Relevant bibliographies by topics / Imagem segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Imagem segmentation'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Imagem segmentation"

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Conti, Luis Américo, and Murilo Baptista. "SYNTHETIC APERTURE SONAR IMAGES SEGMENTATION USING DYNAMICAL MODELING ANALYSIS." Revista Brasileira de Geofísica 31, no. 3 (2013): 455. http://dx.doi.org/10.22564/rbgf.v31i3.315.

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ABSTRACT. Symbolic Models applied to Synthetic Aperture Sonar images are proposed in order to assess the validity and reliability of use of such models and evaluate how effective they can be in terms of image classification and segmentation. We developed an approach for the description of sonar images where the pixels distribution can be transformed into points in the symbolic space in a similar way as symbolic space can encode a trajectory of a dynamical system. One of the main characteristic of approach is that points in the symbolic space are mapped respecting dynamical rules and, as a conse
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Pitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.

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Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MR
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Yazdi, Mahsa Badiee, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (2016): 1650025. http://dx.doi.org/10.4015/s1016237216500253.

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Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify
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Wang, Guodong, Jie Xu, Qian Dong, and Zhenkuan Pan. "Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/237648.

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Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active co
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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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Li, Yuan, Fu Cang Jia, Xiao Dong Zhang, Cheng Huang, and Huo Ling Luo. "Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation." Applied Mechanics and Materials 333-335 (July 2013): 1065–70. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1065.

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The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Est
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Xiang, Ming, Zhen Dong Cui, and Yuan Hong Wu. "A Fingerprint Image Segmentation Method Based on Fractal Dimension." Advanced Materials Research 461 (February 2012): 299–301. http://dx.doi.org/10.4028/www.scientific.net/amr.461.299.

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Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations
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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, et al. "Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/419018.

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This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in
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Shah, Nilima, Dhanesh Patel, and Pasi Fränti. "Fast Mumford-Shah Two-Phase Image Segmentation Using Proximal Splitting Scheme." Journal of Applied Mathematics 2021 (April 13, 2021): 1–13. http://dx.doi.org/10.1155/2021/6618505.

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The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metric
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Li, Jianzhang, Sven Nebelung, Björn Rath, Markus Tingart, and Jörg Eschweiler. "A novel combined level set model for automatic MR image segmentation." Current Directions in Biomedical Engineering 6, no. 3 (2020): 20–23. http://dx.doi.org/10.1515/cdbme-2020-3006.

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AbstractMedical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities ar
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Dissertations / Theses on the topic "Imagem segmentation"

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Monteiro, Fernando C. "Region-based spatial and temporal image segmentation." Doctoral thesis, Universidade do Porto, Faculdade de Engenharia, 2008. http://hdl.handle.net/10198/1835.

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This work discusses region-based representations for image and video sequence segmentation. It presents effective image segmentation techniques and demonstrates how these techniques may be integrated into algorithms that solve some of the motion segmentation problems. The region-based representation offers a way to perform a first level of abstraction and to reduce the number of elements to process with respect to the classical pixel-based representation. Motion segmentation is a fundamental technique for the analysis and the understanding of image sequences of real scenes. Motion segmentatio
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Silva, Maíra Saboia da. "Aglomeração de pixels pela transformada imagem floresta e sua aplicação em segmentação de fundo de imagens natuarais." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275713.

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Orientador: Alexandre Xavier Falcão<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-19T04:43:31Z (GMT). No. of bitstreams: 1 Silva_MairaSaboiada_M.pdf: 1907857 bytes, checksum: 515dfcdf136f4e9cc1c1d8b0690b3116 (MD5) Previous issue date: 2011<br>Resumo: Esta dissertação apresenta uma metodologia automática para separar objetos de interesse em imagens naturais. Objetos de interesse são definidos como os maiores objetos que se destacam com relação aos pixels em torno deles dentro de uma imagem. Estes objetos não preci
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Silva, Daniel Torres Couto Coimbra e. "LIDAR target detection and segmentation in road environment." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/11774.

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Mestrado em Engenharia Mecânica<br>In this project a comparative and exhaustive evaluation of several 2D laser data segmentation algorithms in road scenarios is performed. In a first stage, the segmentation algorithms are implemented using the ROS programming environment; the algorithms are applied to the raw laser scan data in order to extract groups of measurement points which share similar spatial properties and that probably will belong to one single object. Each algorithm has at least one threshold condition parameter that is configurable, and one of the goals is to try to determine the b
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Cappabianco, Fabio Augusto Menocci. "Segmentação de tecidos do cerebro humano em imagens de ressonancia magnetica e sua avaliação." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275845.

