Relevant bibliographies by topics / Segmentation models

Academic literature on the topic 'Segmentation models'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Segmentation models"

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Nishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, and Hiroshi Yamada. "Deep generative models for automated muscle segmentation in computed tomography scanning." PLOS ONE 16, no. 9 (September 10, 2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.

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Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.
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Iyer, Aditi, Maria Thor, Ifeanyirochukwu Onochie, Jennifer Hesse, Kaveh Zakeri, Eve LoCastro, Jue Jiang, et al. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology 67, no. 2 (January 17, 2022): 024001. http://dx.doi.org/10.1088/1361-6560/ac4000.

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Abstract Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.
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van der Putten, Joost, Fons van der Sommen, Jeroen de Groof, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, and Peter H. N. de With. "Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks." Neural Computing and Applications 32, no. 14 (November 21, 2019): 10705–17. http://dx.doi.org/10.1007/s00521-019-04607-w.

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AbstractIn medical imaging, a proper gold-standard ground truth as, e.g., annotated segmentations by assessors or experts is lacking or only scarcely available and suffers from large intervariability in those segmentations. Most state-of-the-art segmentation models do not take inter-observer variability into account and are fully deterministic in nature. In this work, we propose hypersphere encoder–decoder networks in combination with dynamic leaky ReLUs, as a new method to explicitly incorporate inter-observer variability into a segmentation model. With this model, we can then generate multiple proposals based on the inter-observer agreement. As a result, the output segmentations of the proposed model can be tuned to typical margins inherent to the ambiguity in the data. For experimental validation, we provide a proof of concept on a toy data set as well as show improved segmentation results on two medical data sets. The proposed method has several advantages over current state-of-the-art segmentation models such as interpretability in the uncertainty of segmentation borders. Experiments with a medical localization problem show that it offers improved biopsy localizations, which are on average 12% closer to the optimal biopsy location.
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Olson, Richard K., and Janice M. Keenan. "Segmentation in models of reading." Behavioral and Brain Sciences 8, no. 4 (December 1985): 719–20. http://dx.doi.org/10.1017/s0140525x00045866.

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Gwadera, Robert, Aristides Gionis, and Heikki Mannila. "Optimal segmentation using tree models." Knowledge and Information Systems 15, no. 3 (July 28, 2007): 259–83. http://dx.doi.org/10.1007/s10115-007-0091-5.

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Chou, Glen, Necmiye Ozay, and Dmitry Berenson. "Incremental Segmentation of ARX Models." IFAC-PapersOnLine 51, no. 15 (2018): 587–92. http://dx.doi.org/10.1016/j.ifacol.2018.09.222.

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Golovinskiy, Aleksey, and Thomas Funkhouser. "Consistent segmentation of 3D models." Computers & Graphics 33, no. 3 (June 2009): 262–69. http://dx.doi.org/10.1016/j.cag.2009.03.010.

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SIMMONS, D. "Categorical models of image segmentation." Ophthalmic and Physiological Optics 11, no. 3 (July 1991): 282. http://dx.doi.org/10.1016/0275-5408(91)90113-w.

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Mohd Ghani, Noor Ain Syazwani, and Abdul Kadir Jumaat. "Selective Segmentation Model for Vector-Valued Images." Journal of Information and Communication Technology 21, No.2 (April 7, 2022): 149–73. http://dx.doi.org/10.32890/jict2022.21.2.1.

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One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.
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Tseng, Din-Chang, and Ruei-Lung Chen. "Mutiscale Texture Segmentation Using Contextual Hidden Markov Tree Models." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 198–205. http://dx.doi.org/10.7763/ijmlc.2015.v5.507.

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Dissertations / Theses on the topic "Segmentation models"

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Camilleri, Liberato. "Statistical models for market segmentation." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441119.

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Heiler, Matthias. "Image models for segmentation and recognition." [S.l. : s.n.], 2006. http://madoc.bib.uni-mannheim.de/madoc/volltexte/2006/1306.

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Eslami, Seyed Mohammadali. "Generative probabilistic models for object segmentation." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8898.

