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 (August 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 (May 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 (November 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 (May 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 (June 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, 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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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 propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.
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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 propose a novel cost function that simultaneously minimizes the inter-class similarity while maximizing the intra-class similarity. This cost function is optimized using parametric programming. After a small learning step, our approach is fully automatic and fully bottom-up, which requires no high-level knowledge such as shape priors and scene content. It recovers coherent components of images, providing a set of multiscale hypotheses for high-level reasoning. We evaluate our proposed framework by comparing it to other generic figure-ground segmentation approaches. Our method achieves improved performance on state-of-the-art benchmark databases.
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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 (April 11, 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, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary.
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Kimchi, Ruth, and Mary A. Peterson. "Figure-Ground Segmentation Can Occur Without Attention." Psychological Science 19, no. 7 (July 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. The key idea is to transfer the ground truth segmentation from the database of train images to a given test image. The ground truth segmentation of MR images is done by experts. The process includes a hierarchical image decomposition approach that performs the shape matching of test images at several levels, starting with the image as a whole (i.e. level 0) and then going through a pyramid decomposition (i.e. level 1, level 2, etc.) with the database of the train images and the given test image. The goal of pyramid decomposition is to find the section of the training image that best matches a section of the test image of a different level. After that, a re-composition approach is taken to place the best matched sections of the training image to the original test image space. Finally, the ground truth segmentation is transferred from the best training images to their corresponding location in the test image. We have tested our method on a hip joint MR image database and the experiment shows successful results on level 0, level 1 and level 2 re-compositions. Results improve with deeper level decompositions, which supports our hypotheses.
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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
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 the single-cell 3D tomographic images can be done. The first step is segmenting the image into the different cell components. To assess how accurate the segmentation is, there is a need to determine ground truth of the automated segmentation. This dissertation intends to expose a method of obtaining ground truth for 3D segmentation of single cells. This was achieved by developing a software in CSharp. The software allows the user to input a visual segmentation of each 2D slice of a 3D volume by using a pen to trace the visually identified boundary of a cell component on a tablet. With this information, the software creates a segmentation of a 3D tomographic image that is a result of human visual segmentation. To increase the speed of this process, interpolation algorithms can be used. Since it is very time consuming to draw on every slice the user can skip slices. Interpolation algorithms are used to interpolate on the skipped slices. Five different interpolation algorithms were written: Linear Interpolation, Gaussian splat, Marching Cubes, Unorganized Points, and Delaunay Triangulation. To evaluate the performance of each interpolation algorithm the following evaluation metrics were used: Jaccard Similarity, Dice Coefficient, Specificity and Sensitivity.After evaluating each interpolation method we concluded that linear interpolation was the most accurate interpolation method, producing the best segmented volume for a faster ground truth determination method.
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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 images. The algorithm was tested for its accuracy and speed, and was found to be better than 86% accurate across all data sets for an optimal choice of algorithm parameters. IRIS was also found to be faster than a k-means segmentation across the entire image. IRIS was found to be adequate for fulfilling the design goals for the algorithm. IRIS is a feasible system for lane identification and segmentation, but it is not currently a viable system. More work to increase the speed of the algorithm and the accuracy of lane detection and to extend the inherent lane model to more complex road types is needed. IRIS represents a significant step forward in the single camera roadway perception field.
Master of Science
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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 used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering
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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 raw stereo vision output, a 3D reconstruction method and a study to improve running time by controlling the density of point clouds. Presented work makes use of filtering methods and implementations in Point Cloud Library (PCL) and feature matching on graphics processing unit (GPU) using OpenCV with CUDA. Besides 3D reconstruction, the challenge in the project was speed and several steps and ideas are presented to achieve it. Presented 3D reconstruction algorithm uses feature matching in 2D images, converts keypoints to 3D using disparity images, estimates rigid body transformation between matched 3D keypoints and fits point clouds. To correct and control orientation and localization errors, it fits re-projected UAV positions on GPS recorded UAV positions using iterative closest point (ICP) algorithm as the correction step. A new but computationally intensive process of use of superpixel clustering and plane fitting to increase resolution of disparity images to sub-pixel resolution is also presented. Results section provides accuracy of 3D reconstruction results. The presented process is able to generate application acceptable semi-dense 3D reconstruction and ground segmentation at 8-12 frames per second (fps). In 3D reconstruction of an area of size 25 x 40 m2, with UAV flight altitude of 23 m, average obstacle localization error and average obstacle size/dimension error is found to be of 17 cm and 3 cm, respectively.
MS
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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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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.

