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Titlebook: Computer Vision for X-Ray Testing; Imaging, Systems, Im Domingo Mery,Christian Pieringer Textbook 2021Latest edition Springer Nature Switze

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發(fā)表于 2025-3-21 18:04:47 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision for X-Ray Testing
副標(biāo)題Imaging, Systems, Im
編輯Domingo Mery,Christian Pieringer
視頻videohttp://file.papertrans.cn/235/234080/234080.mp4
概述Presents a focus on the most important real-world applications of X-ray testing.Updated edition featuring new material on deep learning, simulation approaches, and dual energy X-ray images.Includes nu
圖書封面Titlebook: Computer Vision for X-Ray Testing; Imaging, Systems, Im Domingo Mery,Christian Pieringer Textbook 2021Latest edition Springer Nature Switze
描述[FIRST EDITION] This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing.?Features: introduces the mathematical background for monocular and multiple view geometry; describes the main techniques for image processing used in X-ray testing; presents a range of different representations for X-ray images, explaining how these enable new features to be extracted from the original image; examines a range of known X-ray image classifiers and classification strategies; discusses some basic concepts for the simulation of X-ray images and presents simple geometric and imaging models that can be used in the simulation; reviews a variety of applications for X-ray testing, from industrial inspection and baggage screening to the quality control of natural products; provides supporting material at an associated website, including a database of X-ray images and a Matlab toolbox for use with the book’s many examples.
出版日期Textbook 2021Latest edition
關(guān)鍵詞Algorithms; Computer Vision; Image Analysis; Image Processing; Non-Destructive Testing; Pattern Recogniti
版次2
doihttps://doi.org/10.1007/978-3-030-56769-9
isbn_softcover978-3-030-56771-2
isbn_ebook978-3-030-56769-9
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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發(fā)表于 2025-3-21 21:39:26 | 只看該作者
Deep Learning in X-ray Testing, review many relevant concepts of deep learning that can be used in computer vision for X-ray testing. We covered the theory and practice of deep learning techniques in real X-ray testing problems. The chapter explained neural networks, Convolutional Neural Network (CNN) that can be used in classifi
板凳
發(fā)表于 2025-3-22 01:10:34 | 只看該作者
Rate of Denervation Muscle Atrophy, review many relevant concepts of deep learning that can be used in computer vision for X-ray testing. We covered the theory and practice of deep learning techniques in real X-ray testing problems. The chapter explained neural networks, Convolutional Neural Network (CNN) that can be used in classifi
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,Graham Swift’s ,: The Polyphonic Novel, isolate the regions of interest, and (v) to remove the blurriness of the X-ray image. The chapter provides an overview and presents several methodologies with examples using real and simulated X-ray images.
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X-Ray Image Processing, isolate the regions of interest, and (v) to remove the blurriness of the X-ray image. The chapter provides an overview and presents several methodologies with examples using real and simulated X-ray images.
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