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One of the long-standing open problems in machine vision has been the task of ‘object segmentation’, in which an image is partitioned into two sets of pixels: those that belong to the object of interest, and those that do not. A closely related task is that of ‘parts-based object segmentation’, where additionally each of the object’s pixels are labelled as belonging to one of several predetermined parts. There is broad agreement that segmentation is coupled to the task of object recognition. Knowledge of the object’s class can lead to more accurate segmentations, and in turn accurate segmentations can be used to obtain higher recognition rates. In this thesis we focus on one side of this relationship: given the object’s class and its bounding box, how accurately can we segment it? Segmentation is challenging primarily due to the huge amount of variability one sees in images of natural scenes. A large number of factors combine in complex ways to generate the pixel intensities that make up any given image. In this work we approach the problem by developing generative probabilistic models of the objects in question. Not only does this allow us to express notions of variability and uncertainty in a principled way, but also to separate the problems of model design and inference. The thesis makes the following contributions: First, we demonstrate an explicit probabilistic model of images of objects based on a latent Gaussian model of shape. This can be learned from images in an unsupervised fashion. Through experiments on a variety of datasets we demonstrate the advantages of explicitly modelling shape variability. We then focus on the task of constructing more accurate models of shape. We present a type of layered probabilistic model that we call a Shape Boltzmann Machine (SBM) for the task of modelling foreground/background (binary) and parts-based (categorical) shapes. We demonstrate that it constitutes the state-of-the-art and characterises a ‘strong’ model of shape, in that samples from the model look realistic and that it generalises to generate samples that differ from training examples. Finally, we demonstrate how the SBM can be used in conjunction with an appearance model to form a fully generative model of images of objects. We show how parts-based object segmentations can be obtained simply by performing probabilistic inference in this joint model. We apply the model to several challenging datasets and find that its performance is comparable to the state-of-the-art.
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Li, Zhi. "Variational image segmentation, inpainting and denoising." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.

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Variational methods have attracted much attention in the past decade. With rigorous mathematical analysis and computational methods, variational minimization models can handle many practical problems arising in image processing, such as image segmentation and image restoration. We propose a two-stage image segmentation approach for color images, in the first stage, the primal-dual algorithm is applied to efficiently solve the proposed minimization problem for a smoothed image solution without irrelevant and trivial information, then in the second stage, we adopt the hillclimbing procedure to segment the smoothed image. For multiplicative noise removal, we employ a difference of convex algorithm to solve the non-convex AA model. And we also improve the non-local total variation model. More precisely, we add an extra term to impose regularity to the graph formed by the weights between pixels. Thin structures can benefit from this regularization term, because it allows to adapt the weights value from the global point of view, thus thin features will not be overlooked like in the conventional non-local models. Since now the non-local total variation term has two variables, the image u and weights v, and it is concave with respect to v, the proximal alternating linearized minimization algorithm is naturally applied with variable metrics to solve the non-convex model efficiently. In the meantime, the efficiency of the proposed approaches is demonstrated on problems including image segmentation, image inpainting and image denoising.
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Yeo, Si Yong. "Implicit deformable models for biomedical image segmentation." Thesis, Swansea University, 2011. https://cronfa.swan.ac.uk/Record/cronfa42416.

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In this thesis, new methods for the efficient segmentation of images are presented. The proposed methods are based on the deformable model approach, and can be used efficiently in the segmentation of complex geometries from various imaging modalities. A novel deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions is presented. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contributes to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and give the deformable model a high invariance in initialization configurations. The voxel interactions across the whole image domain provides a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force held allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, it is shown that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. A comparative study on the segmentation of various geometries with different topologies from both synthetic and real images is provided, and the proposed method is shown to achieve significant improvements against several existing techniques. A robust framework for the segmentation of vascular geometries is described. In particular, the framework consists of image denoising, optimal object edge representation, and segmentation using implicit deformable model. The image denoising is based on vessel enhancing diffusion which can be used to smooth out image noise and enhance the vessel structures. The image object boundaries are derived using an edge detection technique which can produce object edges of single pixel width. The image edge information is then used to derive the geometric interaction field for optimal object edge representation. The vascular geometries are segmented using an implict deformable model. A region constraint is added to the deformable model which allows it to easily get around calcified regions and propagate across the vessels to segment the structures efficiently. The presented framework is ai)plied in the accurate segmentation of carotid geometries from medical images. A new segmentation model with statistical shape prior using a variational approach is also presented in this thesis. The proposed model consists of an image attraction force that propagates contours towards image object boundaries, and a global shape force that attracts the model towards similar shapes in the statistical shape distribution. The image attraction force is derived from gradient vector interactions across the whole image domain, which makes the model more robust to image noise, weak edges and initializations. The statistical shape information is incorporated using kernel density estimation, which allows the shape prior model to handle arbitrary shape variations. It is shown that the proposed model with shape prior can be used to segment object shapes from images efficiently.
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Barker, Simon A. "Image segmentation using Markov random field models." Thesis, University of Cambridge, 1998. https://www.repository.cam.ac.uk/handle/1810/272037.

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Barker, S. A. "Unsupervised image segmentation using Markov Random Field models." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596368.

