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Relevant bibliographies by topics / Spectral segmentation

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Academic literature on the topic 'Spectral segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Spectral segmentation"

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Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (2023): 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Conside

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Mejia, Daniel, Oscar Ruiz-Salguero, and Carlos A. Cadavid. "Spectral-based mesh segmentation." International Journal on Interactive Design and Manufacturing (IJIDeM) 11, no. 3 (2016): 503–14. http://dx.doi.org/10.1007/s12008-016-0300-0.

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Li, Hongyu, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I.-Fan Shen. "Kernel Based Spectral Image Segmentation." Conference on Colour in Graphics, Imaging, and Vision 4, no. 1 (2008): 494–98. http://dx.doi.org/10.2352/cgiv.2008.4.1.art00106.

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Priego, Torres Blanca María, and Richard J. Duro. "An approach for the customized high-dimensional segmentation of remote sensing hyperspectral images." Sensors 19, no. 13 (2019): 2887. https://doi.org/10.3390/s19132887.

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The work presents a methodology for the customized segmentation of remote sensing hyperspectral images using a multigradient cellular automaton (MGCA) approach coupled with an evolutionary algorithm (ECAS-II). The study addresses three main challenges in hyperspectral image segmentation: the need for segmentations tailored to user requirements, the scarcity of adequately labeled reference images, and the loss of information that occurs when high-dimensional images are projected into lower-dimensional spaces before segmentation. The proposed methodology allows for the segmentation of multidimen

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Zhang, Jianwei, Yue Shen, Zhaohui Zheng, and Le Sun. "A Robust Image Segmentation Framework Based on Nonlocal Total Variation Spectral Transform." Wireless Communications and Mobile Computing 2022 (February 24, 2022): 1–20. http://dx.doi.org/10.1155/2022/1442745.

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Image segmentation plays an important role in various computer vision tasks. Nevertheless, noise always inevitably appears in images and brings a big challenge to image segmentation. To handle the problem, we study the nonlocal total variation (NLTV) spectral theory and build up an image segmentation framework with NLTV spectral transform to segment images with noise. Firstly, we decompose an image into the NLTV flow in the NLTV spectral transform, with which the max response time of each pixel is calculated. Secondly, a separation surface is constructed with the max response time to distingui

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Wang, Ke, Hainan Chen, Ligang Cheng, and Jian Xiao. "Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices." Remote Sensing 14, no. 2 (2022): 326. http://dx.doi.org/10.3390/rs14020326.

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Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on th

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Mohammadi, Sina, and Mohamed Allali. "Advancing Brain Tumor Segmentation with Spectral–Spatial Graph Neural Networks." Applied Sciences 14, no. 8 (2024): 3424. http://dx.doi.org/10.3390/app14083424.

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In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, w

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Yuan, Changan, Xiao Qin, Zhengyou Qin, and Ruili Wang. "Image segmentation based on modified superpixel segmentation and spectral clustering." Journal of Engineering 2018, no. 16 (2018): 1704–11. http://dx.doi.org/10.1049/joe.2018.8320.

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Wang, Hao, Tong Lu, Oscar Kin-Chung Au, and Chiew-Lan Tai. "Spectral 3D mesh segmentation with a novel single segmentation field." Graphical Models 76, no. 5 (2014): 440–56. http://dx.doi.org/10.1016/j.gmod.2014.04.009.

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Furth, Yoram, and Stanley R. Rotman. "Efficacy of Segmentation for Hyperspectral Target Detection." Sensors 25, no. 1 (2025): 272. https://doi.org/10.3390/s25010272.

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Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes. This paper elucidates the conditions under which segmentation might improve detector performance. Focusing on a representative algorithm and assuming a target additive model, the study examines

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Dissertations / Theses on the topic "Spectral segmentation"

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Martin, Ian John. "Multi-spectral image segmentation and compression." Thesis, University of Warwick, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343123.

