Relevant bibliographies by topics / LINEAR ITERATIVE CLUSTERING

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'LINEAR ITERATIVE CLUSTERING'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "LINEAR ITERATIVE CLUSTERING"

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Zhao, Jiaxing, Ren Bo, Qibin Hou, Ming-Ming Cheng, and Paul Rosin. "FLIC: Fast linear iterative clustering with active search." Computational Visual Media 4, no. 4 (2018): 333–48. http://dx.doi.org/10.1007/s41095-018-0123-y.

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Yan, Qingan, Long Yang, Chao Liang, Huajun Liu, Ruimin Hu, and Chunxia Xiao. "Geometrically Based Linear Iterative Clustering for Quantitative Feature Correspondence." Computer Graphics Forum 35, no. 7 (2016): 1–10. http://dx.doi.org/10.1111/cgf.12998.

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Magaraja, Anousouya Devi, Ezhilarasie Rajapackiyam, Vaitheki Kanagaraj, et al. "A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer." Applied Sciences 12, no. 20 (2022): 10522. http://dx.doi.org/10.3390/app122010522.

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Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach,
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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments,
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Eun, Hyunjun, Yoonhyung Kim, Chanho Jung, and Changick Kim. "Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering." Journal of Korean Institute of Communications and Information Sciences 43, no. 1 (2018): 20–23. http://dx.doi.org/10.7840/kics.2018.43.1.20.

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Oh, Ki-Won, and Kang-Sun Choi. "Acceleration of simple linear iterative clustering using early candidate cluster exclusion." Journal of Real-Time Image Processing 16, no. 4 (2016): 945–56. http://dx.doi.org/10.1007/s11554-016-0583-1.

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Choi, Kang-Sun, and Ki-Won Oh. "Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation." Computer Vision and Image Understanding 146 (May 2016): 1–8. http://dx.doi.org/10.1016/j.cviu.2016.02.018.

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Eremeev, S. V. "Clustering of spatial data with implicit polygonal structure based on topological approaches." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 25, no. 2 (2025): 261–72. https://doi.org/10.17586/2226-1494-2025-25-2-261-272.

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Clustering is one of the fundamental approaches for data mining, which in the field of geoinformatics and image processing is used to search for knowledge and hidden patterns of spatial information. During automatic vectorization of objects on satellite images due to imperfections of these technologies, missing elements appear on linear and polygonal objects, which prevent full-fledged data analysis and visualization. The paper considers the problem of clustering geometric primitives with implicit polygonal structure with the possibility of eliminating incomplete data in vector models. The pro
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Huang, Hui-Yu, and Zhe-Hao Liu. "Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation." Electronics 10, no. 6 (2021): 717. http://dx.doi.org/10.3390/electronics10060717.

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Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detect
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Cong, Jinyu, Benzheng Wei, Yilong Yin, Xiaoming Xi, and Yuanjie Zheng. "Performance evaluation of simple linear iterative clustering algorithm on medical image processing." Bio-Medical Materials and Engineering 24, no. 6 (2014): 3231–38. http://dx.doi.org/10.3233/bme-141145.

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Dissertations / Theses on the topic "LINEAR ITERATIVE CLUSTERING"

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Alexandre, Eduardo Barreto. "IFT-SLIC: geração de superpixels com base em agrupamento iterativo linear simples e transformada imagem-floresta." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-24092017-235915/.

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A representação de imagem baseada em superpixels tem se tornado indispensável na melhoria da eficiência em sistemas de Visão Computacional. Reconhecimento de objetos, segmentação, estimativa de profundidade e estimativa de modelo corporal são alguns importantes problemas nos quais superpixels podem ser aplicados. Porém, superpixels podem influenciar a qualidade dos resultados do sistema positiva ou negativamente, dependendo de quão bem eles respeitam as fronteiras dos objetos na imagem. Neste trabalho, é proposto um método iterativo para geração de superpixels, conhecido por IFT-SLIC, baseado
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Neubert, Peer. "Superpixels and their Application for Visual Place Recognition in Changing Environments." Doctoral thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-190241.

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Superpixels are the results of an image oversegmentation. They are an established intermediate level image representation and used for various applications including object detection, 3d reconstruction and semantic segmentation. While there are various approaches to create such segmentations, there is a lack of knowledge about their properties. In particular, there are contradicting results published in the literature. This thesis identifies segmentation quality, stability, compactness and runtime to be important properties of superpixel segmentation algorithms. While for some of these propert
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BAGRI, VIKAS. "SIMPLE LINEAR ITERATIVE CLUSTERING AND HAAR WAVELET BASED IMAGE FORGERY DETECTION." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16358.

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The ready availability ofimage-editingsoftwaremakesitimportanttoensuretheauthenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, inwhichpart(s)ofthe image are copied and pasted back somewhereelseinthesameimage.Post-processingcanbe used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. The thesis postulates the use of segmentation approach by following the three step
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Wang, Wei. "Spatially Adaptive Analysis and Segmentation of Polarimetric SAR Data." Doctoral thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218081.

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In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) has been one of the most important instruments for earth observation, and is increasingly used in various remote sensing applications. Statistical modelling and scattering analysis are two main ways for PolSAR data interpretation, and have been intensively investigated in the past two decades. Moreover, spatial analysis was applied in the analysis of PolSAR data and found to be beneficial to achieve more accurate interpretation results. This thesis focuses on extracting typical spatial information, i.e., edges and regions by explo
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Book chapters on the topic "LINEAR ITERATIVE CLUSTERING"

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Liao, Nannan, Hui Liu, Cheng Li, Xia Ren, and Baolong Guo. "Simple Linear Iterative Clustering with Efficiency." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1057-9_11.