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Orientadores: Alexandre Xavier Falcão, Guido Costa Souza de Araujo<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-15T05:47:56Z (GMT). No. of bitstreams: 1 Cappabianco_FabioAugustoMenocci_D.pdf: 2671052 bytes, checksum: 751e1d22cedbe679c7440e3163af54d6 (MD5) Previous issue date: 2010<br>Resumo: A segmentação de tecidos cerebrais se tornou fundamental para a neurologia no tratamento e diagnose de pacientes. Muitas contribuições tem aprimorado as metodologias de segmentaçao mas, ainda ha muito a ser feito. De fato, ruídos
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Tavares, Anderson Carlos Moreira. "Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-03082017-230907/.

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Image segmentation consists on its partition into relevant regions, such as to isolate the pixels belonging to desired objects in the image domain, which is an important step for computer vision, medical image processing, and other applications. Many times automatic segmentation generates results with imperfections. The user can correct them by editing manually, interactively or can simply discard the segmentation and try to automatically generate another result by a different method. Interactive methods combine benefits from manual and automatic ones, reducing user effort and using its high-l
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Parraga, Adriane. "Atlas anatômico da região da cabeça e do pescoço : em direção à radioterapia adaptativa." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/15403.

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Em radioterapia externa, uma nova técnica chamada terapia de radiação de intensidade modulada - IMRT - permite delinear a dose de radiação em imagens de 2 ou 3 dimensões, delimitando de forma bastante precisa e não necessariamente uniforme a região a ser irradiada. Assim, ao mesmo tempo que o tumor é irradiado, é possível evitar a irradiação aos tecidos vizinhos íntegros (sãos), limitando os efeitos secundários do tratamento. Para que a radioterapia externa tenha sucesso usando a técnica IMRT, é fundamental delinear previamente de forma precisa o tumor e os órgãos sãos que devem ser protegidos
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Tambur, Gabriel dos Santos. "Metodologia para inspeção visual e automatica de carga em vagões ferroviarios." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258872.

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Orientador: Clesio Luis Tozzi<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-09T18:52:49Z (GMT). No. of bitstreams: 1 Tambur_GabrieldosSantos_M.pdf: 6836328 bytes, checksum: a9f9ee22e341720b36aab9a7e3566604 (MD5) Previous issue date: 2007<br>Resumo: Neste trabalho foi desenvolvida uma metodologia para inspeção visual e automática de carga de grãos em vagões ferroviários que utiliza técnicas de processamento de imagens. A metodologia inclui três etapas principais: a localização do vagão na
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Medeiros, Rafael Sachett. "Detecção de pele humana utilizando modelos estocásticos multi-escala de textura." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/70193.

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A detecção de gestos é uma etapa importante em aplicações de interação humanocomputador. Se a mão do usuário é detectada com precisão, tanto a análise quanto o reconhecimento do gesto de mão se tornam mais simples e confiáveis. Neste trabalho, descrevemos um novo método para detecção de pele humana, destinada a ser empregada como uma etapa de pré-processamento para segmentação de gestos de mão em sistemas que visam o seu reconhecimento. Primeiramente, treinamos os modelos de cor e textura de pele (material a ser identificado) a partir de um conjunto de treinamento formado por imagens de pele.
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Flores, Eliezer Soares. "Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/115219.

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Segmentação é uma etapa essencial para sistemas de pré-triagem de lesões melanocíticas. Neste trabalho, um novo método para segmentar lesões melanocíticas em imagens de câmera padrão (i.e., imagens macroscópicas) é apresentado. Inicialmente, para reduzir artefatos indesejáveis, os efeitos de sombra são atenuados na imagem macroscópica e uma présegmentação é obtida usando um esquema que combina a transformada wavelet com a transformada watershed. Em seguida, uma imagem de variação textural projetada para melhorar a discriminabilidade da lesão em relação ao fundo é obtida e a região présegmentad
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Braz, Caio de Moraes. "Segmentação de imagens pela transformada imagem-floresta com faixa de restrição geodésica." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-01062016-104354/.

