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Journal articles on the topic 'Detection et segmentation des lignes'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Lessard, Claude, and Creutzer Mathurin. "L’évolution du corps enseignant québécois : 1960-1986." Revue des sciences de l'éducation 15, no. 1 (2009): 43–71. http://dx.doi.org/10.7202/900617ar.

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Résumé Dans cet article, les auteurs esquissent les grandes lignes d’une problématique de l’évolution du corps enseignant québécois des niveaux primaire et secondaire, de la Révolution tranquille à aujourd’hui. La démarche essentiellement socio-historique aborde à la fois la structuration interne du corps enseignant et ses paramètres d’intégration, de différenciation et de segmentation, et aussi l’évolution de la conception dominante de la fonction enseignante. Une attention est portée à l’Université comme instance de légitimation professionnelle des enseignants. Au plan théorique, les auteurs

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Thirukokaranam, Chandrasekar Krishna Kumar, Kenzo Milleville, and Steven Verstockt. "Species Detection and Segmentation of Multi-specimen Historical Herbaria." Biodiversity Information Science and Standards 5 (September 7, 2021): e74060. https://doi.org/10.3897/biss.5.74060.

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Historically, herbarium specimens have provided users with documented occurrences of plants in specific locations over time. Herbarium collections have therefore been the basis of systematic botany for centuries (Younis et al. 2020). According to the latest summary report based on the data from Index Herbariorum, there are around 3400 active herbaria in the world containing 397 million specimens that are spread across 182 countries (Thiers 2021). Exponential growth in high quality image capturing devices induced by the enormous amount of uncovered collections has further led to rising interest

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Çiftci, Sadettin, and Bahattin Kerem Aydin. "Comment on Lee et al. Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening. Diagnostics 2021, 11, 1174." Diagnostics 12, no. 7 (2022): 1738. http://dx.doi.org/10.3390/diagnostics12071738.

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We have read the article titled “Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening” by Lee et al. [...]

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El-shazli, Alaa M. Adel, Sherin M. Youssef, and Marwa Elshennawy. "COMPUTER-AIDED MODEL FOR BREAST CANCER DETECTION IN MAMMOGRAMS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 2 (2016): 31. http://dx.doi.org/10.22159/ijpps.2016v8s2.15216.

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<p>The objective of this research was to introduce a new system for automated detection of breast masses in mammography images. The system will be able to discriminate if the image has a mass or not, as well as benign and malignant masses. The new automated ROI segmentation model, where a profiling model integrated with a new iterative growing region scheme has been proposed. The ROI region segmentation is integrated with both statistical and texture feature extraction and selection to discriminate suspected regions effectively. A classifier model is designed using linear fisher classifi

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Nour, Majid, Hakan Öcal, Adi Alhudhaif, and Kemal Polat. "Skin Lesion Segmentation Based on Edge Attention Vnet with Balanced Focal Tversky Loss." Mathematical Problems in Engineering 2022 (June 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/4677044.

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Segmentation of skin lesions from dermoscopic images plays an essential role in the early detection of skin cancer. However, skin lesion segmentation is still challenging due to artifacts such as indistinguishability between skin lesion and normal skin, hair on the skin, and reflections in the obtained dermoscopy images. In this study, an edge attention network (ET-Net) combining edge guidance module (EGM) and weighted aggregation module is added to the 2D volumetric convolutional neural network (Vnet 2D) to maximize the performance of skin lesion segmentation. In addition, the proposed fusion

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Ariouat, Hanane, Youcef Sklab, Marc Pignal, et al. "Extracting Masks from Herbarium Specimen Images Based on Object Detection and Image Segmentation Techniques." Biodiversity Information Science and Standards 7 (September 6, 2023): e112161. https://doi.org/10.3897/biss.7.112161.

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Herbarium specimen scans constitute a valuable source of raw data. Herbarium collections are gaining interest in the scientific community as their exploration can lead to understanding serious threats to biodiversity. Data derived from scanned specimen images can be analyzed to answer important questions such as how plants respond to climate change, how different species respond to biotic and abiotic influences, or what role a species plays within an ecosystem. However, exploiting such large collections is challenging and requires automatic processing. A promising solution lies in the use of c

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Laputin, Fedor A., Ivan V. Sidorov, and Andrey S. Moshkin. "Assessment of ovarian follicular reserve according to ultrasound data based on machine learning methods." Digital Diagnostics 5, no. 1S (2024): 40–42. http://dx.doi.org/10.17816/dd626171.