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The development of a fully unsupervised algorithm to achieve image segmentation is the central theme of this dissertation. Existing literature falls short of such a goal providing many algorithms capable of solving a subset of this highly challenging problem. Unsupervised segmentation is the process of identifying and locating the constituent regions of an observed image, while having no prior knowledge of the number of regions. The problem can be formulated in a Bayesian framework and through the use of an assumed model unsupervised segmentation can be posed as a problem of optimisation. This is the approach pursued throughout this dissertation. Throughout the literature, the commonly adopted model is an hierarchical image model whose underlying components are various forms of Markov Random Fields. Gaussian Markov Random Field models are used to model the textural content of the observed image's regions, while a Potts model provides a regularisation function for the segmentation. The optimisation of such highly complicated models is a topic that has challenged researchers for several decades. The contribution of this thesis is the introduction of new techniques allowing unsupervised segmentation to be carried using a single optimisation process. It is hoped that these algorithms will facilitate the future study of hierarchical image models and in particular the discovery of further models capable of more closely fitting real world data. The extensive literature surrounding Markov Random Field models and their optimisation is reviewed early in this dissertation, as is the literature concerning the selection of features to identify the textural content of an observed image. In the light of these reviews new algorithms are proposed that achieve a fusion between concepts originating in both these areas. Algorithms previously applied in statistical mechanics form an important part of this work. The use of various Markov Chain Monte Carlo algorithms is prevalent and in particular, the reversible jump sampling algorithm is of great significance. It is the combination of several of these algorithms to form a single optimisation framework that lies at the heart of the most successful algorithms presented here.
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Shepherd, T. "Dynamical models and machine learning for supervised segmentation." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18729/.

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This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials.
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Ljolje, A. "Intonation and phonetic segmentation using hidden Markov models." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377219.

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Chalana, Vikram. "Deformable models for segmentation of medical ultrasound images /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8025.

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Books on the topic "Segmentation models"

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Li, Jia. Image Segmentation and Compression Using Hidden Markov Models. Boston, MA: Springer US, 2000.

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Li, Jia, and Robert M. Gray. Image Segmentation and Compression Using Hidden Markov Models. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4497-5.

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Dickens, W. T. Labor market segmentation theory: Reconsidering the evidence. Cambridge, MA: National Bureau of Economic Research, 1992.

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Lang, Kevin. Labor market segmentation, wage dispersion and unemployment. Cambridge, MA: National Bureau of Economic Research, 1992.

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Charters, Graham Castree. Segmentation and classification in automated chromosome analysis using trainable models. Manchester: University of Manchester, 1994.

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Hruschka, Harald. Abgrenzung und Segmentierung von Markten auf der Grundlage unscharfer Klassifikationsverfahren. Thun: H. Deutsch, 1985.

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Stigler, Matthieu. Understanding the ADR premium under market segmentation. New Delhi: National Institute of Public Finance and Policy, 2010.

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Knetter, Michael. The segmentation of international markets: Evidence from The economist. Cambridge, MA: National Bureau of Economic Research, 1997.

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Guo ji shi chang qu ge hua zhi shi zheng yan jiu. Taibei Shi: Cai tuan fa ren Zhonghua jing ji yan jiu yuan, 1986.

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Jaklič, Aleš. Segmentation and recovery of superquadrics. Dordrecht: Kluwer Academic Publishers, 2000.

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Book chapters on the topic "Segmentation models"

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Meinhardt, Hans. "Models of Segmentation." In Somites in Developing Embryos, 179–89. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-2013-3_14.

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Barga, Roger, Valentine Fontama, and Wee Hyong Tok. "Customer Segmentation Models." In Predictive Analytics with Microsoft Azure Machine Learning, 129–42. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0445-0_7.

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Barga, Roger, Valentine Fontama, and Wee Hyong Tok. "Customer Segmentation Models." In Predictive Analytics with Microsoft Azure Machine Learning, 207–20. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1200-4_10.

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Blahut, Steven, and Jeff Niemira. "Segmentation and Choice Models." In Quantitative Methods in Pharmaceutical Research and Development, 317–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48555-9_7.

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Paczkowski, Walter R. "Price segmentation: Basic models." In Pricing Analytics, 185–212. 1 Edition. | New York : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315178349-11.

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Paczkowski, Walter R. "Price segmentation: Advanced models." In Pricing Analytics, 213–28. 1 Edition. | New York : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315178349-12.

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Li, Jia, and Robert M. Gray. "Testing Models." In Image Segmentation and Compression Using Hidden Markov Models, 91–102. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4497-5_6.

