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Articles de revues 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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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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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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Baskaran, S., L. Mubark Ali, A. Anitharani, E. Annal Sheeba Rani, and N. Nandhagopal. "Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5364–67. http://dx.doi.org/10.1166/jctn.2020.9429.

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Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one

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Yao, Yadong, Yurui Zhang, Zai Liu, and Heming Yuan. "A Bridge Crack Segmentation Algorithm Based on Fuzzy C-Means Clustering and Feature Fusion." Sensors 25, no. 14 (2025): 4399. https://doi.org/10.3390/s25144399.

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In response to the limitations of traditional image processing algorithms, such as high noise sensitivity and threshold dependency in bridge crack detection, and the extensive labeled data requirements of deep learning methods, this study proposes a novel crack segmentation algorithm based on fuzzy C-means (FCM) clustering and multi-feature fusion. A three-dimensional feature space is constructed using B-channel pixels and fuzzy clustering with c = 3, justified by the distinct distribution patterns of these three regions in the image, enabling effective preliminary segmentation. To enhance acc

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Zhong, Zihan, Shu Lv, and Kaibo Shi. "A New Method of Time-Series Event Prediction Based on Sequence Labeling." Applied Sciences 13, no. 9 (2023): 5329. http://dx.doi.org/10.3390/app13095329.

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In the existing research on time-series event prediction (TSEP) methods, most of the work is focused on improving the algorithm for classifying subsequence sets (sets composed of multiple adjacent subsequences). However, these prediction methods ignore the timing dependence between the subsequence sets, nor do they capture the mutual transition relationship between events, the prediction effect on a small sample data set is very poor. Meanwhile, the sequence labeling problem is one of the common problems in natural language processing and image segmentation. To solve this problem, this paper p

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Daneshwari, Mulimani, and Makandar Aziz. "Sports Video Annotation and Multi-Target Tracking using Extended Gaussian Mixture model." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 1 (2021): 1–6. https://doi.org/10.35940/ijrte.A5589.0510121.

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Video offers solutions to many of the traditional problems with coach, trainer, commenter, umpires and other security issues of modern team games. This paper presents a novel framework to perform player identification and tracking technique for the sports (Kabaddi) with extending the implementation towards the event handling process which expands the game analysis of the third umpire assessment. In the proposed methodology, video preprocessing has done with Kalman Filtering (KF) technique. Extended Gaussian Mixture Model (EGMM) implemented to detect the object occlusions and player labeling. M

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Li, Yiming, Yize Wang, Liuwei Lu, Yiran Guo, and Qi An. "Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow." Applied Sciences 14, no. 13 (2024): 5792. http://dx.doi.org/10.3390/app14135792.

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Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on the ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. Second, the static environment occluded by the dynami

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Woodward-Greene, M. Jennifer, Jason M. Kinser, Tad S. Sonstegard, Johann Sölkner, Iosif I. Vaisman, and Curtis P. Van Tassell. "PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements." PLOS ONE 17, no. 10 (2022): e0275821. http://dx.doi.org/10.1371/journal.pone.0275821.

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Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to addre

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Ramesh, Dr M. R. Raja, P. Mamatha, P. Viswanadha Pavan Varma, S. Wasim Akram, and T. Chandu. "Harnessing Deep Learning for Video Based Weapon Detection." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 45 (August 3, 2024): 30–40. http://dx.doi.org/10.55529/jaimlnn.45.30.40.

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This research addresses the escalating global issue of handgun-related crimes by proposing an innovative Intelligent Video Surveillance System (IVSS) that leverages advanced deep learning (DL) techniques for remote firearm detection and timely threat response. The system employs Convolutional Neural Networks (CNN) and the YOLO v3 model, uniquely integrating Transfer Learning (TL) to enhance adaptability and efficacy. Experimental validation using the Internet Movie Firearms Database (IMFDB) demonstrates the system's versatility in detecting various pistols and guns, achieving promising results

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Wang, Haoyu, Zhiming Ye, Dejiang Wang, Jiongyi Zhu, Haili Jiang, and Panpan Liu. "Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN." Buildings 13, no. 3 (2023): 585. http://dx.doi.org/10.3390/buildings13030585.

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The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in construction quality inspection and intelligent construction; it can check the number, spacing, and diameter of rebar on a construction site, and guide robots to complete rebar tying. However, the application of deep learning algorithms relies on a large number of datasets to train models, while manual data collection and annotation are time-consuming and la

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Gagliardi, Alessio, and Sergio Saponara. "AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems." Energies 13, no. 8 (2020): 2098. http://dx.doi.org/10.3390/en13082098.

