Bibliographies thématiques / Real time segmentation and labeling algorithm

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Littérature scientifique sur le sujet « Real time segmentation and labeling algorithm »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Real time segmentation and labeling algorithm"

Danilov, V. V., O. M. Gerget, D. Y. Kolpashchikov, et al. "BOOSTING SEGMENTATION ACCURACY OF THE DEEP LEARNING MODELS BASED ON THE SYNTHETIC DATA GENERATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 33–40. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-33-2021.

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Abstract. In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The con

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Jin, Ran, Xiaozhen Han, and Tongrui Yu. "A Real-Time Image Semantic Segmentation Method Based on Multilabel Classification." Mathematical Problems in Engineering 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/9963974.

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Image semantic segmentation as a kind of technology has been playing a crucial part in intelligent driving, medical image analysis, video surveillance, and AR. However, since the scene needs to infer more semantics from video and audio clips and the request for real-time performance becomes stricter, whetherthe single-label classification method that was usually used before or the regular manual labeling cannot meet this end. Given the excellent performance of deep learning algorithms in extensive applications, the image semantic segmentation algorithm based on deep learning framework has been

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Tenze, Livio, and Enrique Canessa. "NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-Care Health." Sensors 24, no. 24 (2024): 7997. https://doi.org/10.3390/s24247997.

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Visual examination of nails can reflect human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking at the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling. The NAILS leverages a pre-trained Convolutional Neural Network model to segment and label fingernail regions from fingernail images, normalizing RGB values to monitor tiny color changes via a GUI and the use of an HD webcam in real time. The use of normalized RGB values combined with AI-based segmentation for

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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.

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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this,

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Lessani, Mohammad Naser, Jiqiu Deng, and Zhiyong Guo. "A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem." ISPRS International Journal of Geo-Information 10, no. 12 (2021): 826. http://dx.doi.org/10.3390/ijgi10120826.

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Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept

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Jung, Minwoo, and Dae-Young Kim. "Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture." Applied Sciences 14, no. 22 (2024): 10371. http://dx.doi.org/10.3390/app142210371.

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This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the l

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Liu, Ning, Gang Liu, and Hong Sun. "Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System." Sensors 20, no. 12 (2020): 3430. http://dx.doi.org/10.3390/s20123430.

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In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additional

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Ji, Xing, Jia Yuan Zhuang, and Yu Min Su. "Marine Radar Target Detection for USV." Advanced Materials Research 1006-1007 (August 2014): 863–69. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.863.

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Unmanned surface vehicles (USV) have become an intense research area because of their extensive applications. Marine radar is the most important environmental perception sensor for USV. Aiming at the problems of noise jamming, uneven brightness, target lost in marine radar images, and the high-speed USV to the requirement of real-time and reliability, this paper proposes the radar image target detection algorithms which suitable for embedded marine radar target detection system. The smoothing algorithm can adaptive select filter in noise, border and background areas, improves the efficiency an

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Faria Júnior, Clodoaldo de Souza, Milton Hirokazu Shimabukuro, Antonio Maria Garcia Tommaselli, Marcos Ricardo Omena de Albuquerque Maximo, Letícia Rosim Porto, and Nilton Nobuhiro Imai. "Real-Time Leaves Segmentation in RGB Images with Deep Learning in a Single-Board Computer." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-3-2024 (November 4, 2024): 139–46. http://dx.doi.org/10.5194/isprs-annals-x-3-2024-139-2024.

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Abstract. This work proposed and evaluated methods for real-time leaf segmentation using a single-board computer. The main aim was to explore the state-of-the-art techniques based on the YOLO algorithm for real-time operation. For this purpose, the available variants of YOLOv8 and YOLOv9 were evaluated, and a semi-automatic labelling method based on the Segment Anything Model (SAM) algorithm was used. Given the need to delimit the leaf contour for labelling, it was possible to create a larger and more accurate dataset compared to the purely manual procedure. In addition, the cost-benefit of th

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Thèses sur le sujet "Real time segmentation and labeling algorithm"

Abate, Francesco. "Innovative algorithms and data structures for signal treatment applied to ISO/IEC/IEEE 21451 smart transducers." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2493.

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2014 - 2015 Technologies and, in particular sensors, permeate more and more application sectors. From energy management, to the factories one, to houses, environments, infrastructure, and building monitoring, to healthcare and traceability systems, sensors are more and more widespread in our daily life. In the growing context of the Internet of Things capabilities to acquire magnitudes of interest, to elaborate and to communicate data is required to these technologies. These capabilities of acquisition, elaboration, and communication can be integrated on a unique device, a smart sensor

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Chiou, Yung-Chuen, and 邱永椿. "The Study on Real-Time Video Object Segmentation Algorithm Based On Change Detection and Background Updating." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/72169261118433490566.