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BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective data analysis for scientific purposes allows to increase the objectivity of the study and supplement it with auxiliary information [2]. The issue of ovarian localization and follicle segmentation on ultrasou

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Chen, Shuo-Tsung, Tzung-Dau Wang, Wen-Jeng Lee, et al. "Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/798303.

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Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies.Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based o

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Zhong, Johnson. "Analyzing Out-of-Domain Generalization Performance of Pre-Trained Segmentation Models." Network and Communication Technologies 8, no. 1 (2023): 1. http://dx.doi.org/10.5539/nct.v8n1p1.

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Artists illustrate objects to various degrees of complexity. As the amount of detail or the similarity to reality of a depiction decreases, the object tends to be reduced to its simplest, most relevant higher-level features (Harrison, 1981). One of the reasons Deep Neural Networks (DNN) may fail to identify objects in an image is that models are unable to recognize the order of importance of features such as shape, depth, or color within an image, which means even the most minute distortions of pixels within an image that would be imperceptible to humans would greatly impact the performance of

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Jyoti, Kataria Supriya P. Panda. "HybridCSF model for magnetic resonance image based brain tumor segmentation." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1845–52. https://doi.org/10.11591/ijeecs.v35.i3.pp1845-1852.

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The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convo

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Lefkovits, Szidónia, and László Lefkovits. "U-Net architecture variants for brain tumor segmentation of histogram corrected images." Acta Universitatis Sapientiae, Informatica 14, no. 1 (2022): 49–74. http://dx.doi.org/10.2478/ausi-2022-0004.

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Abstract In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges

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Lee, Hongseok, Kyungdoc Kim, Guhyun Kang, Kyu-Hwan Jung, and Sunyoung S. Lee. "Abstract 1721: Spatial distribution of immune cells as quantitative prognosis indicator in hepatocellular carcinoma." Cancer Research 82, no. 12_Supplement (2022): 1721. http://dx.doi.org/10.1158/1538-7445.am2022-1721.

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Abstract Background: We previously demonstrated that the analysis of the tumor microenvironment (TME) in histopathology images via tissue segmentation [1] and cell density in lymphocyte-rich area [2] impacts prognosis and treatment in hepatocellular carcinoma (HCC). Few biomarker models exist to prognosticate patients with HCC via the automated analysis of TME at the cellular level. Methods: Clinical outcomes data and histopathology images of 351 patients with HCC were obtained from TCGA. We advanced a deep learning-based algorithm to analyze the tumor volume and spatial distribution of nuclei

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Gujjunoori, Sagar, Madhu Oruganti, N. Aparna, M. Srija, and Chaitrali Dangare. "Tracking and Size Estimation of Objects in Motion based on Median of Localized Thresholding." International Journal of Engineering & Technology 7, no. 4.6 (2018): 78. http://dx.doi.org/10.14419/ijet.v7i4.6.20241.

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Motion detection and tracking play an important role in Computer vision and Robotics. Optical flow based methods to estimate the motion are widely explored during the last decade. The motion information retrieved from these techniques has enormous applications. Video analysis based on the size, speed, and directions of objects have wider applications in computer vision, robotics and watermarking. Segmentation of moving objects based on the optical flow is very challenging. In this paper, we present a model to estimate the size of a moving object based on the optical flow technique and present

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Qiao, Lei, Wei Yuan, and Liu Tang. "DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires." Forests 15, no. 6 (2024): 947. http://dx.doi.org/10.3390/f15060947.

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Wildfires usually lead to a large amount of property damage and threaten life safety. Image recognition for fire detection is now an important tool for intelligent fire protection, and the advancement of deep learning technologies has enabled an increasing number of cameras to possess functionalities for fire detection and automatic alarm triggering. To address the inaccuracies in extracting texture and positional information during intelligent fire recognition, we have developed a novel network called DCP-Net based on UNet, which excels at capturing flame features across multiple scales. We c

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Kataria, Jyoti, and Supriya P. Panda. "HybridCSF model for magnetic resonance image based brain tumor segmentation." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1845. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1845-1852.

