Artificial Life for Pavement Distress Survey

Qi Wang; Hui Zhang

doi:10.4028/www.scientific.net/KEM.295-296.507

Paper Titles

A Laser Sensor Technique for Measuring 3D Surfaces Based on the Polarized Heterodyne Astigmatic Principle
p.483

Development of a Large Range Nanometer Level Profilometer with Controlled Contact Force
p.489

Instantaneous Measurement of 3D Profiles by Projecting Coded Grating
p.495

A 3D Profile Parallel Detecting System Based on Differential Confocal Microscopy
p.501

Artificial Life for Pavement Distress Survey
p.507

Detection of Vortex Frequency in Gas Flow with Ultrasound
p.515

Design, CFD Investigation and Implementation of a Novel Diverter Mechanism for Water Flow Measurement
p.521

Determination of the Jetting Force of a Jet Actuator
p.527

Performance Characterization of Pressure Sensors Using an Improved Pressure Square Wave Generator
p.533

HomeKey Engineering MaterialsKey Engineering Materials Vols. 295-296Artificial Life for Pavement Distress Survey

Artificial Life for Pavement Distress Survey

Abstract:

Automatic recognition of road distresses is of considerable interest since it facilitates road maintenance. We propose the use of artificial life for pavement distress survey. All artificial organisms in the artificial life algorithm exhibit the principle of cooperation which can be found in computer virtues and the hunting process of predators. Experimental results demonstrate that the proposed approach has a strong effect on noise removal such as elimination of oil stains and removal of dark spots which is one of the most difficult problems in pavement distress survey

You might also be interested in these eBooks

Measurement Technology and Intelligent Instruments VI

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 295-296)

Pages:

507-514

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.295-296.507

Citation:

Cite this paper

Online since:

October 2005

Authors:

Qi Wang, Hui Zhang

Keywords:

Artificial Life, Digital Image Processing, Pavement Distress Survey

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] D.H. Kil and F.B. Shin: Proc Image Processing 1997, United States, Vol. 2 (1997), p.414.

Google Scholar

[2] H.D. Cheng and M. Miyojim: Information Sciences, Vol. 108 (1998), p.219.

Google Scholar

[3] C.G. Langton: Artificial Life (Addison-Wesley Publishing Company, United States 1989).

Google Scholar

[4] J.C. Heudin: Proc CAMP 1995, Computer Architectures for Machine Perception, Vol. 1 (1995), p.418.

Google Scholar

[5] F.R. Miranda, J.E. Kogler, E.D. Moral and M.L. Netto: Computers and Graphics, Vol. 25 (2001), p.955.

Google Scholar

[6] J.M. Liu and H. Jing: Pattern Recognition and Artificial Intelligence, Vol. 15 (2001), p.475.

Google Scholar

[7] B.S. Yang, Y.H. Lee, B.K. Choi and H.J. Kim: Tribology, Vol. 34 (2001), p.427.

Google Scholar

[8] H. He and Y.Q. Chen: Pattern Recognition and Artificial Intelligence, Vol. 15 (2001), p.989.

Google Scholar

[9] H.W. Thimbleby, J.H. Witten and D.J. Pullinger: IEEE Transaction on Systems, Man and Cybernetics, Vol. 25 (1995), p.1166. Fig. 6 Pixel error and noise parameters.

DOI: 10.1109/21.391298

Google Scholar