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This paper proposes a video-based smoke detection technique for early warning in antifire surveillance systems. The algorithm is developed to detect the smoke behavior in a restricted video surveillance environment, both indoor (e.g., railway carriage, bus wagon, industrial plant, or home/office) or outdoor (e.g., storage area or parking area). The proposed technique exploits a Kalman estimator, color analysis, image segmentation, blob labeling, geometrical features analysis, and M of N decisor, in order to extract an alarm signal within a strict real-time deadline. This new technique requires

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Zhan, Wenqiang, Changshi Xiao, Yuanqiao Wen, et al. "Autonomous Visual Perception for Unmanned Surface Vehicle Navigation in an Unknown Environment." Sensors 19, no. 10 (2019): 2216. http://dx.doi.org/10.3390/s19102216.

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Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samp

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Cheng, Zhenzhen, Lijun Qi, and Yifan Cheng. "Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds." Agriculture 11, no. 5 (2021): 431. http://dx.doi.org/10.3390/agriculture11050431.

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Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vital for precision pesticide application. However, the extant studies do not provide an efficient and reliable method of extracting individual trees with irregular tree-crown shapes and complicated backgrounds. This paper on our study proposes a Mahalanobis distance and conditional random field (CRF)-based segmentation model to extract cherry trees a

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Peng, Yun. "Deeplabv3+ for extracting Enteromorpha prolifera from drone images." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 29–38. http://dx.doi.org/10.54097/hset.v56i.9813.

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The coastal waters of Qingdao have been invaded by Enteromorpha proliferata for 15 consecutive years, causing enormous economic losses and becoming one of the hot spots in marine ecology research in China in recent years. At present, the method used for extracting Enteromorpha prolifera commercially is still manual labeling, which consumes labor and time. In addition, although in-depth learning has developed rapidly in the field of image semantics segmentation, the irregularity of Enteromorpha proliferata and the highly affine transformation brought by the UAV shooting perspective will reduce

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Wang, Zehui, Yu Meng, Jingbo Chen, Junxian Ma, Anzhi Yue, and Jiansheng Chen. "Learning Color Distributions from Bitemporal Remote Sensing Images to Update Existing Building Footprints." Remote Sensing 14, no. 22 (2022): 5851. http://dx.doi.org/10.3390/rs14225851.

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For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time, and the area of change is variable; hence, automated methods for generating up-to-date building footprints are urgently needed. However, the labels of current buildings or changed areas are usually lacking, and the conditions for acquiring images from different periods are not perfectly consistent, which can severely limit deep learning methods whe

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Wang, Tao, Juanli Wang, Jia Zhao, and Yanmin Zhang. "A Myocardial Segmentation Method Based on Adversarial Learning." BioMed Research International 2021 (February 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/6618918.

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Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary wildly, with mild case patients having no obvious symptoms and serious cases being potentially life-threatening. Using cardiovascular magnetic resonance imaging (CMRI) technology to create a patient-specific 3D heart model is an important prerequisite for surgical planning in children with CHD. Manually segmenting 3D images using existing tools is time-consuming and laborious, which greatly hinders the routine clinical application of 3D heart mo

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Tao, Ming, Ziheng Xiao, Yulong Liu, Lei Huang, Gongliang Xiang, and Yuanquan Xu. "A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision." Applied Sciences 15, no. 12 (2025): 6411. https://doi.org/10.3390/app15126411.

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As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents a YOLOv8-based binocular vision model for real-time recognition of blasting fragmentation. The model is trained on a dataset comprising 1536 samples, which were annotated using an automatic labeling algorithm and expanded to 7680 samples through data augmentation techniques. The YOLOv8 instance segmentati

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Cao, Qifan, and Lihong Xu. "Unsupervised Greenhouse Tomato Plant Segmentation Based on Self-Adaptive Iterative Latent Dirichlet Allocation from Surveillance Camera." Agronomy 9, no. 2 (2019): 91. http://dx.doi.org/10.3390/agronomy9020091.

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It has long been a great concern in deep learning that we lack massive data for high-precision training sets, especially in the agriculture field. Plants in images captured in greenhouses, from a distance or up close, not only have various morphological structures but also can have a busy background, leading to huge challenges in labeling and segmentation. This article proposes an unsupervised statistical algorithm SAI-LDA (self-adaptive iterative latent Dirichlet allocation) to segment greenhouse tomato images from a field surveillance camera automatically, borrowing the language model LDA. H

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Wu and Xu. "Crop Organ Segmentation and Disease Identification Based on Weakly Supervised Deep Neural Network." Agronomy 9, no. 11 (2019): 737. http://dx.doi.org/10.3390/agronomy9110737.