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<p>The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hyb

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Bougrine, Asma, Rachid Harba, Raphael Canals, Roger Ledee, Meryem Jabloun, and Alain Villeneuve. "Segmentation of Plantar Foot Thermal Images Using Prior Information." Sensors 22, no. 10 (2022): 3835. http://dx.doi.org/10.3390/s22103835.

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Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is add

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Stern, David L., Jan Clemens, Philip Coen, et al. "Experimental and statistical reevaluation provides no evidence for Drosophila courtship song rhythms." Proceedings of the National Academy of Sciences 114, no. 37 (2017): 9978–83. http://dx.doi.org/10.1073/pnas.1707471114.

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From 1980 to 1992, a series of influential papers reported on the discovery, genetics, and evolution of a periodic cycling of the interval between Drosophila male courtship song pulses. The molecular mechanisms underlying this periodicity were never described. To reinitiate investigation of this phenomenon, we previously performed automated segmentation of songs but failed to detect the proposed rhythm [Arthur BJ, et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou et al. [Kyriacou CP, et al. (2017) Proc Natl Acad Sci USA 114:1970–1975] report that we failed to detect song

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Almukhtar, Mohammed, Ameer H. Morad, Hussein L. Hussein, and Mina H. Al-hashimi. "Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 12, no. 1 (2024): 88–99. http://dx.doi.org/10.14500/aro.11333.

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The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (seg

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Makowski, Ryszard, and Robert Hossa. "Automatic speech signal segmentation based on the innovation adaptive filter." International Journal of Applied Mathematics and Computer Science 24, no. 2 (2014): 259–70. http://dx.doi.org/10.2478/amcs-2014-0019.

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Abstract Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006),

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Tanaka, Michio, Hiroki Matsubara, and Takashi Morie. "Human Detection and Face Recognition Using 3D Structure of Head and Face Surfaces Detected by RGB-D Sensor." Journal of Robotics and Mechatronics 27, no. 6 (2015): 691–97. http://dx.doi.org/10.20965/jrm.2015.p0691.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/11.jpg"" width=""300"" /> Summary of proposed method</div>Home service robots must possess the ability to communicate with humans, for which human detection and recognition methods are particularly important. This paper proposes methods for human detection and face recognition that are based on image processing, and are suitable for home service robots. For the human detection method, we combine the method proposed by Xia et al. based on the use of head shape with the results of region segmentation

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Damian-Gaillard, Béatrice. "Sexuality Stereotypes and Fantasies of Consumers of French Male Heterosexual Pornographic Media." Sur le journalisme, About journalism, Sobre jornalismo 8, no. 2 (2019): 46–61. http://dx.doi.org/10.25200/slj.v8.n2.2019.401.

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EN. Based on a survey of the French male heterosexual pornographic press, this paper addresses the class and gender stereotypes writers and editors have of their readership and their sexuality. The analysis is based on publishing company data, editorial guidelines for publications representative of the sector and individual interviews with four managers and nine editors. It identifies the commercial, professional and social issues underlying the construction and deployment of these stereotypes. The study is divided into two parts. The first sheds light on the heterogeneity of existing editoria

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Park, Jeonghyuk, Kyungdoc Kim, Hong-Seok Lee, et al. "Impact of cell density in lymphocyte-rich areas in the tumor microenvironment on prognosis and gene expression landscape in hepatocellular carcinoma." Journal of Clinical Oncology 39, no. 15_suppl (2021): 4107. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.4107.

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4107 Background: Cellular and non-cellular components in the tumor microenvironment (TME) impact prognosis and treatment in hepatocellular carcinoma (HCC). We previously reported a deep learning-based model of tissue segmentation in pathology images, showing an impact of stromal and malignant cell distribution with respect to gene expression on survival and molecular subtypes of cancer [1]. Methods: Clinical outcomes data, mRNA-seq, and histopathology images of 351 patients (pts) with HCC were obtained from TCGA. We established a combined algorithm of two deep learning models: ResNet-based mod

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da Silva, Ricardo Dutra, Rosane Minghim, and Helio Pedrini. "3D Edge Detection Based on Boolean Functions and Local Operators." International Journal of Image and Graphics 15, no. 01 (2015): 1550003. http://dx.doi.org/10.1142/s0219467815500035.

