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Relevant bibliographies by topics / Signal segmentation

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Academic literature on the topic 'Signal segmentation'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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Journal articles on the topic "Signal segmentation"

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Beasley, Ryan A. "Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector." ISRN Signal Processing 2012 (May 17, 2012): 1–9. http://dx.doi.org/10.5402/2012/914232.

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Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient-specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation algorithm combining the region-growing Seeded Cellular Automata with a boundary term based on an edge-detected image. Both single processor and parallel processor implementations are developed and the algorithm is shown to be suitable for quick segmentations (2.2 s for voxel brain MRI) and interactive supervision (2–220 Hz). Furtherm

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Kaushik, Jamuna, and Abhishek Misal. "Segmentation of Phonocardiograms Signal." International Journal of Engineering Research and Advanced Technology 4, no. 7 (2018): 1–10. http://dx.doi.org/10.31695/ijerat.2018.3284.

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Jellema, Renger H., Shaji Krishnan, Margriet M. W. B. Hendriks, Bas Muilwijk, and Jack T. W. E. Vogels. "Deconvolution using signal segmentation." Chemometrics and Intelligent Laboratory Systems 104, no. 1 (2010): 132–39. http://dx.doi.org/10.1016/j.chemolab.2010.07.007.

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Huang, Lin, Jianjun Yan, Shiyu Cai, Rui Guo, Haixia Yan, and Yiqin Wang. "Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network." BioMed Research International 2022 (August 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/2766321.

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Purpose. Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method. This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of th

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Greibus, Mindaugas, and Laimutis Telksnys. "Rule Based Speech Signal Segmentation." Journal of Telecommunications and Information Technology, no. 4 (June 27, 2023): 37–43. http://dx.doi.org/10.26636/jtit.2010.4.1094.

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This paper presents the automated speech signal segmentation problem. Segmentation algorithms based on energetic threshold showed good results only in noise-free environments. With higher noise level automatic threshold calculation becomes complicated task. Rule based postprocessing of segments can give more stable results. Off-line, on-line and extrema types of rules are reviewed. An extrema-type segmentation algorithm is proposed. This algorithm is enhanced bya rule base to extract higher energy level segments from noise. This algorithm can work well with energy like features. The experiment

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Zhang, Zhike, Shuixin Zhang, and Hongyu Feng. "Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm." Journal of Healthcare Engineering 2021 (February 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/5573381.

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Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the image

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Kucharczyk, Daniel, Agnieszka Wyłomańska, Jakub Obuchowski, Radosław Zimroz, and Maciej Madziarz. "Stochastic Modelling as a Tool for Seismic Signals Segmentation." Shock and Vibration 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8453426.

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In order to model nonstationary real-world processes one can find appropriate theoretical model with properties following the analyzed data. However in this case many trajectories of the analyzed process are required. Alternatively, one can extract parts of the signal that have homogenous structure via segmentation. The proper segmentation can lead to extraction of important features of analyzed phenomena that cannot be described without the segmentation. There is no one universal method that can be applied for all of the phenomena; thus novel methods should be invented for specific cases. The

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El rai, Marwa Chendeb, Muna Darweesh, and Mina Al-Saad. "Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing." Electronics 11, no. 21 (2022): 3462. http://dx.doi.org/10.3390/electronics11213462.

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Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervi

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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Segmentation of design protocol using EEG." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 1 (2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.

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AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistan

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Mao, Yongjiang, Wenjuan Ren, Xipeng Li, Zhanpeng Yang, and Wei Cao. "Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation." Applied Sciences 13, no. 4 (2023): 2726. http://dx.doi.org/10.3390/app13042726.

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With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characterist

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Dissertations / Theses on the topic "Signal segmentation"

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Wellhausen, Jens. "Algorithms for audio signal segmentation and separation /." Aachen : Shaker, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016149157&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Karri, Venkata Ramakrishna Reddy, and Venkata Manoj Manda. "Fingerprint Image Segmentation Using Local Radial Transformations." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16430.

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With a considerable increase in technology and need for security, aninterest has been created in the development of biometric technology.Various personal identification techniques like face recognition, voicerecognition, retinal pattern and fingerprint recognition are in existence.Among all the available techniques, fingerprint recognition isthe best personal identification method, since each person has a uniquefingerprint pattern. Fingerprint image segmentation is a part of preprocessingfor fingerprint image recognition. Segmentation separatesthe foreground part of the fingerprint image from

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Wellhausen, Jens [Verfasser]. "Algorithms for Audio Signal Segmentation and Separation / Jens Wellhausen." Aachen : Shaker, 2007. http://d-nb.info/1166510050/34.

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Morel, Baptiste. "IRM du cerveau néonatal : segmentation et analyse du signal." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0035/document.

