Computer Vision for Microscopy Image Analysis Paperback / softback
Edited by Mei (Principal Research Manager, Microsoft, Redmond, Washington, USA) Chen
Part of the Computer Vision and Pattern Recognition series
Paperback / softback
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.
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Paperback / softback
- Pages:228 pages
- Publisher:Elsevier Science Publishing Co Inc
- Publication Date:04/12/2020
- Category:
- ISBN:9780128149720
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Paperback / softback
- Pages:228 pages
- Publisher:Elsevier Science Publishing Co Inc
- Publication Date:04/12/2020
- Category:
- ISBN:9780128149720