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Edge detection is one of the most commonly used operations in image processing and computer vision areas. Edges correspond to the boundaries between regions in an image, which are useful for object segmentation and recognition tasks. This work presents a novel method for 3D edge detection based on Boolean functions and local operators, which is an extension of the 2D edge detector introduced by Vemis et al. [Signal Processing45(2), 161–172 (1995)] The proposed method is composed of two main steps. An adaptive binarization process is initially applied to blocks of the image and the resulting bi

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Ngcofe, Luncedo, and Nale Mudau. "An investigation of geographic object based image analysis (GEOBIA) for human settlement detection in South Africa." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-269-2019.

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<p><strong>Abstract.</strong> The changes to the landscape are constantly occurring both naturally and human induced. One of such changes is the human settlement expansion. The ability to map human settlements is vital for variety of studies including urban development planning and management. For this study human settlement detection is essential for topographic map update. The newly identified human settlements also serves as change detection area indicator for further update of other topographic features that are represented on the topographic map (such as roads etc.). The

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Eckstein, F., A. Wisser, F. Roemer, et al. "POS0403 SENSITIVITY OF AUTOMATED, U-NET-BASED SEGMENTATION TO LAMINAR CARTILAGE TRANSVERSE RELAXATION TIME (T2) IN KNEES WITH DIFFERENCES IN CONTRALATERAL OSTEOARTHRITIS STATUS – DATA FROM THE THE OA-BIO CONSORTIUM." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 457.1–457. http://dx.doi.org/10.1136/annrheumdis-2023-eular.3125.

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BackgroundRadiographically normal knees with contralateral (CL) radiographic joint space narrowing (JSN) are at elevated risk of incident radiographic osteoarthritis (ROA). We previously observed increased superficial femorotibial cartilage layer transverse relaxation time (T2) on magnetic resonance images (MRI) of 39 KLG0 knees (0=normal) with advanced ROA in the contralateral knee (CL JSN), compared with 39 (1:1-matched) KLG0 knees without evidence of CL ROA (bilateral KLG0) [1]. These results suggest that cartilage matrix degeneration occurs in radiographically normal knees with CL JSN, and

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Fang, Chengyong, Xuanmei Fan, Xin Wang, et al. "A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images." Earth System Science Data 16, no. 10 (2024): 4817–42. http://dx.doi.org/10.5194/essd-16-4817-2024.

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Abstract. Rapid and accurate mapping of landslides triggered by extreme events is essential for effective emergency response, hazard mitigation, and disaster management. However, the development of generalized machine learning models for landslide detection has been hindered by the absence of a high-resolution, globally distributed, event-based dataset. To address this gap, we introduce the Globally Distributed Coseismic Landslide Dataset (GDCLD), a comprehensive dataset that integrates multi-source remote sensing images, including PlanetScope, Gaofen-6, Map World, and uncrewed aerial vehicle

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Zhang, Mingyu, Fei Gao, Wuping Yang, and Haoran Zhang. "Correction: Zhang et al. Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction. Electronics 2023, 12, 377." Electronics 12, no. 8 (2023): 1923. http://dx.doi.org/10.3390/electronics12081923.

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Paracha, Muhammad Shaiq, Faisal F. Khan, Arsalan Riaz, and Madina Shirdel. "Abstract 2431: Enhanced cancer detection using TransUnet for low-resolution histopathology images across multiple cancer types." Cancer Research 85, no. 8_Supplement_1 (2025): 2431. https://doi.org/10.1158/1538-7445.am2025-2431.

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Abstract Digital histopathology has become an indispensable tool for cancer diagnosis and prognosis, enabling pathologists to analyze tumor morphology using Whole Slide Images (WSI). However, the high cost and limited availability of high-resolution imaging equipment in underdeveloped regions pose significant challenges. This study investigates the use of Low-Cost Low-Resolution (LCLR) histopathology images for detecting four cancer sites: Oral, Gastrointestinal, Colorectal and Breast cancers. We compared the performance of four models: AlexNet, EfficientNet, Vision Transformer and TransUnet.

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Mora-Fallas, Adán, Hervé Goëau, Susan Mazer, et al. "Accelerating the Automated Detection, Counting and Measurements of Reproductive Organs in Herbarium Collections in the Era of Deep Learning." Biodiversity Information Science and Standards 3 (June 26, 2019): e37341. https://doi.org/10.3897/biss.3.37341.

