MENÜ MENÜ  

cover

Content-based Microscopic Image Analysis

Studien zur Mustererkennung , Bd. 39

Chen Li

ISBN 978-3-8325-4253-5
196 pages, year of publication: 2016
price: 36.50 €
Content-based Microscopic Image Analysis
In this dissertation, novel Content-based Microscopic Image Analysis (CBMIA) methods, including Weakly Supervised Learning (WSL), are proposed to aid biological studies. In a CBMIA task, noisy image, image rotation, and object recognition problems need to be addressed. To this end, the first approach is a general supervised learning method, which consists of image segmentation, shape feature extraction, classification, and feature fusion, leading to a semi-automatic approach.

In contrast, the second approach is a WSL method, which contains Sparse Coding (SC) feature extraction, classification, and feature fusion, leading to a full-automatic approach. In this WSL approach, the problems of noisy image and object recognition are jointly resolved by a region-based classifier, and the image rotation problem is figured out through SC features. To demonstrate the usefulness and potential of the proposed methods, experiments are implemented on di erent practical biological tasks, including environmental microorganism classification, stem cell analysis, and insect tracking.

cover cover cover cover cover cover cover cover cover
Table of contents (PDF)

Keywords:

  • Machine learning
  • Pattern recognition
  • Microscopic image analysis
  • Multimedia retrieval
  • Environmental technology

BUYING OPTIONS

36.50 €
in stock

35.00 €
46.50 €
50.50 €

(D) = Within Germany
(W) = Abroad

*You can purchase the eBook (PDF) alone or combined with the printed book (eBundle). In both cases we use the payment service of PayPal for charging you - nevertheless it is not necessary to have a PayPal-account. With purchasing the eBook or eBundle you accept our licence for eBooks.

For multi-user or campus licences (MyLibrary) please fill in the form or write an email to order@logos-verlag.de