Relevant bibliographies by topics / Ground segmentation

Contents

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

Academic literature on the topic 'Ground segmentation'

Author: Grafiati
Published: 28 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Ground segmentation"

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Aguiar, P. M. Q., and J. M. F. Moura. "Figure-ground segmentation from occlusion." IEEE Transactions on Image Processing 14, no. 8 (2005): 1109–24. http://dx.doi.org/10.1109/tip.2005.851712.

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Kleinschmidt, A., C. Büchel, C. Hutton, and R. S. J. Frackowiak. "Hysteresis Effects in Figure-Ground Segmentation." NeuroImage 7, no. 4 (1998): S356. http://dx.doi.org/10.1016/s1053-8119(18)31189-3.

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Herzog, Michael H., Sabine Kopmann, and Andreas Brand. "Intact figure-ground segmentation in schizophrenia." Psychiatry Research 129, no. 1 (2004): 55–63. http://dx.doi.org/10.1016/j.psychres.2004.06.008.

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Milella, Annalisa, Giulio Reina, James Underwood, and Bertrand Douillard. "Visual ground segmentation by radar supervision." Robotics and Autonomous Systems 62, no. 5 (2014): 696–706. http://dx.doi.org/10.1016/j.robot.2012.10.001.

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Shen, Huiying, James Coughlan, and Volodymyr Ivanchenko. "Figure-ground segmentation using factor graphs." Image and Vision Computing 27, no. 7 (2009): 854–63. http://dx.doi.org/10.1016/j.imavis.2009.02.006.

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van der Putten, Joost, Fons van der Sommen, Jeroen de Groof, et al. "Modeling clinical assessor intervariability using deep hypersphere encoder–decoder networks." Neural Computing and Applications 32, no. 14 (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 multip
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Ying Yang, Michael, and Bodo Rosenhahn. "SUPERPIXEL CUT FOR FIGURE-GROUND IMAGE SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 387–94. http://dx.doi.org/10.5194/isprsannals-iii-3-387-2016.

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Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propos
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Ying Yang, Michael, and Bodo Rosenhahn. "SUPERPIXEL CUT FOR FIGURE-GROUND IMAGE SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 387–94. http://dx.doi.org/10.5194/isprs-annals-iii-3-387-2016.

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Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be properly defined by providing either user inputs or object models. In this paper, we propose a novel graph-based segmentation framework, called superpixel cut. The key idea is to formulate foreground segmentation as finding a subset of superpixels that partitions a graph over superpixels. The problem is formulated as Min-Cut. Therefore, we propos
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Warfield, Simon K., Kelly H. Zou, and William M. Wells. "Validation of image segmentation by estimating rater bias and variance." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1874 (2008): 2361–75. http://dx.doi.org/10.1098/rsta.2008.0040.

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The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a ‘ground truth’ or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically
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Kimchi, Ruth, and Mary A. Peterson. "Figure-Ground Segmentation Can Occur Without Attention." Psychological Science 19, no. 7 (2008): 660–68. http://dx.doi.org/10.1111/j.1467-9280.2008.02140.x.

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Dissertations / Theses on the topic "Ground segmentation"

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Vyas, Aseem. "Medical Image Segmentation by Transferring Ground Truth Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32431.

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The segmentation of medical images is a difficult task due to the inhomogeneous intensity variations that occurs during digital image acquisition, the complicated shape of the object, and the medical expert’s lack of semantic knowledge. Automated segmentation algorithms work well for some medical images, but no algorithm has been general enough to work for all medical images. In practice, most of the time the segmentation results are corrected by the experts before the actual use. In this work, we are motivated to determine how to make use of manually segmented data in automatic segmentation.
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Nordlund, Peter. "Figure-ground segmentation using multiple cues." Doctoral thesis, Stockholm : Tekniska högsk, 1998. http://www.lib.kth.se/abs98/nord0615.pdf.

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Todorovic, Sinisa. "Statistical modeling and segmentation of sky/ground images." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000616.

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Rodolpho, Beatriz Leão. "Ground truth determination for segmentation of tomographic volumes using interpolation." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10832.

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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica<br>Optical projection tomographic microscopy allows for a 3D analysis of individual cells, making it possible to study its morphology. The 3D imagining technique used in this thesis uses white light excitation to image stained cells, and is referred to as single-cell optical computed tomography (cell CT). Studies have shown that morphological characteristics of the cell and its nucleus are deterministic in cancer diagnoses. For a more complete and accurate analysis of these characteristics, a fully-automated analysis of
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Brown, Ryan Charles. "IRIS: Intelligent Roadway Image Segmentation." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49105.

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The problem of roadway navigation and obstacle avoidance for unmanned ground vehicles has typically needed very expensive sensing to operate properly. To reduce the cost of sensing, it is proposed that an algorithm be developed that uses a single visual camera to image the roadway, determine where the lane of travel is in the image, and segment that lane. The algorithm would need to be as accurate as current lane finding algorithms as well as faster than a standard k- means segmentation across the entire image. This algorithm, named IRIS, was developed and tested on several sets of roadway ima
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Claudio, Pedro. "Automated Visual Database Creation for a Ground Vehicle Simulator." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2638.