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Millions of herbarium records provide an invaluable legacy and knowledge of the spatial and temporal distributions of plants over centuries across all continents (Soltis et al. 2018). Due to recent efforts to digitize and to make publicly accessible most major natural collections, investigations of ecological and evolutionary patterns at unprecedented geographic scales are now possible (Carranza-Rojas et al. 2017, Lorieul et al. 2019). Nevertheless, biologists are now facing the problem of extracting from a huge number of herbarium sheets basic information such as textual descriptions, the num

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T., Gayathri, and Sundeep Kumar K. "Brain Tumor Segmentation and Classification Using CNN Pre-Trained VGG-16 Model in MRI Images." IIUM Engineering Journal 25, no. 2 (2024): 196–211. http://dx.doi.org/10.31436/iiumej.v25i2.2963.

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The formation of a group of abnormal cells in the brain that penetrate the neighboring tissues is known as a brain tumor. The initial detection of brain tumors is necessary to aid doctors in treating cancer patients to increase the survival rate. Various deep learning models are discovered and developed for efficient brain tumor detection and classification. In this research, a transfer learning-based approach is proposed to resolve overfitting issues in classification. The BraTS – 2018 dataset is utilized in this research for segmentation and classification. Batch normalization is utilized in

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Chen, Yuhong, Weilong Peng, Keke Tang, Asad Khan, Guodong Wei, and Meie Fang. "PyraPVConv: Efficient 3D Point Cloud Perception with Pyramid Voxel Convolution and Sharable Attention." Computational Intelligence and Neuroscience 2022 (May 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/2286818.

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Designing efficient deep learning models for 3D point cloud perception is becoming a major research direction. Point-voxel convolution (PVConv) Liu et al. (2019) is a pioneering research work in this topic. However, since with quite a few layers of simple 3D convolutions and linear point-voxel feature fusion operations, it still has considerable room for improvement in performance. In this paper, we propose a novel pyramid point-voxel convolution (PyraPVConv) block with two key structural modifications to address the above issues. First, PyraPVConv uses a voxel pyramid module to fully extract

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Gourmelon, Nora, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein. "Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery." Earth System Science Data 14, no. 9 (2022): 4287–313. http://dx.doi.org/10.5194/essd-14-4287-2022.

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Abstract. Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. Howe

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Jiang, Hou, Ling Yao, Ning Lu, et al. "Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery." Earth System Science Data 13, no. 11 (2021): 5389–401. http://dx.doi.org/10.5194/essd-13-5389-2021.

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Abstract. In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8,

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Saeed, Muhammad Usman, Ghulam Ali, Wang Bin, et al. "RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images." Electronics 10, no. 16 (2021): 1962. http://dx.doi.org/10.3390/electronics10161962.

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The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The arc

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Schofield, Andrew J., and Timothy A. Yates. "Interactions between Orientation and Contrast Modulations Suggest Limited Cross-Cue Linkage." Perception 34, no. 7 (2005): 769–92. http://dx.doi.org/10.1068/p5294.

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Recent studies of texture segmentation and second-order vision have proposed very similar models for the detection of orientation modulation and contrast modulation (OM and CM). From the similarity of the models it is tempting to assume that the two cues might be processed by a single generalised texture mechanism; however, recent results (Kingdom et al, 2003 Visual Neuroscience2 65–76) have suggested that these cues are detected independently, or at least in a mechanism that is able to maintain an apparent independence between the cues. We tested new combinations of OM and CM and found that C

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Yadlapalli, Priyanka, and D. Bhavana. "Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model." Scalable Computing: Practice and Experience 23, no. 4 (2022): 403–20. http://dx.doi.org/10.12694/scpe.v23i4.2051.

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Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have be

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Pirotti, F., C. Paterno, and M. Pividori. "APPLICATION OF TREE DETECTION METHODS OVER LIDAR DATA FOR FOREST VOLUME ESTIMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1055–60. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1055-2020.

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Abstract. Lidar (light detection and ranging) data are becoming more and more important in the analysis of the most relevant forest parameters. This study aims to compare the most recent segmentation methods for single trees using the ALS (Airborne Laser Scanning) point cloud and the CHM (Canopy Height Model). The methods used were the Li et al., method developed in 2012 and the Multi CHM method developed in 2015. The parameters analysed were the height and diameter for the individual trees and the volume and density for the entire forest. The efficiency of each method was verified by comparin

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Ercan, Caner, Mairene Coto-Llerena, Salvatore Piscuoglio, and Luigi M. Terracciano. "Abstract 453: Establishing quantitative image analysis methods for tumor microenvironment evaluation." Cancer Research 82, no. 12_Supplement (2022): 453. http://dx.doi.org/10.1158/1538-7445.am2022-453.