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Object segmentation and classification using the deep convolutional neural network (DCNN) has been widely researched in recent years. On the one hand, DCNN requires large data training sets and precise labeling, which bring about great difficulties in practical application. On the other hand, it consumes a large amount of computing resources, so it is difficult to apply it to low-cost terminal equipment. This paper proposes a method of crop organ segmentation and disease recognition that is based on weakly supervised DCNN and lightweight model. While considering the actual situation in the gre

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Lavanya, R., and N. R. Shanker. "Thermal Image Based Occupant Count Measurement Model using Human Body Temperature for Smart Building." Indian Journal Of Science And Technology 17, no. 26 (2024): 2683–90. http://dx.doi.org/10.17485/ijst/v17i26.1647.

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Objectives: The proposed Occupant Count Measurement (OCM) model aims to enhance sustainability, energy efficiency, comfort, and safety in smart buildings by accurately determining occupant count using thermal camera images and body temperature data. Methods: The model leverages real-time thermal camera images without the need for a pre-existing dataset. Key parameters include temperature threshold, occupant motion, size, and shape to ensure accurate occupancy estimation. The K-means algorithm identifies and clusters regions of interest (ROI) in thermal images corresponding to human body temper

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R, Lavanya, and R. Shanker N. "Thermal Image Based Occupant Count Measurement Model using Human Body Temperature for Smart Building." Indian Journal of Science and Technology 17, no. 26 (2024): 2683–90. https://doi.org/10.17485/IJST/v17i26.1647.

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Abstract <strong>Objectives:</strong> The proposed Occupant Count Measurement (OCM) model aims to enhance sustainability, energy efficiency, comfort, and safety in smart buildings by accurately determining occupant count using thermal camera images and body temperature data. <strong>Methods:</strong> The model leverages real-time thermal camera images without the need for a pre-existing dataset. Key parameters include temperature threshold, occupant motion, size, and shape to ensure accurate occupancy estimation. The K-means algorithm identifies and clusters regions of interest

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Miao, Ying, Danyang Shao, and Zhimin Yan. "Privacy-Oriented Successive Approximation Image Position Follower Processing." Complexity 2021 (June 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6853809.

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In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the resea

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Liu, Tao, Chunsheng Li, Zongbao Liu, et al. "Research on Image Identification Method of Rock Thin Slices in Tight Oil Reservoirs Based on Mask R-CNN." Energies 15, no. 16 (2022): 5818. http://dx.doi.org/10.3390/en15165818.

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Terrestrial tight oil has extremely strong diagenesis heterogeneity, so a large number of rock thin slices are needed to reveal the real microscopic pore-throat structure characteristics. In addition, difficult identification, high cost, long time, strong subjectivity and other problems exist in the identification of tight oil rock thin slices, and it is difficult to meet the needs of fine description and quantitative characterization of the reservoir. In this paper, a method for identifying the characteristics of rock thin slices in tight oil reservoirs based on the deep learning technique wa

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Roth, Andreas, Konstantin Wüstefeld, and Frank Weichert. "A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation." Journal of Imaging 7, no. 10 (2021): 206. http://dx.doi.org/10.3390/jimaging7100206.

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In the context of sensor-based data analysis, the compensation of image artifacts is a challenge. When the structures of interest are not clearly visible in an image, algorithms that can cope with artifacts are crucial for obtaining the desired information. Thereby, the high variation of artifacts, the combination of different types of artifacts, and their similarity to signals of interest are specific issues that have to be considered in the analysis. Despite the high generalization capability of deep learning-based approaches, their recent success was driven by the availability of large amou

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Ortega Adarme, M. X., P. J. Soto Vega, G. A. O. P. Costa, R. Q. Feitosa, and C. Heipke. "A DEBIASING VARIATIONAL AUTOENCODER FOR DEFORESTATION MAPPING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 217–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-217-2023.

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Abstract. Deep Learning (DL) algorithms provide numerous benefits in different applications, and they usually yield successful results in scenarios with enough labeled training data and similar class proportions. However, the labeling procedure is a cost and time-consuming task. Furthermore, numerous real-world classification problems present a high level of class imbalance, as the number of samples from the classes of interest differ significantly. In various cases, such conditions tend to promote the creation of biased systems, which negatively impact their performance. Designing unbiased sy

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Yu., Gorgo, Hretskyi I, Nejedlik P, Prigancova A, Kalinichenko E, and Gromozova E. "Quantitative estimates of the metachromasia reaction of volutin granules of yeast using neural networks." Artificial Intelligence 29, AI.2024.29(2) (2024): 62–71. http://dx.doi.org/10.15407/jai2024.02.062.