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Vários métodos tradicionais de segmentação de imagens, como a transformada de watershed de marcado- res e métodos de conexidade fuzzy (Relative Fuzzy Connectedness- RFC, Iterative Relative Fuzzy Connected- ness - IRFC), podem ser implementados de modo eficiente utilizando o método em grafos da Transformada Imagem-Floresta (Image Foresting Transform - IFT). No entanto, a carência de termos de regularização de fronteira em sua formulação fazem com que a borda do objeto segmentado possa ser altamente irregular. Um modo de contornar isto é por meio do uso de restrições de forma do objeto, que favo
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Books on the topic "Imagem segmentation"

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Bhalerao, Abhir H. Multiresolution image segmentation. typescript, 1991.

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El-Baz, Ayman, Xiaoyi Jiang, and Suri Jasjit, eds. Biomedical Image Segmentation. CRC Press, 2016. http://dx.doi.org/10.4324/9781315372273.

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Roland, Wilson. Image segmentation and uncertainty. Research Studies Press, 1988.

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Morel, Jean Michel, and Sergio Solimini. Variational Methods in Image Segmentation. Birkhäuser Boston, 1995. http://dx.doi.org/10.1007/978-1-4684-0567-5.

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Köster, Klaus. Robust clustering and image segmentation. University of Birmingham, 1999.

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Gorte, Ben. Probabilistic segmentation of remotely sensed images. International Institute for Aerospace Survey and Earth Sciences (ITC), 1998.

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

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Bhanu, Bir. Genetic Learning for Adaptive Image Segmentation. Springer US, 1994.

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Bhanu, Bir. Genetic learning for adaptive image segmentation. Kluwer Academic Publishers, 1994.

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Bhanu, Bir, and Sungkee Lee. Genetic Learning for Adaptive Image Segmentation. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2774-9.

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Book chapters on the topic "Imagem segmentation"

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Soille, Pierre. "Segmentation." In Morphological Image Analysis. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05088-0_9.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_16.

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Awcock, G. J., and R. Thomas. "Segmentation." In Applied Image Processing. Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13049-8_5.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03174-2_10.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21817-4_10.

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Bräunl, Thomas, Stefan Feyrer, Wolfgang Rapf, and Michael Reinhardt. "Segmentation." In Parallel Image Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04327-1_7.

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Sundararajan, D. "Segmentation." In Digital Image Processing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_10.

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Soille, Pierre. "Segmentation." In Morphological Image Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03939-7_9.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-11565-7_10.

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Wang, Hongzhi, and Paul A. Yushkevich. "Guiding Automatic Segmentation with Multiple Manual Segmentations." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33418-4_53.

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Conference papers on the topic "Imagem segmentation"

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Sikander Hayat Khiyal, Malik, Aihab Khan, and Amna Bibi. "Modified Watershed Algorithm for Segmentation of 2D Images." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3349.

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With the repaid advancement of computer technology, the use of computer-based technologies is increasing in different fields of life. Image segmentation is an important problem in different fields of image processing and computer vision. Image segmentation is the process of dividing images according to its characteristic e.g., color and objects present in the images. Different methods are presented for image segmentation. The focus of this study is the watershed segmentation. The tool used in this study is MATLAB. Good result of watershed segmentation entirely relay on the image contrast. Imag
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Xu, Yue, Fei Yin, Zhaoxiang Zhang, and Cheng-Lin Liu. "Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/147.

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Layout analysis is a fundamental process in document image analysis and understanding. It consists of several sub-processes such as page segmentation, text line segmentation, baseline detection and so on. In this work, we propose a multi-task layout analysis method that use a single FCN model to solve the above three problems simultaneously. The FCN is trained to segment the document image into different regions and detect the center line of each text line by classifying pixels into different categories. By supervised learning on document images with pixel-wise labels, the FCN can extract disc
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Adão, Milena Menezes, Silvio Jamil F. Guimarães, and Zenilton K. G. Patrocı́nio Jr. "Evaluation of machine learning applied to the realignment of hierarchies for image segmentation." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8311.

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A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects can be located at different scales due to their size differences or to their distinct distances from the camera. In literature, many works have been developed to improve hierarchical image segmentation results. One possible solution is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of random forest and artificial neural network as regressors models to predict score values for regions belo
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Shum, Judy, Adam Goldhammer, Elena DiMartino, and Ender Finol. "CT Imaging of Abdominal Aortic Aneurysms: Semi-Automatic Vessel Wall Detection and Quantification of Wall Thickness." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192638.