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Abstract Introduction: There is growing evidence that supports the role of the tumor microenvironment (TME) in the development and progression of hepatocellular carcinoma (HCC). However, the correlation between its composition and prognosis remain unclear. TME evaluation requires a combination of cell type and spatial information. These information can be obtained with the use of immunohistochemistry on patient derived slides. However, the IHC quantification remains a challenge. Computational methods such as artificial intelligence-based tool, may expedite the detection and classification thou

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Guiotte, F., M. B. Rao, S. Lefèvre, P. Tang, and T. Corpetti. "RELATION NETWORK FOR FULL-WAVEFORMS LIDAR CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 515–20. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-515-2020.

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Abstract. LiDAR data are widely used in various domains related to geosciences (flow, erosion, rock deformations, etc.), computer graphics (3D reconstruction) or earth observation (detection of trees, roads, buildings, etc.). Because of the unstructured nature of remaining 3D points and because of the cost of acquisition, the LiDAR data processing is still challenging (few learning data, difficult spatial neighboring relationships, etc.). In practice, one can directly analyze the 3D points using feature extraction and then classify the points via machine learning techniques (Brodu, Lague, 2012

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Paing, May Phu, Kazuhiko Hamamoto, Supan Tungjitkusolmun, and Chuchart Pintavirooj. "Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications." Applied Sciences 9, no. 11 (2019): 2329. http://dx.doi.org/10.3390/app9112329.

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Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase

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Meena, Sansar Raj, Lorenzo Nava, Kushanav Bhuyan, et al. "HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery." Earth System Science Data 15, no. 7 (2023): 3283–98. http://dx.doi.org/10.5194/essd-15-3283-2023.

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Abstract. Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth observation (EO) data, several gaps and uncertainties remain with developing models to be operational at the global scale. The lack of a high-resolution globally distributed and event-diverse dataset for landslide segmentation poses a challenge in developing machine learning models that can accurately and robustly detect landslides in various

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Marshak, Charlie, Marc Simard, and Michael Denbina. "Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels." Remote Sensing 11, no. 5 (2019): 556. http://dx.doi.org/10.3390/rs11050556.

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We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife R

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Borges, Flavia, Sandra Ofori, and Maura Marcucci. "Myocardial Injury after Noncardiac Surgery and Perioperative Atrial Fibrillation." Canadian Journal of General Internal Medicine 16, SP1 (2021): 18–26. http://dx.doi.org/10.22374/cjgim.v16isp1.530.

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One in 60 patients who undergo major noncardiac surgery dies within 30 days following surgery. The most common cause is cardiac complications, of which myocardial injury after noncardiac surgery (MINS) and perioperative atrial fibrillation (POAF) are common, affecting about 18 and 11% of adults, respectively, after noncardiac surgery. Patients who suffer MINS are at a higher risk of death compared to patients without MINS. Similarly, patients who develop POAF are at a higher risk of stroke and death compared to patients who do not. Most patients who suffer MINS are asymptomatic, and its diagno

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Wasnaa Kadhim Jawad. "A New Approach for Real-Time Object Detection using Improved YOLOv5." Journal of Information Systems Engineering and Management 10, no. 25s (2025): 07–12. https://doi.org/10.52783/jisem.v10i25s.3927.

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Object detection poses a complicated and pivotal task in computer imaginative and prescient, experiencing tremendous progress with the emergence of deep gaining knowledge of in current years. Researchers have drastically boosted the effectiveness of item detection and its associated obligations, including type, localization, and segmentation, through harnessing deep studying fashions. Object detectors are normally categorized into two groups: two-level detectors, which hire elaborate architectures to pay attention on selective region proposals, and single-level detectors, which make use of eas

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Lee, Sangho, Kyoung Hee Choi, Sohyun Hwang, and Jihyang Kim. "#319 : Blastocyst Formation Prediction Based on Deep Learning Model from 3-Day Embryo Images in Time-Lapse Incubator Using Data Augmentation." Fertility & Reproduction 05, no. 04 (2023): 701. http://dx.doi.org/10.1142/s2661318223744132.

