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Author: Mei Chen
Publisher: Academic Press
ISBN: 0128149736
Category : Computers
Languages : en
Pages : 230
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Book Description
Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation
Author: Mei Chen
Publisher: Academic Press
ISBN: 0128149736
Category : Computers
Languages : en
Pages : 230
Get Book Here
Book Description
Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation
Author: Carlos Federico Arteta
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Nazmul Siddique
Publisher: CRC Press
ISBN: 100080478X
Category : Computers
Languages : en
Pages : 248
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Book Description
Computer vision and image analysis are indispensable components of every automated environment. Modern machine vision and image analysis techniques play key roles in automation and quality assurance. Working environments can be improved significantly if we integrate computer vision and image analysis techniques. The more advancement in innovation and research in computer vision and image processing, the greater the efficiency of machines as well as humans. Computer Vision and Image Analysis for Industry 4.0 focuses on the roles of computer vision and image analysis for 4.0 IR-related technologies. The text proposes a variety of techniques for disease detection and prediction, text recognition and signature verification, image captioning, flood level assessment, crops classifications and fabrication of smart eye-controlled wheelchairs.
Author: Scott E Umbaugh
Publisher: CRC Press
ISBN: 1000788512
Category : Technology & Engineering
Languages : en
Pages : 441
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Book Description
1) Learn how to develop computer vision application algorithms 2) Learn to use software tools for analysis and development 3) Learn underlying processes need for image analysis 4) Learn concepts so that the reader can develop their own algorithms 5) Software tools provided
Author: John C. Russ
Publisher: Springer Science & Business Media
ISBN: 1461305632
Category : Science
Languages : en
Pages : 461
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Book Description
The use of computer-based image analysis systems for all kinds of images, but especially for microscope images, has become increasingly widespread in recent years, as computer power has increased and costs have dropped. Software to perform each of the various tasks described in this book exists now, and without doubt additional algorithms to accomplish these same things more efficiently, and to perform new kinds of image processing, feature discrimination and measurement, will continue to be developed. This is likely to be true particularly in the field of three-dimensional imaging, since new microscopy methods are beginning to be used which can produce such data. It is not the intent of this book to train programmers who will assemble their own computer systems and write their own programs. Most users require only the barest of knowledge about how to use the computer, but the greater their understanding of the various image analysis operations which are possible, their advantages and limitations, the greater the likelihood of success in their application. Likewise, the book assumes little in the way of a mathematical background, but the researcher with a secure knowledge of appropriate statistical tests will find it easier to put some of these methods into real use, and have confidence in the results, than one who has less background and experience. Supplementary texts and courses in statistics, microscopy, and specimen preparation are recommended as necessary.
Author: Yuankai Huo
Publisher: Springer Nature
ISBN: 3031169611
Category : Computers
Languages : en
Pages : 200
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Book Description
This book constitutes the refereed proceedings of the 1st International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022. The 18 papers presented at MOVI 2022 were carefully reviewed and selected from 25 submissions. The objective of the MOVI workshop is to promote novel scalable and resource-efficient medical image analysis algorithms for high-dimensional image data analy-sis, from optical imaging to virtual microscopy.
Author: Bart M. Haar Romeny
Publisher: Springer Science & Business Media
ISBN: 1402015038
Category : Computers
Languages : en
Pages : 470
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Book Description
Front-End Vision and Multi-Scale Image Analysis is a tutorial in multi-scale methods for computer vision and image processing. It builds on the cross fertilization between human visual perception and multi-scale computer vision (`scale-space') theory and applications. The multi-scale strategies recognized in the first stages of the human visual system are carefully examined, and taken as inspiration for the many geometric methods discussed. All chapters are written in Mathematica, a spectacular high-level language for symbolic and numerical manipulations. The book presents a new and effective approach to quickly mastering the mathematics of computer vision and image analysis. The typically short code is given for every topic discussed, and invites the reader to spend many fascinating hours `playing' with computer vision. Front-End Vision and Multi-Scale Image Analysis is intended for undergraduate and graduate students, and all with an interest in computer vision, medical imaging, and human visual perception.
Author: Mousumi Gupta
Publisher: Springer Nature
ISBN: 9811387982
Category : Technology & Engineering
Languages : en
Pages : 150
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Book Description
This book includes high-quality papers presented at the Symposium 2019, organised by Sikkim Manipal Institute of Technology (SMIT), in Sikkim from 26–27 February 2019. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.
Author: Scott E Umbaugh
Publisher: CRC Press
ISBN: 143980205X
Category : Technology & Engineering
Languages : en
Pages : 980
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Book Description
Whether for computer evaluation of otherworldly terrain or the latest high definition 3D blockbuster, digital image processing involves the acquisition, analysis, and processing of visual information by computer and requires a unique skill set that has yet to be defined a single text. Until now. Taking an applications-oriented, engineering approach, Digital Image Processing and Analysis provides the tools for developing and advancing computer and human vision applications and brings image processing and analysis together into a unified framework. Providing information and background in a logical, as-needed fashion, the author presents topics as they become necessary for understanding the practical imaging model under study. He offers a conceptual presentation of the material for a solid understanding of complex topics and discusses the theory and foundations of digital image processing and the algorithm development needed to advance the field. With liberal use of color through-out and more materials on the processing of color images than the previous edition, this book provides supplementary exercises, a new chapter on applications, and two major new tools that allow for batch processing, the analysis of imaging algorithms, and the overall research and development of imaging applications. It includes two new software tools, the Computer Vision and Image Processing Algorithm Test and Analysis Tool (CVIP-ATAT) and the CVIP Feature Extraction and Pattern Classification Tool (CVIP-FEPC). Divided into five major sections, this book provides the concepts and models required to analyze digital images and develop computer vision and human consumption applications as well as all the necessary information to use the CVIPtools environment for algorithm development, making it an ideal reference tool for this fast growing field.
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0128104090
Category : Computers
Languages : en
Pages : 460
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Book Description
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