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Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may provide useful information to predict rupture risk. Our procedure for estimating wall thickness in AAAs includes medical image segmentation and wall thickness detection. Image segmentation requires identifying and segmenting the luminal and outer wall boundaries of the blood vessels and wall thickness can be calculated by using intensity histograms and neural networks. The goal of this study is to develop an image-based, semi-automated method to trace the contours of the vessel wall and measure the wall
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Li, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, and Rick Goh. "Medical Image Segmentation using Squeeze-and-Expansion Transformers." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/112.

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Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transform
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Hage, Ilige S., and Ramsey F. Hamade. "Segregation of Cortical Bone’s Haversian Systems via Automated Image Segmentation." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51872.

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The lamellar or Haversian system is comprised mainly of fundamental units “osteons”. Haversian canals run through the center of the osteons where one or more blood vessels are located. The bone matrix is comprised of concentric lamellae surrounding Haversian canals. Those lamellae are punctuated by holes called lacunae, which are connected to each other through the canaliculi supplying nutrients. Haversian canals, lacunae and canaliculi of the Haversian system constitute the main porosities in cortical bone, thus it is advantageous to segregate those systems in segmented images that will help
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Mansilla, Lucy, and Paulo Miranda. "Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints." In XXVIII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2015.10003.

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Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new segmentation methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape). Our experimental results indicate substantial
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Zhupanska, Olesya I. "On the Role of Segmentation in the Analysis of Micro-CT Data of Impact Damage in the CFRP Composites." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11037.

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Abstract In this paper we discuss the role of different image segmentation methods that are used for the analysis of the micro computed tomography (micro-CT) data of damage in the carbon fiber reinforced polymer (CFRP) composites due to low velocity impact. Segmentation is one of the most critical steps in the image processing of the three dimensional (3D) CT data and accurate assessment of the damage from CT data depends to a great extent on the image segmentation. We have extensively studied low velocity impact damage in the CFRP composites using 3D CT. CFRP textile composite laminates were
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Da Silva, Caio Cesar Viana, and Jefersson Alex Dos Santos. "Open Set Semantic Segmentation of Remote Sensing Images." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12985.

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The development of computational vision approaches that exploit satellite imagery is relatively recent, mainly due to the limited availability of this type of image. In the area of remote sensing, applications that employ computational vision techniques are modeled for classification in closed set scenarios. However, the world is not purely closed set, many scenarios present classes that are not previously known by the algorithm, an open set scenario. Thus, the main objective of this paper is the study and development of semantic segmentation techniques considering the open set scenario applie
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Renner, Johan, Roland Gårdhagen, and Matts Karlsson. "Subject Specific In-Vivo CFD Estimated Aortic WSS: Comparison Between Manual and Automated Segmentation Methods." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192735.

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When making computational fluid dynamics (CFD) based estimations of wall shear stress (WSS) in the human aorta, medical image converting processes to 3D geometries are important as the result is strongly dependent on the quality of the geometry [1]. The image interpretation process or segmentation can be more or less automated; however in clinical work today the gold standard is to manually interpret the medical image information. This combined magnetic resonance imaging (MRI) and CFD method aims to estimate WSS in human arteries in-vivo as WSS is strongly linked to atherosclerosis [2]. More o
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Reports on the topic "Imagem segmentation"

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Shah, Jayant. Object Oriented Segmentation of Images. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290792.

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Kim, A., I. Pollak, H. Krim, and A. S. Willsky. Scale-Based Robust Image Segmentation. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada457838.

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Sharma, Karan. The Link Between Image Segmentation and Image Recognition. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.199.

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Ekeland, I., P. L. Lions, Y. Meyer, and J. M. Morel. Vibrations, Viscosity, Wavelets and Image Segmentation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada225750.

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Fosgate, C. H., H. Krim, A. S. Willsky, W. W. Irving, and R. D. Chaney. Multiscale Segmentation of SAR Imagery. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada458575.

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Shaw, K. B., and M. C. Lohrenz. A Survey of Digital Image Segmentation Algorithms. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada499374.

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Ghosh, Payel. Medical Image Segmentation Using a Genetic Algorithm. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.25.

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Xu, Y., and E. C. Uberbacher. 2-D image segmentation using minimum spanning trees. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/113991.

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Wu, Jin Chu, Michael Halter, Raghu N. Kacker, John T. Elliot, and Anne L. Plant. Measurement uncertainty in cell image segmentation data analysis. National Institute of Standards and Technology, 2013. http://dx.doi.org/10.6028/nist.ir.7954.

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Blackston, Matthew, Deniz Aykac, and Paul Hausladen. Progress Update on Segmentation of Neutron Tomographic Images, 2018. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1675057.

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