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Title Computer vision for microscopy image analysis / edited by Mei Chen.

Publication Info. Amsterdam : Academic Press, 2021.

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Location Call No. OPAC Message Status
 Axe Elsevier ScienceDirect Ebook  Electronic Book    ---  Available
Description 1 online resource
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Computer vision and pattern recognition
Computer vision and pattern recognition series.
Summary 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
Subject Microscopy -- Data processing.
Computer vision.
Microscopie -- Informatique.
Vision par ordinateur.
Computer vision
Microscopy -- Data processing
Added Author Chen, Mei (Computer scientist), editor.
Other Form: Print version: 9780128149720
ISBN 0128149736
9780128149737 (electronic bk.)
9780128149720 (pbk.)
Standard No. UKMGB 019930294
AU@ 000068418896

 
    
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