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L’essor de l’imagerie médicale par résonance magnétique (IRM) permet une exploration de plus en plus précise du cerveau en période néonatale. Comment interpréter le plus objectivement possible des images dont les particularités compliquent l’analyse ? La controverse autour des hyperintensités diffuses de la substance blanche (diffuse excessive high signal intensity, DEHSI) en est une illustration. Le premier objectif est d’étudier la variabilité des appréciations des radiologues. Il existe une bonne reproductibilité des mesures bidimensionnelles des structures cérébrales, mais une reproductibi

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Morel, Baptiste. "IRM du cerveau néonatal : segmentation et analyse du signal." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0035.

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L’essor de l’imagerie médicale par résonance magnétique (IRM) permet une exploration de plus en plus précise du cerveau en période néonatale. Comment interpréter le plus objectivement possible des images dont les particularités compliquent l’analyse ? La controverse autour des hyperintensités diffuses de la substance blanche (diffuse excessive high signal intensity, DEHSI) en est une illustration. Le premier objectif est d’étudier la variabilité des appréciations des radiologues. Il existe une bonne reproductibilité des mesures bidimensionnelles des structures cérébrales, mais une reproductibi

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Husson, Jean-Luc. "Une approche hiérarchique de la segmentation du signal de parole." Nancy 1, 1998. http://www.theses.fr/1998NAN10292.

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Dans cette thèse, nous reconsidérons le décodage acoustico-phonétique de parole sous un angle analytique. Pour ce faire, nous proposons un système de segmentation automatique du signal de parole continue et dans des conditions d'indépendance vis-à-vis du locuteur. Notre objectif consiste à transformer l'onde acoustique continue en une séquence discrète de segments temporels correspondant à des réalisations acoustiques de phonèmes. Le premier chapitre vise à présenter au lecteur non expert du domaine la problématique de la communication orale homme-machine et démontrer l'importance du niveau de

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Kwok, Kwok Sai. "Algorithms for image segmentation and their applications to video signal processing." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244298.

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Bodin, Emanuel. "Furniture swap : Segmentation and 3D rotation of natural images using deep learning." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435503.

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Learning to perceive scenes and objects from 2D images as 3D models is atrivial task for a human but very challenging for a computer. Being ableto retrieve a 3D model from a scene just by taking a picture of it canbe of great use in many fields, for example when making 3D blueprintsfor buildings or working with animations in the game or film industry.Novel view synthesis is a field within deep learning where generativemodels are trained to construct 3D models of scenes or objects from 2Dimages. In this work, the generative model HoloGAN is combined together with aU-net segmentation network. Th

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Dahdouh, Sonia. "Filtrage, segmentation et suivi d'images échographiques : applications cliniques." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00647326.

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La réalisation des néphrolithotomies percutanées est essentiellement conditionnée par la qualité dela ponction calicièle préalable. En effet, en cas d'échec de celle-ci, l'intervention ne peut avoir lieu.Réalisée le plus souvent sous échographie, sa qualité est fortement conditionnée par celle du retouréchographique, considéré comme essentiel par la deuxième consultation internationale sur la lithiase pour limiter les saignements consécutifs à l'intervention.L'imagerie échographique est largement plébiscitée en raison de son faible coût, de l'innocuité del'examen, liée à son caractère non inva

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Khemiri, Houssemeddine. "Approche générique appliquée à l'indexation audio par modélisation non supervisée." Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0055/document.

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La quantité de données audio disponibles, telles que les enregistrements radio, la musique, les podcasts et les publicités est en augmentation constance. Par contre, il n'y a pas beaucoup d'outils de classification et d'indexation, qui permettent aux utilisateurs de naviguer et retrouver des documents audio. Dans ces systèmes, les données audio sont traitées différemment en fonction des applications. La diversité de ces techniques d'indexation rend inadéquat le traitement simultané de flux audio où différents types de contenu audio coexistent. Dans cette thèse, nous présentons nos travaux sur

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Books on the topic "Signal segmentation"

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Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118908686.

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Kato, Zoltan, and Josiane Zerubia. Markov Random Fields in Image Segmentation. Now Publishers, 2012.

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Wang, Xiaogang. Deep Learning in Object Recognition, Detection, and Segmentation. Now Publishers, 2016.

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Choi, Haan-Go. Multiresolution segmentation methodology for respiratory electromyographic signals. 1992.

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Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology Books, 2018.

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Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology, 2018.

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Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2011.

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Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2014.

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Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2011.

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Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals: The Stockwell Transform Applied on Bio-signals and Electric Signals. Wiley-Interscience, 2014.

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Book chapters on the topic "Signal segmentation"

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Segmentation." In Digital Signal Processing. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_6.