In this thesis we have developed, implemented (in IDL and ENVI), and evaluated algorithms for classification of buildings, vegetation, power lines, posts, and roads. The data is gridded and interpolated and a ground surface is estimated before the classification. For the building classification an object based approach was used unlike most classification algorithms which are pixel based. The building classifica-tion has been tested and compared with two existing classification algorithms.

The developed algorithm classified 99.6 % of the building pixels correctly, while the two other algorithms classified 92.2 % respective 80.5 % of the pixels correctly. The algorithms developed for the other classes were tested with thefollowing result (correctly classified pixels): vegetation, 98.8 %; power lines, 98.2 %; posts, 42.3 %; roads, 96.2 %.

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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-stage smoothing, was used to enhance lidar canopy surfaces prior to segmentation. Canopy segmentation was achieved using a vertical crown template approach termed the Spatially and Morphologically Isolated Crest (SMIC) process. SMIC delineates dominant tree crowns by detecting elevated crown crests within a 3D lidar canopy surface. Consolidated crown units constitute the basic sampling, analysis and reporting units for wall-to-wall forest inventory. The performance, sensitivity and limitations of these procedures were evaluated using a combination of simulated forest models and actual lidar forest data. Automated crown polygons were used as a sampling template to extract dominant tree height values which were converted to DAB estimates via height-to-biomass relationships derived from field survey and on-site destructive sampling. Results were compared with field based tree height and biomass estimates. Compared against a manually derived crown map from a 2ha field plot, canopy segmentation results revealed a producer???s accuracy of 76% and overall accuracy of 67%. Results indicated a trend toward greater crown splitting (fragmentation) as trees increase in age, height, stem diameter and crown size. Extracted dominant tree height values were highly correlated with ground survey height estimates (r2 0.95 for precision survey and r2 0.69 for standard survey). There was also no significant difference between SMIC and manual crown height estimates. SMIC units overestimated ground-based DAB by 5%; this increased to 36% with the inclusion of segmentation errors. However, SMIC estimation of total plot above-ground biomass (AGB) was within 9% of the ground-based estimate. Results are encouraging considering the mixed-species, multi-aged composition of the forest, and the combined effects of SMIC segmentation and lidar height errors.
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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 challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.
Ph. D.
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Books on the topic "Ground segmentation"

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Verheggen, Pieter Paul. Nieuwe Nederlanders: Etnomarketing voor diversiteitsbeleid. Alphen aan den Rijn: 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 groups with the most persuasive approaches. The chapter illustrates methods used for building segmentations and provides case examples.
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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, this chapter discusses the future of segmentation, given the advances in big data analysis and artificial intelligence.
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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 different sources of motion information are assigned to contours and integrated over space and time.
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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 or morphological modifications of paired appendages. There is also considerable variation between groups in the structure and function of the appendages on different body regions.
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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 gap between public and private sectors is mainly attributable to the composition or characteristics of workers, the wage gap in the upper sector of the distribution is due to returns to characteristics effect. The public-sector wage premium explains the strong preference in public positions.
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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 resulted in persistent stratification, with ethnic groups organized as ethnoclasses. Such stratified societies are characterized by an equilibrium of inequality, in which historically marginalized groups lack both the organizational strength and the opportunities for armed rebellion. In contrast, where colonialism and decolonization divided ethnic groups into segmented, unranked subsocieties that feature distinct socioeconomic and cultural institutions, ethnic mobilization is more likely to trigger violent conflict. Second, the theory links this structural explanation to the political actors at the heart of ethnic movements—in particular, ethnic organizations. It elucidates how these organizations fuel the risk of civil conflict in segmented unranked societies, but peacefully promote the empowerment of historically marginalized groups in stratified societies. The book draws on an innovative mixed-methods design that combines large-n statistical analyses—using new data on the linguistic and religious segmentation of ethnic groups, as well as on ethnic organizations—with case studies based on original field research in four different countries in sub-Saharan Africa and Latin America.
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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 approach: segmentation; “the unattainable text” (also referred to as “the undiscoverable text” or “le texte introuvable”); le blocage symbolique (also referred to as “the symbolic blockage”);“the textual volume”; Hitchcock and psychoanalysis; and enunciation.
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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, 31–51. London: 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, 53–70. London: 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, 528–36. Berlin, Heidelberg: 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, 115–34. Berlin, Heidelberg: 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, 320–29. Berlin, Heidelberg: 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, 784–91. Berlin, Heidelberg: 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, 450–64. Berlin, Heidelberg: 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, 338–51. Berlin, Heidelberg: 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, 491–503. Cham: 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, 134–46. Cham: 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 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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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. Fort Belvoir, VA: Defense Technical Information Center, January 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.), April 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 collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
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