Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images

Paul Hing Kwan; K Toraichi

Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images

Paul Hing Kwan
, K Toraichi

Computer Science

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

Abstract

In this paper, an encoding framework of road map information based on automatic Fluency function approximation of thin line images developed in a recent research is introduced. Road map images having multiple structural elements are stratified into layers of 1bit/pixel images based on color. Each of these layers is processed in stages of adaptive pixel sequence tracing, connectivity restoration, and eventually function approximation. In our experiments, numerical maps of 1/200000 scale obtained from the Geographical Survey Institute of Japan are used. The experimental results confirmed the proposed framework is able to encode the numerical map images in which the visual quality is maintained upon decoding. This framework can be a useful preprocessing step in R&D of high quality GIS applications.

Original language	English
Title of host publication	Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004
Editors	Hsing-Wei Chu, Michael Savoie, Kazuo Toraichi, Paul Kwan
Place of Publication	Austin, United States of America
Publisher	International Institute of Informatics and Systemics (IIIS)
Pages	7-12
Volume	III
ISBN (Print)	9789806560178, 9806560175
Publication status	Published - 2004
Event	CCCT 2004: 2nd International Conference on Computing, Communications and Control Technologies - Austin, United States of America Duration: 14 Aug 2004 → 17 Aug 2004

Conference

Conference	CCCT 2004: 2nd International Conference on Computing, Communications and Control Technologies
City	Austin, United States of America
Period	14/08/04 → 17/08/04

Keywords

Image Processing

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https://hdl.handle.net/1959.11/4775

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Kwan, P. H., & Toraichi, K. (2004). Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images. In H.-W. Chu, M. Savoie, K. Toraichi, & P. Kwan (Eds.), Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004 (Vol. III, pp. 7-12). International Institute of Informatics and Systemics (IIIS). https://hdl.handle.net/1959.11/4775

Kwan, Paul Hing ; Toraichi, K. / Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images. Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004. editor / Hsing-Wei Chu ; Michael Savoie ; Kazuo Toraichi ; Paul Kwan. Vol. III Austin, United States of America : International Institute of Informatics and Systemics (IIIS), 2004. pp. 7-12

@inproceedings{e8f2a850d21a4f3b92afe32db0b07b49,

title = "Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images",

abstract = "In this paper, an encoding framework of road map information based on automatic Fluency function approximation of thin line images developed in a recent research is introduced. Road map images having multiple structural elements are stratified into layers of 1bit/pixel images based on color. Each of these layers is processed in stages of adaptive pixel sequence tracing, connectivity restoration, and eventually function approximation. In our experiments, numerical maps of 1/200000 scale obtained from the Geographical Survey Institute of Japan are used. The experimental results confirmed the proposed framework is able to encode the numerical map images in which the visual quality is maintained upon decoding. This framework can be a useful preprocessing step in R\&D of high quality GIS applications.",

keywords = "Image Processing",

author = "Kwan, \{Paul Hing\} and K Toraichi",

year = "2004",

language = "English",

isbn = "9789806560178",

volume = "III",

pages = "7--12",

editor = "Hsing-Wei Chu and Michael Savoie and Kazuo Toraichi and Paul Kwan",

booktitle = "Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004",

publisher = "International Institute of Informatics and Systemics (IIIS)",

note = "CCCT 2004: 2nd International Conference on Computing, Communications and Control Technologies ; Conference date: 14-08-2004 Through 17-08-2004",

}

Kwan, PH & Toraichi, K 2004, Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images. in H-W Chu, M Savoie, K Toraichi & P Kwan (eds), Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004. vol. III, International Institute of Informatics and Systemics (IIIS), Austin, United States of America, pp. 7-12, CCCT 2004: 2nd International Conference on Computing, Communications and Control Technologies, Austin, United States of America, 14/08/04. <https://hdl.handle.net/1959.11/4775>

Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images. / Kwan, Paul Hing; Toraichi, K.
Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004. ed. / Hsing-Wei Chu; Michael Savoie; Kazuo Toraichi; Paul Kwan. Vol. III Austin, United States of America: International Institute of Informatics and Systemics (IIIS), 2004. p. 7-12.

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

TY - GEN

T1 - Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images

AU - Kwan, Paul Hing

AU - Toraichi, K

PY - 2004

Y1 - 2004

N2 - In this paper, an encoding framework of road map information based on automatic Fluency function approximation of thin line images developed in a recent research is introduced. Road map images having multiple structural elements are stratified into layers of 1bit/pixel images based on color. Each of these layers is processed in stages of adaptive pixel sequence tracing, connectivity restoration, and eventually function approximation. In our experiments, numerical maps of 1/200000 scale obtained from the Geographical Survey Institute of Japan are used. The experimental results confirmed the proposed framework is able to encode the numerical map images in which the visual quality is maintained upon decoding. This framework can be a useful preprocessing step in R&D of high quality GIS applications.

AB - In this paper, an encoding framework of road map information based on automatic Fluency function approximation of thin line images developed in a recent research is introduced. Road map images having multiple structural elements are stratified into layers of 1bit/pixel images based on color. Each of these layers is processed in stages of adaptive pixel sequence tracing, connectivity restoration, and eventually function approximation. In our experiments, numerical maps of 1/200000 scale obtained from the Geographical Survey Institute of Japan are used. The experimental results confirmed the proposed framework is able to encode the numerical map images in which the visual quality is maintained upon decoding. This framework can be a useful preprocessing step in R&D of high quality GIS applications.

KW - Image Processing

UR - http://turing.une.edu.au/~kwan/publications/pdf/map-encoding-CCCT04.pdf

M3 - Conference contribution

SN - 9789806560178

SN - 9806560175

VL - III

SP - 7

EP - 12

BT - Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004

A2 - Chu, Hsing-Wei

A2 - Savoie, Michael

A2 - Toraichi, Kazuo

A2 - Kwan, Paul

PB - International Institute of Informatics and Systemics (IIIS)

CY - Austin, United States of America

T2 - CCCT 2004: 2nd International Conference on Computing, Communications and Control Technologies

Y2 - 14 August 2004 through 17 August 2004

ER -

Kwan PH, Toraichi K. Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images. In Chu HW, Savoie M, Toraichi K, Kwan P, editors, Proceedings of the International Conference on Computing, Communications and Control Technologies: CCCT 2004. Vol. III. Austin, United States of America: International Institute of Informatics and Systemics (IIIS). 2004. p. 7-12

Automatic Road Map Encoding by Fluency Function Approximation of Thin Line Images

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