<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper the image fusion algorithm based on Contourlet transform and Pulse Coupled Neural Network (PCNN) was proposed to improve the performance of the image fusion in the detection accuracy of obstacles in forests. At the same time, the wavelet transform and the Principal Component Analysis (PCA) were simulated for comparison with the proposed algorithm. Then visible and infrared thermal images were collected in a forest. The experimental results have shown that the fused images using the method proposed provided a better understanding of the reality, enhanced images’ clarity and eliminated factors which provided shelters for targets.</p></abstract>

In this paper the image fusion algorithm based on Contourlet transform and Pulse Coupled Neural Network (PCNN) was proposed to improve the performance of the image fusion in the detection accuracy of obstacles in forests. At the same time, the wavelet transform and the Principal Component Analysis (PCA) were simulated for comparison with the proposed algorithm. Then visible and infrared thermal images were collected in a forest. The experimental results have shown that the fused images using the method proposed provided a better understanding of the reality, enhanced images’ clarity and eliminated factors which provided shelters for targets.

Image Fusion Algorithm Based on Contourlet Transform and PCNN for Detecting Obstacles in Forests

School of Technology, Beijing Forestry University, Beijing 100083, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this paper the image fusion algorithm based on Contourlet transform and Pulse Coupled Neural Network (PCNN) was proposed to improve the performance of the...

Image Fusion Algorithm Based on Contourlet Transform and PCNN for Detecting Obstacles in Forests

Online veröffentlicht: 13. März 2015

Seitenbereich: 116 - 125

DOI: https://doi.org/10.1515/cait-2015-0010

© by Zheng Yu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.