Paper Abstract and Keywords |
Presentation |
2013-11-07 15:20
Image Segmentation for NBI Endoscopy Image Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Yoko Kominami, Rie Miyaki, Taiji Matsuo, Shigeto Yoshida, Shinji Tanaka (Hiroshima Univ.) MI2013-53 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we propose a texture image segmentation method by using SVM posterior probabilities with a Markov Random Field (MRF) framework for colorectal NBI image. Although several types may exist in a image, current system recognize only a part of it. To recognize the whole of a image, we use an MRF model to segment into each class. The performance of our MRF model is demonstrated on NBI endoscopic image dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
NBI Endoscopy / Support Vector Machine / Bag--of--Visual Words / Markov Random Fields / $alpha$-$beta$ swap Graph Cut / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 281, MI2013-53, pp. 39-43, Nov. 2013. |
Paper # |
MI2013-53 |
Date of Issue |
2013-10-31 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2013-53 |
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