Flow feature extraction based on entropy and Clifford algebra

Xiaofan Liu; Wenyao Zhang; Ning Zheng

doi:10.1007/978-3-319-21963-9_27

Flow feature extraction based on entropy and Clifford algebra

Xiaofan Liu, Wenyao Zhang^*, Ning Zheng

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

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

3 Citations (Scopus)

Abstract

Feature extraction is important to the visualization of large scale flow fields. To extract flow field features, we propose a new method that is based on Clifford algebra and information entropy theory. Given an input 3D flow field defined on uniform grids, it is firstly converted to a multi-vector field. We then compute its flow entropy field according to information theory, and choose high entropy regions to do the Clifford convolution with predefined multi-vector filter masks. Features are determined on the convolution results. With this method, we can locate, identify, and visualize a set of flow features. And test results show that our method can reduce computation time and find more features than the topology-based method.

Original language	English
Title of host publication	Image and Graphics - 8th International Conference, ICIG 2015, Proceedings
Editors	Yu-Jin Zhang
Publisher	Springer Verlag
Pages	292-300
Number of pages	9
ISBN (Print)	9783319219622
DOIs	https://doi.org/10.1007/978-3-319-21963-9_27
Publication status	Published - 2015
Event	8th International Conference on Image and Graphics, ICIG 2015 - Tianjin, China Duration: 13 Aug 2015 → 16 Aug 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9218
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Image and Graphics, ICIG 2015
Country/Territory	China
City	Tianjin
Period	13/08/15 → 16/08/15

Keywords

Clifford convolution
Feature extraction
Flow field entropy
Flow visualization

Access to Document

10.1007/978-3-319-21963-9_27

Cite this

Liu, X., Zhang, W., & Zheng, N. (2015). Flow feature extraction based on entropy and Clifford algebra. In Y.-J. Zhang (Ed.), Image and Graphics - 8th International Conference, ICIG 2015, Proceedings (pp. 292-300). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9218). Springer Verlag. https://doi.org/10.1007/978-3-319-21963-9_27

Liu, Xiaofan ; Zhang, Wenyao ; Zheng, Ning. / Flow feature extraction based on entropy and Clifford algebra. Image and Graphics - 8th International Conference, ICIG 2015, Proceedings. editor / Yu-Jin Zhang. Springer Verlag, 2015. pp. 292-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d06ce58e9f2949f7b1dd5c1a4c763f18,

title = "Flow feature extraction based on entropy and Clifford algebra",

abstract = "Feature extraction is important to the visualization of large scale flow fields. To extract flow field features, we propose a new method that is based on Clifford algebra and information entropy theory. Given an input 3D flow field defined on uniform grids, it is firstly converted to a multi-vector field. We then compute its flow entropy field according to information theory, and choose high entropy regions to do the Clifford convolution with predefined multi-vector filter masks. Features are determined on the convolution results. With this method, we can locate, identify, and visualize a set of flow features. And test results show that our method can reduce computation time and find more features than the topology-based method.",

keywords = "Clifford convolution, Feature extraction, Flow field entropy, Flow visualization",

author = "Xiaofan Liu and Wenyao Zhang and Ning Zheng",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Image and Graphics, ICIG 2015 ; Conference date: 13-08-2015 Through 16-08-2015",

year = "2015",

doi = "10.1007/978-3-319-21963-9_27",

language = "English",

isbn = "9783319219622",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "292--300",

editor = "Yu-Jin Zhang",

booktitle = "Image and Graphics - 8th International Conference, ICIG 2015, Proceedings",

address = "Germany",

}

Liu, X, Zhang, W & Zheng, N 2015, Flow feature extraction based on entropy and Clifford algebra. in Y-J Zhang (ed.), Image and Graphics - 8th International Conference, ICIG 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9218, Springer Verlag, pp. 292-300, 8th International Conference on Image and Graphics, ICIG 2015, Tianjin, China, 13/08/15. https://doi.org/10.1007/978-3-319-21963-9_27

Flow feature extraction based on entropy and Clifford algebra. / Liu, Xiaofan; Zhang, Wenyao; Zheng, Ning.
Image and Graphics - 8th International Conference, ICIG 2015, Proceedings. ed. / Yu-Jin Zhang. Springer Verlag, 2015. p. 292-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9218).

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

TY - GEN

T1 - Flow feature extraction based on entropy and Clifford algebra

AU - Liu, Xiaofan

AU - Zhang, Wenyao

AU - Zheng, Ning

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Feature extraction is important to the visualization of large scale flow fields. To extract flow field features, we propose a new method that is based on Clifford algebra and information entropy theory. Given an input 3D flow field defined on uniform grids, it is firstly converted to a multi-vector field. We then compute its flow entropy field according to information theory, and choose high entropy regions to do the Clifford convolution with predefined multi-vector filter masks. Features are determined on the convolution results. With this method, we can locate, identify, and visualize a set of flow features. And test results show that our method can reduce computation time and find more features than the topology-based method.

AB - Feature extraction is important to the visualization of large scale flow fields. To extract flow field features, we propose a new method that is based on Clifford algebra and information entropy theory. Given an input 3D flow field defined on uniform grids, it is firstly converted to a multi-vector field. We then compute its flow entropy field according to information theory, and choose high entropy regions to do the Clifford convolution with predefined multi-vector filter masks. Features are determined on the convolution results. With this method, we can locate, identify, and visualize a set of flow features. And test results show that our method can reduce computation time and find more features than the topology-based method.

KW - Clifford convolution

KW - Feature extraction

KW - Flow field entropy

KW - Flow visualization

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U2 - 10.1007/978-3-319-21963-9_27

DO - 10.1007/978-3-319-21963-9_27

M3 - Conference contribution

AN - SCOPUS:84943621758

SN - 9783319219622

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 292

EP - 300

BT - Image and Graphics - 8th International Conference, ICIG 2015, Proceedings

A2 - Zhang, Yu-Jin

PB - Springer Verlag

T2 - 8th International Conference on Image and Graphics, ICIG 2015

Y2 - 13 August 2015 through 16 August 2015

ER -

Liu X, Zhang W, Zheng N. Flow feature extraction based on entropy and Clifford algebra. In Zhang YJ, editor, Image and Graphics - 8th International Conference, ICIG 2015, Proceedings. Springer Verlag. 2015. p. 292-300. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-21963-9_27

Flow feature extraction based on entropy and Clifford algebra

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