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The metachromatic coloration of volutinous granules of the yeast Saccharomyces cerevisiae is one of the indicators of the influence of sharp geomagnetic field (GMF) perturbations. The metachromasia reaction is based on the aggregation of dye molecules in interaction with inorganic polyphosphates, which are components of volutinous granules. To determine the characteristics of the geomagnetic field that cause the appearance of different colors of the metachromasia reaction, it is necessary to simultaneously monitor this reaction and changes in the GMF. High-quality monitoring is possible with r

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Gálvez-Gutiérrez, Ana I., Frederico Afonso, and Juana M. Martínez-Heredia. "On the Usage of Deep Learning Techniques for Unmanned Aerial Vehicle-Based Citrus Crop Health Assessment." Remote Sensing 17, no. 13 (2025): 2253. https://doi.org/10.3390/rs17132253.

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This work proposes an end-to-end solution for leaf segmentation, disease detection, and damage quantification, specifically focusing on citrus crops. The primary motivation behind this research is to enable the early detection of phytosanitary problems, which directly impact the productivity and profitability of Spanish and Portuguese agricultural developments, while ensuring environmentally safe management practices. It integrates an onboard computing module for Unmanned Aerial Vehicles (UAVs) using a Raspberry Pi 4 with Global Positioning System (GPS) and camera modules, allowing the real-ti

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Santacroce, G., P. Meseguer, I. Zammarchi, et al. "P406 A novel active learning-based digital pathology protocol annotation for histologic assessment in Ulcerative Colitis using PICaSSO Histologic Remission Index (PHRI)." Journal of Crohn's and Colitis 18, Supplement_1 (2024): i843—i844. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0536.

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Abstract Background Histologic remission (HR) is a critical treatment target in Ulcerative Colitis (UC). Among several scoring systems, the PICaSSO Histologic Remission Index (PHRI) simplifies HR assessment by evaluating the presence of neutrophils in the bowel tissue. Our artificial intelligence (AI) system built upon PHRI showed remarkable accuracy in HR assessment. PHRI assess neutrophils in four different regions of interest, so segmentation of these compartments is crucial to predict PHRI automatically. However, creating labelled histopathological datasets to train fully-supervised segmen

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Maidannyk, Valentyn A., Yuriy Simonov, Noel A. McCarthy, and Quang Tri Ho. "Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs." Foods 13, no. 1 (2023): 94. http://dx.doi.org/10.3390/foods13010094.

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Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders’ rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be able to automatically measure the real-time water diffusion coefficient in dairy powders based on confocal microscopy techniques. Furthermore, morphological characteristics and local hydration of individual particles were identified using an imaging analysis procedure written in Matlab©—R2023b and image analysis throug

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Bassier, Maarten, Meisam Yousefzadeh, and Maarten Vergauwen. "Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM." Journal of Information Technology in Construction 25 (March 2, 2020): 173–92. http://dx.doi.org/10.36680/j.itcon.2020.011.

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As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is

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Bai, Ting, Dominik Stütz, Chang Liu, Dimitri Bulatov, Jorg Hacker, and Linlin Ge. "Unsupervised Bushfire Burn Severity Mapping Using Aerial and Satellite Imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 29–36. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-29-2024.

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Abstract. It is critical to assess bushfire impact rapidly and accurately because bushfires play a significant role in forest degradation and present a threat to ecosystems and human lives. Over the past decades, several supervised algorithms of burn severity mapping have been proposed, facing the significant drawback of time-consuming labeling. Moreover, there is no robust framework for burn severity mapping through fusing multi-sensor, multi-resolution, and multi-temporal remote sensing imagery from satellite and aerial platforms. Therefore, this paper presents an unsupervised two-step pipel

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El Hassani, M., S. Jehan-Besson, L. Brun, et al. "A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation." VLSI Design 2008 (April 24, 2008): 1–12. http://dx.doi.org/10.1155/2008/892370.

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We propose a time-consistent video segmentation algorithm designed for real-time implementation. Our algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images with few parameters to be set. Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination diff

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Shen, Jianbing, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, and Ling Shao. "Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm." IEEE Transactions on Image Processing 25, no. 12 (2016): 5933–42. http://dx.doi.org/10.1109/tip.2016.2616302.