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Kong, Tian Fook. "Multilevel spectral clustering : graph partitions and image segmentation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45275.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.<br>Includes bibliographical references (p. 145-146).<br>While the spectral graph partitioning method gives high quality segmentation, segmenting large graphs by the spectral method is computationally expensive. Numerous multilevel graph partitioning algorithms are proposed to reduce the segmentation time for the spectral partition of large graphs. However, the greedy local refinement used in these multilevel schemes has the tendency of trapping the partition in poor local minima. In thi

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Rusnak, John Joseph 1975. "Pitch period segmentation and spectral representation of speech signals." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80641.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (leaves 87-88).<br>by John Joseph Rusnak, Junior.<br>M.Eng.

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Casaca, Wallace Correa de Oliveira. "Graph Laplacian for spectral clustering and seeded image segmentation." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24062015-112215/.

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Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the re

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Rosenblum, Wendy. "Optimal selection of textural and spectral features for scene segmentation /." Online version of thesis, 1990. http://hdl.handle.net/1850/11492.

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Ersahin, Kaan. "Segmentation and classification of polarimetric SAR data using spectral graph partitioning." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/14607.

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Polarimetric Synthetic Aperture Radar (POLSAR) data have been commercially available for the last few years, which has increased demand for its operational use in remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. Existing strategies for automated POLSAR data analysis have utilized the polarimetric attributes of pixels, which involve target decompositions based on physical, mathematical or statistical models. A well-established and widely-used technique is the Wisha

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Zhao, Yonghui. "Image segmentation and pigment mapping of cultural heritage based on spectral imaging /." Online version of thesis, 2008. http://hdl.handle.net/1850/7050.

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Yuan, Jiangye. "Remote Sensing Image Segmentation and Object Extraction Based on Spectral and Texture Information." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339169309.

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Rajadell, Rojas Olga. "Data selection and spectral-spatial characterisation for hyperspectral image segmentation. Applications to remote sensing." Doctoral thesis, Universitat Jaume I, 2013. http://hdl.handle.net/10803/669093.

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El análisis de imágenes ha impulsado muchos descubrimientos en la ciencia actual. Esta tesis se centra en el análisis de imágenes remotas para inspección aérea, exactamente en el problema de segmentación y clasificación de acuerdo al uso del suelo. Desde el nacimiento de los sensores hiperespectrales su uso ha sido vital para esta tarea ya que facilitan y mejoran sustancialmente el resultado. Sin embargo el uso de imágenes hiperespectrales entraña, entre otros, problemas de dimensionalidad y de interacción con los expertos. Proponemos mejoras que ayuden a paliar estos inconvenientes y hagan

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Stephani, Henrike [Verfasser], Gabriele [Akademischer Betreuer] Steidl, and Erich Peter [Akademischer Betreuer] Klement. "Automatic Segmentation and Clustering of Spectral Terahertz Data / Henrike Stephani. Betreuer: Gabriele Steidl ; Erich Peter Klement." Kaiserslautern : Technische Universität Kaiserslautern, 2012. http://d-nb.info/1027625681/34.

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Books on the topic "Spectral segmentation"

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Martin, Ian John. Multi-spectral image segmentation and compression. typescript, 1999.

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Book chapters on the topic "Spectral segmentation"

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Park, JinHyeong, Hongyuan Zha, and Rangachar Kasturi. "Spectral Clustering for Robust Motion Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24673-2_32.

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Du, Hui, Yuping Wang, Xiaopan Dong, and Yiu-ming Cheung. "Texture Image Segmentation Using Spectral Clustering." In Communications in Computer and Information Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21380-4_113.

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Archip, Neculai, Robert Rohling, Peter Cooperberg, Hamid Tahmasebpour, and Simon K. Warfield. "Spectral Clustering Algorithms for Ultrasound Image Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_106.

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Zhongmin, Liu, Li Bohao, Li Zhanming, and Hu Wenjin. "Error Based Nyström Spectral Clustering Image Segmentation." In Intelligent Computing Theories and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_49.

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Di, Xiaofei, Hong Chang, and Xilin Chen. "Multi-layer Spectral Clustering for Video Segmentation." In Computer Vision – ACCV 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37444-9_1.