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碩士 國立高雄應用科技大學 電子與資訊工程研究所碩士班 93 Video object segmentation is key role for developing technique of content-based video processing. In practically, it can be implemented in pre-processing for contend-based video system in order to separate the video frame into video objects. Many proposed video segmentation algorithms which are aimed at specific sequence, e.g., shoulder-head sequence, or need an absolute background frame. Besides, the higher computational burden is requested because the complex operator is used in spatial domain. However, there restrictions hardly make it to be involv

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Yeh, Ruei-Cheng, and 葉睿誠. "Real-Time Processing Of Multiple Source Segmentation and Separation Using MUSIC Algorithm with Calibrated Array Manifold Vector." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/11584968399298425897.

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碩士 國立交通大學 工學院聲音與音樂創意科技碩士學位學程 104 A real-time system structure for multiple sound sources segmentation and separation using Multiple Signal Classification algorithm is proposed in this thesis. Using a calibrated array manifold vector, the proposed calibration method improves the accuracy of the MUSIC algorithm for wide-band detections, hence providing high accuracy source segmentation and separation results. And system structure using the Multiple Signal Classification algorithm to detect and estimate the localization of sound source’s spectrum distribution. And then using probabili

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Han-ChangChen and 陳漢昌. "Real-Time Human Position Tracking and Gesture Recognition System Based on Image Segmentation Algorithm and Its Application to Image Browser." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/65525731598906370826.

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碩士 國立成功大學 工程科學系碩博士班 101 Abstract Human vision is one of our most advanced senses; therefore, image for the human’s sense is very important. Along with the rapid improvement in the development of computer technology and execution speed, image processing techniques have also matured. However, in the past, positioning cameras have been used nearly exclusively for detecting and tracking moving objects. If the moving objects move outside the lens’ view area, it can not be tracked. In order to improve this weakness and reduce blind spots, this thesis proposes a real-time object tracking

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Chapitres de livres sur le sujet "Real time segmentation and labeling algorithm"

Jau, U. L., and C. S. Teh. "Real-Time Object-Based Video Segmentation Using Colour Segmentation and Connected Component Labeling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05036-7_12.

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Hao, Zhifeng, Wen Wen, Zhou Liu, and Xiaowei Yang. "Real-Time Foreground-Background Segmentation Using Adaptive Support Vector Machine Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_62.

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Leclercq, Philippe, and Thomas Bräunl. "A Color Segmentation Algorithm for Real-Time Object Localization on Small Embedded Systems." In Robot Vision. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44690-7_9.

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Ishii, Shun, Kizito Nkurikiyeyezu, Mika Luimula, Anna Yokokubo, and Guillaume Lopez. "ExerSense: Real-Time Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor." In Smart Innovation, Systems and Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8944-7_15.

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Ding, Yuhua, George J. Vachtsevanos, Anthony J. Yezzi, Wayne Daley, and Bonnie S. Heck-Ferri. "A Real-Time Multisensory Image Segmentation Algorithm with an Application to Visual and X-Ray Inspection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36592-3_19.

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Eun, Jung, Jeonghyo Ha, Sung Hyun Baek, Sangkeun Moon, and Junmo Kim. "U-Net-Based Segmentation for Electrical Lines and Its Application to Real-Time Maintenance Algorithm for Electricity Facilities." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4803-8_38.

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Zhang, Junxiong, Yu Zhang, Jinyi Xie, et al. "Panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_18.

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Abstract In modern greenhouses, complicated tasks and unstructured environments generate the imperious demand for advanced semantic information about each object at work scenes. A significant problem that mainstream methods intend to resolve is that the refinement and understanding of environmental information cannot efficiently cover the entire task in real time. Therefore, this paper proposes a panoptic semantic mapping method to identify each object that is supposed to be concerned in greenhouses. This method builds grid maps with advanced semantic information based on RGB and depth images.

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Kari, Narmada, Sanjay Kumar Singh, and Dumpala Shanthi. "Machine Learning Techniques in Image Segmentation." In Handbook of Artificial Intelligence. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124514123010009.

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Image is an important medium to express information easily. This paper deals with the content of image segmentation with machine learning. Segmentation is the process of extracting the information required from the image. Machine learning is the process that helps to classify to obtain good results. A number of algorithms are designed for the segmentation process. The algorithms are selected based on the application. Quality segmentation can be applied if the algorithm is fixed at the application level. Standalone methods can be used for real-time applications. Schematic segmentation is one of

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Bagla, Kartikay, Amogh Dhar Diwan, and Kshitij Agarwal. "DarthYOLO: Using YOLO for Real-Time Image Segmentation." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220794.

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Image segmentation is a common use case of image processing. It enables a wide variety of activities, from self-driving cars to traffic systems that are capable of governing them. Most state of the art models like Mask R-CNN and other transformer-based models perform well but have very high inference times. Therefore in this paper, a novel model architecture is proposed that can run on consumer grade hardware while giving near real time image segmentation. This is done by creating masks on regions of interest proposed by a lightweight object detection algorithm that is the YOLOv4. Such an algo

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Casaburi D., D'Amore L., Marcellino L., and Murli A. "Real time ultrasound image sequence segmentation on multicores." In Advances in Parallel Computing. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-530-3-185.

