Linear Target Feature Extraction Applied in Target Identification

Author:

Zhao Man1,Cui Jin Jiang2,Wu He Nan1,Yang Guang1,Jiang Da Yong3

Affiliation:

1. Aviation University of Air Force

2. Chinese Academy of Sciences

3. Changchun University of Science and Technology

Abstract

Linear target is the most widely used in remote sensing image. Effective extraction of the linear target can make us reduce a lot of practical work, thus greatly improve the target extraction and identification of timeliness. According to this situation, in the process of building a recognition system, the recognition efficiency can be realized by joining human recognize and identify, combining with the intelligence of computer processing and powerful place. So in this paper, the method based on edge detection and Hough transform algorithm is exploded. Line Extraction and Target Recognition System is developed. The system is realized under Windows operating system. The tool is Visual C++ 6.0 software. The platform is MFC functions. The system is written in C++ language. The characteristics of the system are the strong pertinence and the simple operation. When the system is applied safely, the results are definite and clear.

Publisher

Trans Tech Publications, Ltd.

Reference5 articles.

1. Roushdy, M., Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter, the International Journal on Graphics, Vision, and Image Processing, 6(4), 17-23 (2006).

2. Z. Rahman and D.J. Jobson, Noise, edge extraction, and visibility of features, in Proceedings of SPIE, 5817, p.200, (2005).

3. S. Lefvre, C. Jeusse, N. Vincent, A Comparison of Line Detectors for Image Background Modeling, volume IAPR International Workshop on Pattern Recognition in Information Systems, pages 185–190, (2003).

4. C. Teutsch, D. Berndt, E. Trostmann, M. Weber, Real-time detection of elliptic shapes for automated object recognition and object tracking, in: Proceedings of Machine Vision Applications in Industrial Inspection XIV, San Jose, CA, USA, p.171–179 (2006).

5. Koschan A, Abidi M. Detection and classification of edges in color images, IEEE Signal Processing Magazine, 2005, 22(1): 12- 29.

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