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This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are u
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Kumar, Prashant. "Online 3D Reconstruction and Ground Segmentation using Drone based Long Baseline Stereo Vision System." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/98009.

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This thesis presents online 3D reconstruction and ground segmentation using unmanned aerial vehicle (UAV) based stereo vision. For this purpose, a long baseline stereo vision system has been designed and built. Application of this system is to work as part of an air and ground based multi-robot autonomous terrain surveying project at Unmanned Systems Lab (USL), Virginia Tech, to act as a first responder robotic system in disaster situations. Areas covered by this thesis are design of long baseline stereo vision system, study of stereo vision raw output, techniques to filter out outliers from r
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Brandin, Martin, and Roger Hamrén. "Classification of Ground Objects Using Laser Radar Data." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1572.

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<p>Accurate 3D models of natural environments are important for many modelling and simulation applications, for both civilian and military purposes. When building 3D models from high resolution data acquired by an airborne laser scanner it is de-sirable to separate and classify the data to be able to process it further. For example, to build a polygon model of a building the samples belonging to the building must be found.</p><p>In this thesis we have developed, implemented (in IDL and ENVI), and evaluated algorithms for classification of buildings, vegetation, power lines, posts, and roads. T
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Turner, Russell Sean School of Biological Earth &amp Environmental Science UNSW. "An airborne Lidar canopy segmentation approach for estimating above-ground biomass in coastal eucalypt forests." Awarded by:University of New South Wales. School of Biological, Earth and Environmental Science, 2006. http://handle.unsw.edu.au/1959.4/27362.

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There is growing interest in airborne lidar for forest carbon accounting and precision forestry purposes. Airborne lidar systems offer a cost-effective, versatile, operationally flexible and robust sampling tool for forest managers. The objective of this study was to develop and test lidar canopy surface enhancement and segmentation processes for estimating dominant above-ground biomass (DAB) in a harvested eucalypt forest on the Central Coast of New South Wales (Australia). The Crown Infill, Trim and Smooth (CITS) process, incorporating a series of filters, algorithms, and selective multi-sta
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Christie, Gordon A. "Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/83807.

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Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these c
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Books on the topic "Ground segmentation"

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Verheggen, Pieter Paul. Nieuwe Nederlanders: Etnomarketing voor diversiteitsbeleid. Samsom, 2001.

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McVey, Dominic, and Adam Crosier. Generating insight and building segmentation models in social marketing. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198717690.003.0007.

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This chapter introduces the concepts of insight and segmentation and outlines their contribution to understanding audiences and targeting interventions to ensure effective social marketing. Studying the target group at the programme scoping stage develops an appreciation of the challenges they face every day. This insight and knowledge will help with understanding the audience ‘exchange’ and with building strong message propositions which will be relevant and salient to the target group. Segmentation can generate new insights into the drivers and barriers to change and help target the right gr
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Moore, Gordon, John A. Quelch, and Emily Boudreau. Consumer Segmentation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190886134.003.0006.

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Chapter 6 discusses the use of segmentation strategies in healthcare as tools for understanding differences among groups of consumers. Segmentation makes it possible for a business to target its customers and design, market, price, and distribute its products in the most efficient and effective way. By segmenting the population they care for, healthcare organizations and even individual clinicians can focus their efforts on what is important to those they most want to care for. After highlighting several examples of successful segmentation strategies from different kinds of healthcare firms, t
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Caplovitz, Gideon P., Alex Boswell, and Kyle Killebrew. The Bar-Cross-Ellipse Illusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0012.

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This chapter describes a multistable stimulus that reveals the complexity of visual processing that underlies the determination of an object’s form and motion. The stimulus is constructed by placing an ellipse on a uniform background and then partially occluding it with four squares, each with the same color as the background. When the ellipse is made to rotate, it can be perceived in any of four distinct ways, and, over time, the percept will switch between them. Each percept corresponds to a distinct figure-ground segmentation that is determined on the basis of contour ownership and how diff
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Mulder, S. H. Euromarketing: Nieuwe Nederlanders: Feiten, cijfers en trends (Trendreeks). Motivaction Amsterdam, 1998.

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Demographic Targeting: The Essential Role of Population Groups in Retail Marketing. Ashgate Publishing, 2002.

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Castellani, Claudia, and Marianne Wootton. Crustacea: Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0021.

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This chapter provides an introduction to the Crustacea, one of the most abundant and diverse components of the plankton. Within a single net-haul, the vast diversity within this group, coupled with the large number of species and the morphological similarity both between species and between developmental stages, can often pose a significant identification challenge even to experienced taxonomists. Although all Crustacea originally share a common body plan, their morphology can differ quite markedly due to different degrees of expression of body segmentation patterns and as a result of the loss
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Bouassida, Ines, and Abdel-Rahmen El Lahga. Public–Private Wage Disparities, Employment, and Labor Market Segmentation in Tunisia. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799863.003.0004.

