Template Match Object Detection for Inertial Navigation Systems
Wiedo Hu, Ahmed Mohamed Gharuib, Alaa El-Din Sayed Hafez
DOI: 10.4236/pos.2011.22008   PDF    HTML   XML   6,263 Downloads   12,701 Views   Citations


This paper devoted to propose template match object detection for inertial navigation systems (INS). The proposed method is an image processing technique to improve the precision of the INS for detecting and tracking the ground objects from flying vehicles. Template matching is one of the methods used for ground object detection and tracking. Robust and reliable object detection is a critical step of object recognition. This paper presents a proposed mathematical morphological template matching method for detection and tracking of ground objects. Our focus is on flying systems equipped with camera to capture photos for the ground and recognize it. The proposed method is independent on the altitude or the orientation of the object. The algorithm is simulated using Matlab program and the numerical experiments are shown which verify the object detection for a wide range altitude and orientation. The results show superiority of this method for identifying and recognizing the ground objects.

Share and Cite:

W. Hu, A. Gharuib and A. Hafez, "Template Match Object Detection for Inertial Navigation Systems," Positioning, Vol. 2 No. 2, 2011, pp. 78-83. doi: 10.4236/pos.2011.22008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Roger M. Dufour, Eric L. Miller and Nikolas P. Galatsanos, “Template Matching Based Object Recognition With Unknown Geometric Parameters,” IEEE Transaction on Image Processing, Vol. 11, No. 12, Dec. 2002. doi:10.1109/TIP.2002.806245
[2] Gupta, Rahul Gupta, Amardeep Singh and Matt Wytock, “Object Recognition using Template Matching,” http://www.stanford .edu/class/cs229 /proj2008.
[3] R. Brunelli, “Template Matching Techniques in Computer Vision: Theory and Practice,” Wiley, 2009. doi:10.1002/9780470744055
[4] Kyriacou, Theocharis, Guido Bugmann, and Stanislao Lauria, “Vision-based urban navigation procedures for verbally instructed robots,” Robotics and Autonomous Systems, Expanded Academic ASAP, No. 30, 2005.
[5] Zhao Yu-qian, Gui Wei-hua, Chen Zhen-cheng, Tang Jing-tian and Li Ling-yun, “ Medical Images Edge Detection Based on Mathematical Morphology,” Proceedings of IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
[6] Li, Y., Tsin, Y., Genc, Y. and Kanade, T., “Object detection using 2D spatial ordering constraints,” Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society Conference on, 20-25 June, 2005.
[7] Heisele B., Rocha C., “Local shape features for object recognition,” Pattern Recognition, 2008. ICPR 2008 19th International Conference on, Tampa, FL, 8-11 Dec. 2008
[8] Neal R. Harvey, Reid Porter, and James Theiler, “Ship detection in satellite imagery using rank-order grayscale hit-or-miss transforms,” Proc. SPIE, Vol. 7701, 2010.
[9] E. G. M. Petrakis, A. Diplaros, and E. Milios, “Matching and retrieval of distorted and occluded shapes using dynamic programming,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 11, 2002. doi:10.1109/TPAMI.2002.1046166
[10] Feng Ge, Tiecheng Liu, Song Wang and Joachim Stahl, “Template-Based Object Detection through Partial Shape Matching and Boundary Verification,” International Journal of Information and Communication Engineering, Vol. 4, No. 2, 2008.
[11] Rjiv Kumar Nathi, “On Road Vehicle/Object Detection Template,” Indian Computer and Science and Engineering, Vol. 1, No. 2, 2010.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.