, Borja García, Fernando Pérez-Rodríguez, and Angel M. García-Pedrero. "Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery." Sensors 22, no. 9 (2022): 3269. http://dx.doi.org/10.3390/s22093269.

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Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have c

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Estupinan-Suarez, L. M., C. Florez-Ayala, M. J. Quinones, A. M. Pacheco, and A. C. Santos. "Detection and characterizacion of Colombian wetlands using Alos Palsar and MODIS imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 375–82. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-375-2015.

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Wetlands regulate the flow of water and play a key role in risk management of extreme flooding and drought. In Colombia, wetland conservation has been a priority for the government. However, there is an information gap neither an inventory nor a national baseline map exists. In this paper, we present a method that combines a wetlands thematic map with remote sensing derived data, and hydrometeorological stations data in order to characterize the Colombian wetlands. Following the adopted definition of wetlands, available spatial data on land forms, soils and vegetation was integrated in order t

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Luo, Yiyun, Jinnian Wang, Xiankun Yang, Zhenyu Yu, and Zixuan Tan. "Pixel Representation Augmented through Cross-Attention for High-Resolution Remote Sensing Imagery Segmentation." Remote Sensing 14, no. 21 (2022): 5415. http://dx.doi.org/10.3390/rs14215415.

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Natural imagery segmentation has been transferred to land cover classification in remote sensing imagery with excellent performance. However, two key issues have been overlooked in the transfer process: (1) some objects were easily overwhelmed by the complex backgrounds; (2) interclass information for indistinguishable classes was not fully utilized. The attention mechanism in the transformer is capable of modeling long-range dependencies on each sample for per-pixel context extraction. Notably, per-pixel context from the attention mechanism can aggregate category information. Therefore, we pr

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Xu, Haiqing, Mingyang Yu, Fangliang Zhou, and Hongling Yin. "Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer." Applied Sciences 14, no. 5 (2024): 1986. http://dx.doi.org/10.3390/app14051986.

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Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object

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Saha, Sriparna, and Sanghamitra Bandyopadhyay. "Fuzzy Symmetry Based Real-Coded Genetic Clustering Technique for Automatic Pixel Classification in Remote Sensing Imagery." Fundamenta Informaticae 84, no. 3-4 (2008): 471–92. https://doi.org/10.3233/fun-2008-843-411.

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The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes presents a challenging task. In this paper, a newly developed real-coded variable string length genetic fuzzy clustering technique with a new point symmetry distance is used for this purpose. The proposed

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Zhang, Mingjin, Yingfeng Zhu, Longyi Li, Jie Guo, Zhengkun Liu, and Yunsong Li. "S4Det: Breadth and Accurate Sine Single-Stage Ship Detection for Remote Sense SAR Imagery." Remote Sensing 17, no. 5 (2025): 900. https://doi.org/10.3390/rs17050900.

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Synthetic Aperture Radar (SAR) is a remote sensing technology that can realize all-weather and all-day monitoring, and it is widely used in ocean ship monitoring tasks. Recently, many oriented detectors were used for ship detection in SAR images. However, these methods often found it difficult to balance the detection accuracy and speed, and the noise around the target in the inshore scene of SAR images led to a poor detection network performance. In addition, the rotation representation still has the problem of boundary discontinuity. To address these issues, we propose S4Det, a Sinusoidal Si

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Nikfar, Maryam, Mohammad Zoej, Mehdi Mokhtarzade, and Mahdi Shoorehdeli. "Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery." Remote Sensing 7, no. 7 (2015): 8271–99. http://dx.doi.org/10.3390/rs70708271.

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Zhang, Zhiqi, Wendi Xia, Guangqi Xie, and Shao Xiang. "Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network." Drones 7, no. 9 (2023): 559. http://dx.doi.org/10.3390/drones7090559.

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Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. However, targets often appear occluded and confused, and it is difficult for existing detectors to accurately detect poppies. To address this problem, we propose an opium poppy detection network, YOLOHLA, for UAV remote sensing images. Specifically, we propose a new attention module that uses two branches to extract features at different scales. To enhance generalization capabilities, we introduce a learni

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Nkrumah, Isaac, Maryam Moshrefizadeh, Omar Tahri, Erik Blasch, Kannappan Palaniappan, and Hadi AliAkbarpour. "EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery." Sensors 24, no. 23 (2024): 7493. http://dx.doi.org/10.3390/s24237493.

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In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-ligh

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Cheng, Jijie, Yi Liu, and Xiaowei Li. "Coal Mine Rock Burst and Coal and Gas Outburst Perception Alarm Method Based on Visible Light Imagery." Sustainability 15, no. 18 (2023): 13419. http://dx.doi.org/10.3390/su151813419.

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To solve the current reliance of coal mine rock burst and coal and gas outburst detection on mainly manual methods and the problem wherein it is still difficult to ensure disaster warning required to meet the needs of coal mine safety production, a coal mine rock burst and coal and gas outburst perception alarm method based on visible light imagery is proposed. Real-time video images were collected by color cameras in key areas of underground coal mines; the occurrence of disasters was determined by noting when the black area of a video image increases greatly, when the average brightness is l

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