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Zhang, Houwang, and Yuan Zhu. "KSLIC: K-mediods Clustering Based Simple Linear Iterative Clustering." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_44.

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Ding, Tianyou, Wentao Zhang, and Chunning Zhou. "Clustering Effect of Iterative Differential and Linear Trails." In Information Security and Cryptology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26553-2_13.

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Choi, Kang-Sun, and Ki-Won Oh. "Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination." In Pattern Recognition and Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_65.

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Wang, Jing, Zilan Hu, and Haixian Wang. "Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_15.

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Su, Fan, Hui Xu, Guodong Chen, Zhenhua Wang, Lining Sun, and Zheng Wang. "Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space." In Intelligent Robotics and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27541-9_34.

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Moodley, Sadhasivan G., Oludayo O. Olugbara, and Timothy T. Adeliyi. "Effects of Strong Attribute Cooccurrence Influence Distance on Simple Linear Iterative Clustering Superpixel." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5035-1_25.

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Pavithra, G., T. C. Manjunath, and Dharmanna Lamani. "Detection of Primary Glaucoma in Humans Using Simple Linear Iterative Clustering (SLIC) Algorithm." In Lecture Notes on Data Engineering and Communications Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24643-3_50.

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Mathews, Arun B., S. U. Aswathy, and Ajith Abraham. "Lung CT Image Enhancement Using Improved Linear Iterative Clustering for Tumor Detection in the Juxta Vascular Region." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09176-6_53.

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Dvorak, Julia, Leonie Stanzl, Tobias Lachnit, Martin Benfer, Frank Balzereit, and Gisela Lanza. "On the Systematic Selection of CE Strategies for End-of-Life-Products: A Guide for Practitioners." In Zukunftstechnologien für den multifunktionalen Leichtbau. Springer Fachmedien Wiesbaden, 2024. https://doi.org/10.1007/978-3-658-45889-8_18.

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AbstractMotivated by the goal of reducing resource consumption, industries around the globe are rethinking their linear production systems to move towards a circular economy (CE). CE strategies must be selected individually for each product and each instance. The scope of the decision logic developed in this work was constrained to reuse, repair, remanufacture, recycle, and recover. Following this, factors that influence the selection of a strategy are identified based on literature and expert interviews and mapped to the selected CE strategies. A criteria clustering occurs according to PESTEL
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Conference papers on the topic "LINEAR ITERATIVE CLUSTERING"

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Neelamraju, Pavan Mohan, and Inbarasan Muniraj. "SLICE: Combined Super Linear Iterative Clustering and Earth Mover’s Distance for Brain Tumour Classification." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.dw1h.3.

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We demonstrate that the combination of Super Linear Iterative Clustering and Earth Mover’s Distance efficiently segments tumours from the MRI dataset. Despite using a smaller training dataset our approach achieves an accuracy of 86.2%.
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Wang, Hefei. "Research on the optimization of simple linear iterative clustering algorithm in superpixel segmentation." In Fifth International Conference on Telecommunications, Optics, and Computer Science, edited by Witold Pedrycz and Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3067445.

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Kim, Kwang-Shik, Dongni Zhang, Mun-Cheon Kang, and Sung-Jea Ko. "Improved simple linear iterative clustering superpixels." In 2013 IEEE 17th International Symposium on Consumer Electronics (ISCE). IEEE, 2013. http://dx.doi.org/10.1109/isce.2013.6570216.

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Li, Shiren, Junwei Huang, Jiayu Shang, and Xiongyi Wei. "A robust simple linear iterative clustering algorithm." In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP). IEEE, 2017. http://dx.doi.org/10.1109/siprocess.2017.8124557.

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Kang-Sun Choi and Ki-Won Oh. "Fast simple linear iterative clustering for superpixel segmentation." In 2015 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2015. http://dx.doi.org/10.1109/icce.2015.7066521.

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Wei, Zhifei, Baolong Guo, Cheng Li, and Zhijie Chen. "Speeded-up Simple Linear Iterative Clustering Based on Region Homogeneity." In 2019 2nd International Conference on Safety Produce Informatization (IICSPI). IEEE, 2019. http://dx.doi.org/10.1109/iicspi48186.2019.9096051.

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Al-Azawi, Razi J., Qussay S. Al-Jubouri, and Yousra Abd Mohammed. "Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering." In 2019 12th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2019. http://dx.doi.org/10.1109/dese.2019.00038.

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Margapuri, Venkat, Trevor Rife, Chaney Courtney, Brandon Schlautman, Kai Zhao, and Mitchell Neilsen. "Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering." In 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). IEEE, 2022. http://dx.doi.org/10.1109/ipas55744.2022.10052996.

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Doğan, Çağdaş. "Seaweed Growth Detection in Aquaculture Environment Using Simple Linear Iterative Clustering Method." In The 8th International Conference of Biotechnology, Environment and Engineering Sciences. SRO media, 2020. http://dx.doi.org/10.46617/icbe8001.

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Estimating the total biomass of cultivates in aquaculture plantations (fisheries, mussel plants, seaweed farms and compound sites) remains to be an issue for the industry and the researchers alike. There has been a diverse array of approaches towards this issue, like using markers, manually stapling the leaflets, weighting the actual mass of the organism and calculating the total mass by extrapolation. Seaweed growth detection is a subset of this problem. Our goal is to introduce a solution by automatically detecting the ratio of the target object in images of seaweed taken from an underwater
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Junliang, Ma, Wang Xili, and Xiao Bing. "Semi-supervised image segmentation with globalized probability of boundary and simple linear iterative clustering." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393374.

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Journal articles
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