An Improved Algorithm of Improved Computation Efficiency on LTS Hausdorff Distance

Hui Wang; Guo Jia Li; Jun Hui Pan; Fu Hua Shang

doi:10.4028/www.scientific.net/AMM.347-350.3217

Paper Titles

Based on the Basis Module Library Reuse of Oil Field Simulation Training System Development Research and Application
p.3199

Research of Battlefield Environment Virtual Simulation
p.3204

Multi-Robot Path Planning Based on Learning Classifier System with Policy Gradient Reinforcement Learning and Support Vector Machine (PGRL-SVM)
p.3208

NSCT-Based Multi-Sensor Image Fusion Algorithm
p.3212

An Improved Algorithm of Improved Computation Efficiency on LTS Hausdorff Distance
p.3217

Research on the Architecture and Strategies of a Novel Software CDN
p.3222

Optimized Algorithm for Mining Maximum Frequent Itemsets on Association Rule
p.3227

Digital Image Zero-Watermarking Algorithm Based on Improved Wavelet Moment
p.3232

Design and Implementation of Digital Image Processing Teaching Demonstration System
p.3237

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350An Improved Algorithm of Improved Computation...

An Improved Algorithm of Improved Computation Efficiency on LTS Hausdorff Distance

Abstract:

The computation efficiency of traditional algorithm is not high, and there is more time consuming. This paper presents an effective method for improved hausdorff distance, depth correction of logging curves is based on improved Hausdorff distance. In this method. On the basis of existing LTS hausdorff distance, the contrast curve segment is divided into neighborhood in an area, the LTS hausdorff distance is calculated by using engineering approximate, and the improving methods of search path is put forward, which ensures that the improved algorithm is better than the original algorithm has high computing efficiency and accuracy in theory.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

3217-3221

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.3217

Citation:

Cite this paper

Online since:

August 2013

Authors:

Hui Wang, Guo Jia Li, Jun Hui Pan, Fu Hua Shang

Keywords:

Hausdorff Distance, Logging Curve, Operation Efficiency

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Tingzhi S. One technique of improved Hausdorff distance and genetic algorithm in image matching recognition, Journal of China Institute of Communications, vol 22, pp.112-116, (2001).

Google Scholar

[2] Wei Q. Improved star map identification algorithm based on Hausdorff distance, Journal of Beijing University of Aeronautics and Astronautics. vol. 32, pp.8-12, (2006).

Google Scholar

[3] Zhimin Qiu. An Automatic Target Recognition Algorithm Based on Hausdorff Distance, Infrared Technology, vol 28(4), pp.199-202, (2006).

Google Scholar

[4] Gao Y. Efficiently comparing face images using a modified Hausdorff distance, Image and Signal Processing, IEEE Procedings, vol. 150, pp.346-350, (2003).

DOI: 10.1049/ip-vis:20030805

Google Scholar

[5] Daniel P. Huttcnlocher, A multi-resolution technique for comparing images using the Hausdorff distance, IEEE Transactions unPattern Analysis and Machine Intelligence[J], vol. 14, p.705~706, (1993).

Google Scholar

[6] G. Borgefors, Hierarchical chamfer matching: A parametric edge matching algorithm, IEEE Trans. Pattern Anal. Machine Intelligence[J], vol. 10, pp.849-865, (1988).

DOI: 10.1109/34.9107

Google Scholar

[7] Dong-Gyu Sim, Oh-Kyu Kwon, and Rae-Hong Park, Object Matching Algorithms Using Robust Hausdorff Distance Measures, IEEE Transactions On Image Processing, vol. 15, pp.425-428, (2004).

DOI: 10.1109/83.748897

Google Scholar