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The dysfunction of the Tunisian labor market is exacerbated particularly by the segmentation between public and private sector employment. These different segments differ in terms of returns to human capital, social protection and mobility, affecting career development and the wage structure in the economy. In this chapter, we present the patterns of wage distribution in Tunisia across important socioeconomic groups and a detailed analysis of the wage gap between public and private sectors. Our results show particularly that while in the bottom sector of the wage distribution the positive wage
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Vogt, Manuel. Mobilization and Conflict in Multiethnic States. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190065874.001.0001.

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Why are ethnic movements more likely to turn violent in some multiethnic countries than in others? Focusing on the long-term legacies of European colonialism, this book presents two ideal-typical logics of ethnic group mobilization—one of violent competition and another of nonviolent emancipatory opposition. The book’s theory first explains why ethnic grievances are translated into either violent or nonviolent forms of conflict as a function of distinct ethnic cleavage types, resulting from different colonial experiences. Violent intergroup conflict is least likely where settler colonialism re
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Radner, Hilary, and Alistair Fox. Film Analysis: Image and Movement. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422888.003.0002.

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This chapter assesses Raymond Bellour’s contribution to the area of research known as “film analysis,” arguing that it is best understood as an “art” rather than a scientific practice. Grounded in the French tradition of “explication du texte” as a means of approaching literature, Bellour was among the first film scholars to bring a French literary sensibility to the analysis of Classical Hollywood film, which enabled him to recognize the rhetorical refinements of the cinematic medium and its potential for poetic expression. The chapter explores the significant concepts that define Bellour’s a
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Book chapters on the topic "Ground segmentation"

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Elgammal, Ahmed. "Figure-Ground Segmentation—Pixel-Based." In Visual Analysis of Humans. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-997-0_3.

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Leibe, Bastian. "Figure-Ground Segmentation—Object-Based." In Visual Analysis of Humans. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-997-0_4.

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Kohlberger, Timo, Vivek Singh, Chris Alvino, Claus Bahlmann, and Leo Grady. "Evaluating Segmentation Error without Ground Truth." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_65.

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Nordlund, Peter, and Jan-Olof Eklundh. "Real-Time Maintenance of Figure-Ground Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-49256-9_8.

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Hodge, Victoria, Garry Hollier, John Eakins, and Jim Austin. "Eliciting Perceptual Ground Truth for Image Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11788034_33.

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Zhou, Jinghao, Sukmoon Chang, Dimitris N. Metaxas, Binsheng Zhao, Lawrence H. Schwartz, and Michelle S. Ginsberg. "Automatic Detection and Segmentation of Ground Glass Opacity Nodules." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11866565_96.

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Maire, Michael. "Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15552-9_33.

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Packer, Ben, Stephen Gould, and Daphne Koller. "A Unified Contour-Pixel Model for Figure-Ground Segmentation." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15555-0_25.

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Liang, Yuyu, Mengjie Zhang, and Will N. Browne. "A Supervised Figure-Ground Segmentation Method Using Genetic Programming." In Applications of Evolutionary Computation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3_40.

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Liang, Yuyu, Mengjie Zhang, and Will N. Browne. "Multi-objective Genetic Programming for Figure-Ground Image Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28270-1_12.

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Conference papers on the topic "Ground segmentation"

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Chen Tongtong, Dai Bin, Liu Daxue, Zhang Bo, and Liu Qixu. "3D LIDAR-based ground segmentation." In 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166587.

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Beuren, Arlete Teresinha, Alceu de Souza Britto, and Jacques Facon. "Sky/Ground Segmentation Using Different Approaches." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206876.

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Hofbauer, Heinz, Fernando Alonso-Fernandez, Peter Wild, Josef Bigun, and Andreas Uhl. "A Ground Truth for Iris Segmentation." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.101.

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Ren, Xiaofeng, and Jitendra Malik. "Tracking as Repeated Figure/Ground Segmentation." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383177.

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Roberts, G. A. "Model Guided Segmentation Of Ground Targets." In Applications of Artificial Intelligence V, edited by John F. Gilmore. SPIE, 1987. http://dx.doi.org/10.1117/12.940620.

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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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Kuettel, D., and V. Ferrari. "Figure-ground segmentation by transferring window masks." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247721.

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An, Chang, Dawei Yin, and Henry S. Baird. "Document Segmentation Using Pixel-Accurate Ground Truth." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.69.

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Yuan, Ding, Jingjing Qiang, Jianfei Li, Hong Zhang, and Xiaoyan Luo. "Figure-ground Image Segmentation via Semantic Information." In 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2019. http://dx.doi.org/10.1109/rcar47638.2019.9043955.

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Hernandez, Jorge, and Beatriz Marcotegui. "Point cloud segmentation towards urban ground modeling." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137562.

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Reports on the topic "Ground segmentation"

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Carlberg, Matthew, James Andrews, Peiran Gao, and Avideh Zakhor. Fast Surface Reconstruction and Segmentation with Ground-Based and Airborne LIDAR Range Data. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada538860.

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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collec
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