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Mitiche, Amar, and Ismail Ben Ayed. "Image Models." In Variational and Level Set Methods in Image Segmentation, 83–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15352-5_5.

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Gopal, Sanjay S. "Finite Mixture Models." In Advanced Algorithmic Approaches to Medical Image Segmentation, 341–61. London: Springer London, 2002. http://dx.doi.org/10.1007/978-0-85729-333-6_6.

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ElTanboly, Ahmed, Ali Mahmoud, Ahmed Shalaby, Magdi El-Azab, Mohammed Ghazal, Robert Keynton, Ayman El-Baz, and Jasjit S. Suri. "Deformable Models and Image Segmentation." In Level Set Method in Medical Imaging Segmentation, 207–60. Boca Raton : Taylor & Francis, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/b22435-8.

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Conference papers on the topic "Segmentation models"

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Liew, Alan Wee-Chung, Hong Yan, Tuan D. Pham, and Xiaobo Zhou. "Automated cDNA Microarray Image Segmentation." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816637.

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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 belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation witch considering all user-defined segmentations that exist in the ground-truth. Experimental results are presented for two different hierarchical segmentation methods. Moreover, an analysis of the adoption of different combination of mid-level features to describe regions and different architectures from random forest and artificial neural network to train regressors models. Experimental results have point out that the use of new proposed score was able to improve final segmentation results.
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Sapna Varshney, S., N. Rajpal, and R. Purwar. "Comparative study of image segmentation techniques and object matching using segmentation." In 2009 International Conference on Methods and Models in Computer Science (ICM2CS). IEEE, 2009. http://dx.doi.org/10.1109/icm2cs.2009.5397985.

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Gwadera, Robert, Aristides Gionis, and Heikki Mannila. "Optimal Segmentation Using Tree Models." In Sixth International Conference on Data Mining (ICDM'06). IEEE, 2006. http://dx.doi.org/10.1109/icdm.2006.122.

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Trulls, Eduard, Stavros Tsogkas, Iasonas Kokkinos, Alberto Sanfeliu, and Francesc Moreno-Noguer. "Segmentation-Aware Deformable Part Models." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.29.

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Reynar, Jeffrey C. "Statistical models for topic segmentation." In the 37th annual meeting of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 1999. http://dx.doi.org/10.3115/1034678.1034735.

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Yu, Donggang, Tuan D. Pham, Hong Yan, Wei Lai, Denis I. Crane, Tuan D. Pham, and Xiaobo Zhou. "Segmentation and Reconstruction of Cultured Neuron Skeleton." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816626.

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Kharabe, S. R., Poonamkumar S. Hanwate, K. P. Kaliyamurthie, and Dhananjay S. Gaikwad. "Human image segmentation." In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017. http://dx.doi.org/10.1109/icammaet.2017.8186647.

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Ostrowski, David Alfred. "Model segmentation for numerical prediction." In 2009 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA). IEEE, 2009. http://dx.doi.org/10.1109/hima.2009.4937821.

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Wehrwein, Scott, and Richard Szeliski. "Video Segmentation with Background Motion Models." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.96.

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Reports on the topic "Segmentation models"

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Sclove, Stanley L. Statistical Models and Methods for Cluster Analysis and Image Segmentation. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada169145.

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Lecumberry, Federico, Alvaro Pardo, and Guillermo Sapiro. Simultaneous Object Classification and Segmentation with High-Order Multiple Shape Models. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada513239.

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He, Ping, and Jun Zheng. Segmentation of TIBIA Bone in Ultrasound Images Using Active Shape Models. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada412425.

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Kolar, Jachym, Yang Liu, and Elizabeth Shriberg. Speaker Adaptation of Language Models for Automatic Dialog Act Segmentation of Meetings. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada469307.

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.
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Blundell, S., and Philip Devine. Creation, transformation, and orientation adjustment of a building façade model for feature segmentation : transforming 3D building point cloud models into 2D georeferenced feature overlays. Engineer Research and Development Center (U.S.), January 2020. http://dx.doi.org/10.21079/11681/35115.

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Liu, Lifeng, and Stan Sclaroff. Region Segmentation via Deformable Model-Guided Split and Merge. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada451541.

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Garris, Michael D. Component-based handprint segmentation using adaptive writing style model. Gaithersburg, MD: National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5843.

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Li, Qunhua, Chris Fraley, Roger E. Bumgarner, Ka Y. Yeung, and Adrian E. Raftery. Donuts, Scratches and Blanks: Robust Model-Based Segmentation of Microarray Images. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada454864.

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Wehrens, Ron, Lutgarde M. Buydens, Chris Fraley, and Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada459638.

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