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Background and Aims: Current literature suggests that blastocyst ET (Embryo Transfer) at day 5 improves pregnancy outcomes compared with cleavage ET at day 3. However, blastocyst ET poses potential challenges due to the risk of developmental arrest at the cleavage stage. Therefore, accurately predicting blastocyst formation in embryos will help determining optimal days for embryo culture. The aim of our study is to develop a deep learning-based classification model that can predict blastocyst formation based on 3-day embryo images captured from Time-Lapse incubators. Method: A total of 200 emb

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Iacucci, M., V. Vadori, P. Meseguer, et al. "DOP045 Novel AI-Driven Detection, Localisation and Quantification of Neutrophils for Prediction of Early Response to Therapy in a Phase 2 Ulcerative Colitis Clinical Trial." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i166—i167. https://doi.org/10.1093/ecco-jcc/jjae190.0084.

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Abstract Background Achieving histological remission is a key treatment goal in Ulcerative Colitis (UC) as it is associated with better disease management and predicts response to therapy. Neutrophils are the key drivers of disease activity in UC and are, therefore, integral to many histological scoring systems. However, current scoring methods remain complex and subject to inter-rater variability. Thus, our study aimed to develop a novel AI-driven model to standardise detection, localisation, and quantification of neutrophils, supporting evaluation of histological activity and enabling predic

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Naidu, Adarsh. "Synthetic Data Generation for Privacy Preservation in Financial Technologies." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 1 (2020): 139–42. https://doi.org/10.54660/.ijmrge.2020.1.1.139-142.

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This research examines the utilization of Generative Adversarial Networks (GANs) to produce synthetic financial data that ensures privacy while adhering to stringent regulatory frameworks, such as the General Data Protection Regulation (GDPR) (European Union, 2016) [4] and the California Consumer Privacy Act (CCPA). Financial institutions handle extensive sensitive data, necessitating stringent privacy safeguards. Conventional anonymization techniques frequently reduce data utility, thereby limiting their effectiveness for machine learning, research, and analysis. Conversely, GANs offer an inn

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Naidu, Adarsh. "Synthetic Data Generation for Privacy Preservation in Financial Technologies." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 2 (2020): 64–67. https://doi.org/10.54660/.ijmrge.2020.1.2.64-67.

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This research examines the utilization of Generative Adversarial Networks (GANs) to produce synthetic financial data that ensures privacy while adhering to stringent regulatory frameworks, such as the General Data Protection Regulation (GDPR) (European Union, 2016) [4] and the California Consumer Privacy Act (CCPA). Financial institutions handle extensive sensitive data, necessitating stringent privacy safeguards. Conventional anonymization techniques frequently reduce data utility, thereby limiting their effectiveness for machine learning, research, and analysis. Conversely, GANs offer an inn

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Orthuber, E., and J. Avbelj. "3D BUILDING RECONSTRUCTION FROM LIDAR POINT CLOUDS BY ADAPTIVE DUAL CONTOURING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 157–64. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-157-2015.

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This paper presents a novel workflow for data-driven building reconstruction from Light Detection and Ranging (LiDAR) point clouds. The method comprises building extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment boundary creation, and a structural 3D building reconstruction approach using adaptive 2.5D Dual Contouring. First, a 2D-grid is overlain on the segmented point cloud. Second, in each grid cell 3D vertices of the building model are estimated from the corresponding LiDAR points. Then, the number of 3D vertices is reduced in a quad-tree colla

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Xu, Xiaoping, Meirong Ji, Bobin Chen, and Guowei Lin. "Analysis on Characteristics of Dysplasia in 345 Patients with Myelodysplastic Syndrome." Blood 112, no. 11 (2008): 5100. http://dx.doi.org/10.1182/blood.v112.11.5100.5100.

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Abstract Objective To investigate the characteristics of dysplasia in myelodysplastic syndrome (MDS). Methods Collect 716 samples of adult patients with abnormal blood routine but unclear cause between July 04, 2003 and March 14, 2007. Based on the gold standard of WHO MDS classification, all cases were detected on cytomorphological observation, cytochemical stain, bone marrow pathological study, cytogenetics, flow cytometry, and ect. The bone marrow cytological study on some abnormal hematopoietic cells has a diagnostic value to determine clonal or non-clonal diseases and assess sensitivity a

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