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Murillo Rendón, Santiago, Cristian Castro Hoyos, Carlos M. Travieso-Gonzales, and Germán Castellanos-Domínguez. "Phonocardiography Signal Segmentation for Telemedicine Environments." In Advances in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38682-4_15.

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Chaloupka, Josef. "Audio-Visual TV Broadcast Signal Segmentation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31964-9_21.

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Ebrahiminia, Alireza, Mohamad Sadegh Helfroush, Habibollah Danyali, and Shabab Bazrafkan. "Contourlet-Based Levelset SAR Image Segmentation." In Artificial Intelligence and Signal Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10849-0_6.

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Gromniak, Martin, Sven Magg, and Stefan Wermter. "Neural Field Conditioning Strategies for 2D Semantic Segmentation." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44210-0_42.

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AbstractNeural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, and not memorize only one, it needs to be conditioned on a latent code which describes the signal at hand. Despite being an important aspect, there has been little research on conditioning strategies for neural fields. In this work, we explore the use of neural fields as decoders for 2D semantic segmentation. For this task, we compare three conditioning methods, simple concatenation of the latent code, Feature-wise Linear Modulation (FiLM), and Cross-A

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Meyer, Fernand. "Flooding and Segmentation." In Mathematical Morphology and its Applications to Image and Signal Processing. Springer US, 2002. http://dx.doi.org/10.1007/0-306-47025-x_21.

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Eli, Gulnar, and Askar Hamdulla. "Automatic Phonetic Segmentation Using HMM Model in Uyghur Language." In Multimedia and Signal Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_78.

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Castro-Hoyos, C., F. M. Grisales-Franco, J. D. Martínez-Vargas, Carlos D. Acosta-Medina, and Germán Castellanos-Domínguez. "Stationary Signal Separation Using Multichannel Local Segmentation." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_23.

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Merdassi, Hager, Walid Barhoumi, and Ezzeddine Zagrouba. "Color Images Co-segmentation Based on Fuzzy Local-Entropy Classification." In Multimedia and Signal Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_31.

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Fu, Zhaoxia, and Liming Wang. "Color Image Segmentation Using Gaussian Mixture Model and EM Algorithm." In Multimedia and Signal Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_9.

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Conference papers on the topic "Signal segmentation"

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Richter, Henryk, Fabian Müller-Graf, Jonas Merz, et al. "EIT Heartbeat Signal Segmentation by Fourier Based Synchrosqueeezing." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942705.

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Balaha, Farah, Noha M. Hassan, Mahmoud Awad, and Zied Bahroun. "Vibrational Signal Segmentation Method for Aluminum Drilling Monitoring." In 2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE, 2024. https://doi.org/10.1109/ictmod63116.2024.10878177.

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Caselli, Elena, Andrea Vottero, Sandra Pieraccini, Stefano Pastorelli, and Laura Gastaldi. "Evaluation of Human Movement Smoothness and influence of signal processing techniques." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006638.

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Movement smoothness is a pivotal parameter for evaluating the quality of human motion, reflecting its fluidity and continuity. This parameter holds significant importance in fields such as industrial ergonomics, medical rehabilitation and sports performance optimization. Metrics such as Spectral Arc-Length (SPARC) and Log of Dimensionless Jerk (LDLJ) are commonly used to quantify smoothness, but the impact of signal segmentation on these measurements remains underexplored. This study investigates how segmenting motion signals influences smoothness assessments in different movement tasks.Object

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Goh, Y. H., and P. Raveendran. "Phoneme segmentation of speech signal." In 2009 International Conference for Technical Postgraduates (TECHPOS). IEEE, 2009. http://dx.doi.org/10.1109/techpos.2009.5412045.

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Möller, Torben, Daniel Langenkämper, and Tim W. Nattkemper. "Wind turbine segmentation performing kNN-clustering on superpixel segmentations." In Image and Signal Processing for Remote Sensing XXV, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2532764.

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Gasperini, Stefano, Magdalini Paschali, Carsten Hopke, David Wittmann, and Nassir Navab. "Signal Clustering With Class-Independent Segmentation." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053409.

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Mahajan, S. P., V. B. Katekar, and V. K. Kokate. "Speaker segmentation." In National Conference on Signal and Image Processing Applications. IET, 2009. http://dx.doi.org/10.1049/ic.2009.0179.

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Djuric, P. M., S. M. Kay, and G. F. Boudreaux-Bartels. "Segmentation of nonstationary signals." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226633.

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Martínez-Martín, E., and A. P. del Pobil. "A Hybrid Algorithm for Motion Segmentation." In Signal and Image Processing. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.710-010.

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Marks, Victoria S., Christopher L. Felton, Robert W. Techentin, et al. "Stockwell Transform Detector For Photoplethysmography Signal Segmentation." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645540.

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