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Sidibe, Samba, Oumar Niang, and Ndeye Fatou Ngom. "Auto-adaptive Signal Segmentation Using Spectral Intrinsic Decomposition." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1165-9_51.

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Wu, Zhengzhe, Ville Heikkinen, Markku Hauta-Kasari, and Jussi Parkkinen. "Segmentation of Skin Spectral Images Using Simulated Illuminations." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_34.

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Robles-Kelly, Antonio. "Segmentation via Graph-Spectral Methods and Riemannian Geometry." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11556121_81.

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Matsekh, Anna, Alexei Skurikhin, Lakshman Prasad, and Edward Rosten. "Numerical Aspects of Spectral Segmentation on Polygonal Grids." In Applied Parallel and Scientific Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28151-8_19.

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He, Yuqing, Saijie Wang, Kuo Pei, Mingqi Liu, and Jiawei Lai. "Visible Spectral Iris Segmentation via Deep Convolutional Network." In Biometric Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_46.

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Conference papers on the topic "Spectral segmentation"

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Peng, Yaopeng, Danny Z. Chen, and Milan Sonka. "Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981199.

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Zhang, Xiaoyu, Laixian Zhang, Yingchun Li, Houpeng Sun, and Rong Li. "Point cloud segmentation method of satellites based on improved DGCNN." In Conference on Spectral Technology and Applications (CSTA 2024), edited by Zhe Wang and Hongbin Ding. SPIE, 2024. https://doi.org/10.1117/12.3034940.

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Fisher, Hayden B., and Brian R. White. "Ensemble Machine Learning Segmentation of Widefield Optical Imaging Using Spectral and Temporal Information." In Bio-Optics: Design and Application. Optica Publishing Group, 2025. https://doi.org/10.1364/boda.2025.jm4a.12.

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We propose and evaluate a novel machine learning segmentation approach for widefield optical imaging, utilizing multi-wavelength and temporal data, surpassing traditional single baseline image segmentation methods.

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Palnitkar, Rahul, and Jeova Farias Sales Rocha Neto. "A Sparse Graph Formulation for Efficient Spectral Image Segmentation." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647589.

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Kupfer, Benny, Leah Bar, and Nir Sochen. "Fully Unsupervised Deep Spectral Clustering for Retinal Vessel Segmentation." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981224.

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Han, Yikai, Jimao Jiang, and Yuru Pei. "Unsupervised Cell Localization and Segmentation via Synchronized Spectral Clustering." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981280.

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Wang Chongjun, Li Wu jun, Ding Lin, Tian Juan, and Chen Shifu. "Image segmentation using spectral clustering." In 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05). IEEE, 2005. http://dx.doi.org/10.1109/ictai.2005.74.

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CHHATWAL, HS, and AG CONSTANTINIDES. "SPEECH SPECTRAL SEGMENTATION FOR SPECTRAL ESTIMATION AND FORMANT MODELLING." In Autumn Conference 1986. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/22384.

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Casaca, Wallace, Gabriel Taubin, and Luis Gustavo Nonato. "Graph Laplacian for Spectral Clustering and Seeded Image Segmentation." In XXVIII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2015.9998.

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Interactive segmentation methods have gained much attention lately, specially due to their good performance in segmenting complex images and easy utilization. However, most interactive segmentation algorithms rely on sophisticated mathematical formulations whose effectiveness highly depends on the kind of image to be processed. In fact, sharp adherence to the contours of image segments, uniqueness of solution, high computational burden, and extensive user interaction are some of the weaknesses of most existing methods. In this thesis we proposed two novel interactive image segmentation techniq

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Yang, Lu, Qing Song, Zhihui Wang, and Shihui Zhang. "Spectrum segmentation and peak-seeking method based on spectral energy." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393139.

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Reports on the topic "Spectral segmentation"

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Mohan, Anish, Guillermo Sapiro, and Edward Bosch. Spatially-Coherent Non-Linear Dimensionality Reduction and Segmentation of Hyper-Spectral Images (PREPRINT). Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada478496.

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s

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