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Echography (ultrasound imaging of the heart) is one of the driving application areas of medical imaging. Our focus is to track the motion of endocardium during a complete cardiac cycle to allow clinicians for estimating the left ventricle and atrium (LVA) area deformations. The main challenge is to do this in a suitable response time. We describe a PETSc-based parallel software designed to detect and delineate the LV contour during a complete cardiac cycle. LV segmentation is performed by applying a three steps algorithm: speckle reduction, optic flow computation and spatio-temporal level-set

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Actes de conférences sur le sujet "Real time segmentation and labeling algorithm"

Raponi, Antonello, and Zoltan Nagy. "CompArt: Next-Generation Compartmental Models for Complex Systems Powered by Artificial Intelligence." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.186609.

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Compartmental models are widely used to simplify the analysis of complex fluid dynamics systems, yet subjective compartment definitions and computational constraints often limit their applicability. The CompArt algorithm introduces an AI-driven framework that automates compartmentalization in Computational Fluid Dynamics (CFD) simulations, optimizing both accuracy and efficiency. By leveraging unsupervised clustering techniques such as Agglomerative Clustering, CompArt identifies coherent flow regions based on velocity and turbulent kinetic energy dissipation rate, ensuring a data-driven, phys

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Abate, F., V. Paciello, A. Pietrosanto, and G. Monte. "Preliminary analysis of a real time segmentation and labeling algorithm." In 2015 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems (EESMS). IEEE, 2015. http://dx.doi.org/10.1109/eesms.2015.7175880.

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Abate, F., A. Pietrosanto, V. Paciello, V. Huang, and G. Monte. "Uncertainty of a real time segmentation and labeling algorithm in signal period measurement." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7744988.

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Atapattu, Sachithra, Narmada Balasooriya, Awantha Jayasiri, Oscar Silva, Raymond Gosine, and George Mann. "Landing Zone Identification Using A Hardware-accelerated Deep Learning Module." In Vertical Flight Society 77th Annual Forum & Technology Display. The Vertical Flight Society, 2021. http://dx.doi.org/10.4050/f-0077-2021-16862.

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This work develops a deep learning-based autonomous Landing Zone (LZ) identification module for a Vertical TakeOff and Landing (VTOL) drone using colored Light Detection and Ranging (LiDAR) point cloud data. "ConvPoint", a top-performing neural network (NN) architecture of the Semantic3D.net pointcloud segmentation benchmark leaderboard, was chosen as the reference architecture for the development. A classification method based on the terrain geometry characteristics is used for automatic labeling of the datasets followed by manual adjustment of label through visual observation. The automatic

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Choi, HyeOk, Yong-Suk Park, and Kyung-Taek Lee. "Re-Labeling for Real-time Semantic Segmentation in Specific Environments." In 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2020. http://dx.doi.org/10.1109/icce-asia49877.2020.9276789.

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Deshpande, Sourabh, Manish Raj Aryal, and Sam Anand. "Deep Learning-Based Recognition of Manufacturing Components Using Augmented Reality for Worker Training of Assembly Tasks." In ASME 2024 19th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/msec2024-125279.

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Abstract Current smart manufacturing computational tools and optimization methods leverage advances from artificial intelligence (AI), augmented / mixed/virtual reality, and predictive modeling to meet factory production goals. With significant developments in computer graphics tools, deep learning methods, and immersive experiences (metaverse), there is a need to study its seamless adaptation to AR-based manufacturing-specific scenarios, especially for worker training. In this work, we develop an augmented reality-based worker training framework aimed at helping novice technicians with comple

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Gong, Wei, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo, and Freddy Chong Tat Chua. "In-game action list segmentation and labeling in real-time strategy games." In 2012 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2012. http://dx.doi.org/10.1109/cig.2012.6374150.

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Zhu, Song, Danhua Cao, Yubin Wu, and Shixiong Jiang. "A novel real-time superpixel segmentation algorithm." In International Conference on Optical Instruments and Technology (OIT2013), edited by Xinggang Lin and Jesse Zheng. SPIE, 2013. http://dx.doi.org/10.1117/12.2036679.

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Liu, Hanyu, Hongying Zhang, Junwen Li, and Yujun He. "Global Feature-Guided Real-Time Semantic Segmentation Algorithm." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904211.

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Gonzalez-Sosa, E., G. Robledo, D. Gonzalez-Morin, P. Perez-Garcia, and A. Villegas. "Real Time Egocentric Object Segmentation for Mixed Reality: THU-READ Labeling and Benchmarking Results." In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2022. http://dx.doi.org/10.1109/vrw55335.2022.00048.

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