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Song, Wei, Yifei Tian, Simon Fong, Kyungeun Cho, Wei Wang, and Weiqiang Zhang. "GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance." Sustainability 8, no. 10 (2016): 916. http://dx.doi.org/10.3390/su8100916.

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Blokhinov, Y. B., V. A. Gorbachev, Y. O. Rakutin, and D. A. Nikitin. "A REAL-TIME SEMANTIC SEGMENTATION ALGORITHM FOR AERIAL IMAGERY." Computer Optics 42, no. 1 (2018): 141–48. http://dx.doi.org/10.18287/2412-6179-2018-42-1-141-148.

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We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class. Based on a comparative analysis of preliminary segmentation methods, methods for calculating attributes from image segments, as well as various algorithms of machine learning, the most effective methods in terms of their accuracy and performance are identified. Based on the research results, a modular near real-time algorithm of semantic segmentation is constructed. Training and testing is performed on the ISPRS Vaihingen collection of aerial photos of the visible and I

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Kim, Kyungnam, Thanarat H. Chalidabhongse, David Harwood, and Larry Davis. "Real-time foreground–background segmentation using codebook model." Real-Time Imaging 11, no. 3, June 2005 (2005): 72–185. https://doi.org/10.1016/j.rti.2004.12.004.

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We present a real-time algorithm for foreground–background segmentation. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. The codebook representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for

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Robitaille, Michael C., Jeff M. Byers, Joseph A. Christodoulides, and Marc P. Raphael. "Robust optical flow algorithm for general single cell segmentation." PLOS ONE 17, no. 1 (2022): e0261763. http://dx.doi.org/10.1371/journal.pone.0261763.

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Cell segmentation is crucial to the field of cell biology, as the accurate extraction of single-cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. In an effort to increase available segmentation tools that can perform across research groups and platforms, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation of single cells by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environ

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JEEVARATHINAM, Mrs A., and KANISHKA S. "REAL-TIME TRAFFIC ANALYSIS AND PREDICTION USING YOLO ALGORITHM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–7. https://doi.org/10.55041/ijsrem42726.

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Currently the traffic control system in our country is non-flexible to the ever growing number of vehicles on the road. Traffic light is the basic element in traffic flow control through specified waiting and going time, fixed traffic light time systems is a bad control way. Intelligent traffic system includes smart way to control traffic light time based on number of vehicles in each lane. Improving traffic signal control system will increase safety, reliability, and traffic flow speed and reduce average travelling and waiting time for passengers. The objective is to design an efficient autom

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Aguilar-González, Abiel, Alejandro Medina Santiago, Jorge Antonio Orozco Torres, J. A. de Jesús Osuna-Coutiño, Madaín Pérez Patricio, and Néstor A. Morales-Navarro. "TurboPixels: A Superpixel Segmentation Algorithm Suitable for Real-Time Embedded Applications." Applied Sciences 14, no. 24 (2024): 11912. https://doi.org/10.3390/app142411912.

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Superpixel segmentation aims to produce a consistent grouping of pixels. In recent years, the importance of superpixel segmentation has increased in computer vision since it offers useful primitives for extracting image features and simplifies the complexity of other image processing steps. In this work, we propose the TurboPixels algorithm, whose main contribution is a hardware architecture for superpixel segmentation. Compared with previous approaches, our superpixels are computed without the need for iterative loops. This makes it possible to reduce algorithmic complexity and increases proc

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Heo, Jiseong, and Hyoung woo Lim. "Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration." Journal of Robotics 2022 (June 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/9916292.

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Applying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were used to gather pseudolabels for semantic segmentation. A very simple dynamics model was used as a simulator, and dynamic parameters were obtained from the linear regression of manual driving records. Segmentation and a dynamic calibration method were found to be effective in easing the transition from

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Glänzer, Lukas, Husam E. Masalkhi, Anjali A. Roeth, Thomas Schmitz-Rode, and Ioana Slabu. "Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images." Cancers 15, no. 15 (2023): 3773. http://dx.doi.org/10.3390/cancers15153773.

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Semantic segmentation is an important imaging analysis method enabling the identification of tissue structures. Histological image segmentation is particularly challenging, having large structural information while providing only limited training data. Additionally, labeling these structures to generate training data is time consuming. Here, we demonstrate the feasibility of a semantic segmentation using U-Net with a novel sparse labeling technique. The basic U-Net architecture was extended by attention gates, residual and recurrent links, and dropout regularization. To overcome the high class

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Deng, Weiye, Xiaoping Chen, and Jingwei Jiang. "A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud." Electronics 13, no. 5 (2024): 841. http://dx.doi.org/10.3390/electronics13050841.

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Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation alg

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