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Books on the topic 'Segmentation des images échographiques'

Author: Grafiati

Published: 25 May 2024

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1

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

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2

Banik, Shantanu, Rangaraj M. Rangayyan, and Graham S. Boag. Landmarking and Segmentation of 3D CT Images. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-01635-6.

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3

Zuluaga, Maria A., Kanwal Bhatia, Bernhard Kainz, Mehdi H. Moghari, and Danielle F. Pace, eds. Reconstruction, Segmentation, and Analysis of Medical Images. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52280-7.

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4

Tuckett, Nicholas M. Maximum gradient profile segmentation of gesture images. Manchester: UMIST, 1995.

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5

Griffiths, Jane Edisbury. Market segmentation and the corporate images of grocery retailers. Manchester: University of Manchester, 1996.

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6

Zhuang, Xiahai, and Lei Li, eds. Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65651-5.

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7

Anil, Phatak, Chatterji Gano, and Ames Research Center, eds. Scene segmentation of natural images using texture measures and back-propagation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1993.

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8

Noltimier, Katy Farness. Geological analysis of ERS-1 SAR mosaic: Implications for the tectonic segmentation of the Antarctic Peninsula. Columbus, Ohio: Byrd Polar Research Center, Ohio State University, 1998.

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9

Abkar, Ali Akbar. Likelihood-based segmentation and classification of remotely sensed images: A Bayesian optimization approach for combining RS and GIS. Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1999.

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10

Bloomquist, James D. Hierarchical image segmentation to infrared images. 1985.

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11

Boag, Graham, Rangaraj Rangayyan, and Shantanu Banik. Landmarking and Segmentation of 3D CT Images. Springer International Publishing AG, 2009.

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12

Bhatia, Kanwal, Maria A. Zuluaga, Bernhard Kainz, Mehdi H. Moghari, and Danielle F. Pace. Reconstruction, Segmentation, and Analysis of Medical Images. Springer, 2017.

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13

Boag, Graham, Rangaraj Rangayyan, and Shantanu Banik. Landmarking and Segmentation of 3D CT Images. Morgan & Claypool Publishers, 2009.

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14

Hacipasaoglu, Kani. Segmentation of noisy images using nonstationary Markov fields. 1987.

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15

Kupeli, Timur. Comparison of model-based segmentation algorithms for color images. 1987.

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16

Shafarenko, Leila. Perception-driven automatic segmentation of colour images using mathematical morphology. 1996.

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17

wasson, vikas, and gurinder kaur. Novel Approach for Thyroid Segmentation of Ultrasound Images Based on Neural Networks. Independently Published, 2018.

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18

Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. AK Peters, 2004.

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19

Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images. Storming Media, 1996.

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20

Evaluation of Segmentation for Bone Structures in 3D Rendering of Ultrasound Residual Limb Images. Storming Media, 1996.

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21

Behrooz, Ali. Systems and Methods for Automated Segmentation of Individual Skeletal Bones in 3D Anatomical Images: United States Patent 9999400. Independently Published, 2020.

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22

Li, Lei, and Xiahai Zhuang. Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Springer International Publishing AG, 2020.

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23

Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the intricacies of digital imaging systems, pixel connectivity, color models, and file formats are dissected. Following this, image enhancement techniques, including spatial and frequency domain methods and histogram processing, are elaborated upon. The book then addresses image restoration, discussing degradation models, noise modeling, and blur, and offers insights into the compelling world of multi-resolution analysis with in-depth discussions on wavelets and image pyramids. Segmentation processes, especially edge operators, boundary detections, and thresholding techniques, are detailed in subsequent chapters. The text culminates by diving deep into the applications of image processing, exploring supervised and unsupervised learning, clustering algorithms, and various classifiers. Throughout the discourse, practical examples, real-world applications, and intuitive diagrams are integrated to facilitate an enriched learning experience. This book stands as an essential guide for both novices aiming to grasp the basics and experts looking to hone their knowledge in image processing. Keywords: Digital Imaging Systems, Image Enhancement, Image Restoration, Multi-resolution Analysis, Wavelets, Image Segmentation, Feature Extraction, SIFT, SURF, Image Classifiers, Supervised Learning, Clustering Algorithms.

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24

Schmitt-Beck, Rüdiger, Sigrid Roßteutscher, Harald Schoen, Bernhard Weßels, and Christof Wolf, eds. The Changing German Voter. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198847519.001.0001.

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Over the past half century, the behavior of German voters has changed profoundly—at first rather gradually but during the last decade at accelerated speed. Electoral decision-making has become much more volatile, rendering election outcomes less predictable. Party system fragmentation intensified sharply. The success of the Alternative for Germany (AfD) put an end to Germany’s exceptionality as one of the few European countries without a strong right-wing populist party. Utilizing a wide range of data compiled by the German Longitudinal Election Study, the book examines changing voters’ behavior in the context of changing parties, campaigns, and media during the period of its hitherto most dramatically increased fluidity at the 2009, 2013, and 2017 federal elections. Guided by the notions of realignment and dealignment, the study addresses three questions: How did the turbulences that increasingly characterize German electoral politics come about? How did they in turn condition voters’ decision-making? How were voters’ attitudes and choices affected by situational factors that pertained to the specifics of particular elections? The book demonstrates how traditional cleavages lost their grip on voters and a new socio-cultural line of conflict became the dominant axis of party competition. A series of major crises, but also programmatic shifts of the established parties promoted this development. It led to a segmentation of the party system that pits the right-wing populist AfD against the traditional parties. The book also demonstrates the relevance of coalition preferences, candidate images as well as media and campaign effects for voters’ attitudes, beliefs, and preferences.

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