Sparsity analysis of endoscopy images

Tzu Hao Su; Si Ching Pan; Xutao Wei; Yu Liang Chiang; Ting Lan Lin; Yangming Wen; Zhaoyi Liu; Shih Lun Chen; Ho Yin Lee

doi:10.1109/GCCE.2017.8229209

Sparsity analysis of endoscopy images

Tzu Hao Su
, Si Ching Pan
, Xutao Wei
, Yu Liang Chiang
, Ting Lan Lin
, Yangming Wen
, Zhaoyi Liu
, Shih Lun Chen
, Ho Yin Lee

Department of Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sparsity analysis of images is important to understand the image characteristic and its possible potential in applications. In this paper, normal images and endoscopy image are investigated for their sparsity using K-SVD algorithm that finds a dictionary basis with minimal number of non-zero coefficients in the transformed domain to have minimal prediction error. The results show that the endoscopy image has lower prediction error and lower number of non-zero coefficients in the transformed domain. This indicates the fact that one can develop a better endoscopy image encoder with better prediction mechanism, and a better endoscopy image decoder with better error concealment method to recover data contaminated by the noise, both based on the idea of sparsity.

Original language	English
Title of host publication	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-2
Number of pages	2
ISBN (Electronic)	9781509040452
DOIs	https://doi.org/10.1109/GCCE.2017.8229209
State	Published - 19 Dec 2017
Event	6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan Duration: 24 Oct 2017 → 27 Oct 2017

Publication series

Name	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
Volume	2017-January

Conference

Conference	6th IEEE Global Conference on Consumer Electronics, GCCE 2017
Country/Territory	Japan
City	Nagoya
Period	24/10/17 → 27/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

K-SVD
OMP
endoscopy image
sparsity representation

Access to Document

10.1109/GCCE.2017.8229209

Cite this

Su, T. H., Pan, S. C., Wei, X., Chiang, Y. L., Lin, T. L., Wen, Y., Liu, Z., Chen, S. L., & Lee, H. Y. (2017). Sparsity analysis of endoscopy images. In 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 (pp. 1-2). (2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2017.8229209

@inproceedings{bedd41c2c7a141ada9b83b3ee8915c1c,

title = "Sparsity analysis of endoscopy images",

abstract = "Sparsity analysis of images is important to understand the image characteristic and its possible potential in applications. In this paper, normal images and endoscopy image are investigated for their sparsity using K-SVD algorithm that finds a dictionary basis with minimal number of non-zero coefficients in the transformed domain to have minimal prediction error. The results show that the endoscopy image has lower prediction error and lower number of non-zero coefficients in the transformed domain. This indicates the fact that one can develop a better endoscopy image encoder with better prediction mechanism, and a better endoscopy image decoder with better error concealment method to recover data contaminated by the noise, both based on the idea of sparsity.",

keywords = "K-SVD, OMP, endoscopy image, sparsity representation",

author = "Su, \{Tzu Hao\} and Pan, \{Si Ching\} and Xutao Wei and Chiang, \{Yu Liang\} and Lin, \{Ting Lan\} and Yangming Wen and Zhaoyi Liu and Chen, \{Shih Lun\} and Lee, \{Ho Yin\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th IEEE Global Conference on Consumer Electronics, GCCE 2017 ; Conference date: 24-10-2017 Through 27-10-2017",

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Su, TH, Pan, SC, Wei, X, Chiang, YL, Lin, TL, Wen, Y, Liu, Z, Chen, SL & Lee, HY 2017, Sparsity analysis of endoscopy images. in 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-2, 6th IEEE Global Conference on Consumer Electronics, GCCE 2017, Nagoya, Japan, 24/10/17. https://doi.org/10.1109/GCCE.2017.8229209

Sparsity analysis of endoscopy images. / Su, Tzu Hao; Pan, Si Ching; Wei, Xutao et al.
2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-2 (2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Sparsity analysis of endoscopy images

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Keywords

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