A new colour image fusion scheme is applied to visible and thermal images of military relevant scenarios. An observer experiment is performed to test if the increased amount of detail in the fused images can improve the accuracy of observers performing a detection and localisation task. The results show that observers can localise a target in a scene (1) with a significantly higher accuracy, and (2) with a greater amount of confidence when they perform with fused images (either gray or colour fused), compared to the individual image modalities (visible and thermal).

Fusion of visible and thermal imagery improves situational awareness. Discussion. Author's reply

This paper analyses the characteristics of infrared detection imaging, with the specific application of multi-scale analysis theory for SAR and infrared image fusion. After learning and discussing the research results in image fusion area at home and abroad, it proposes an adaptive weighted image fusion method which combines the idea of fuzzy theory based on Curvelet transform, i.e., defines the membership function with fuzzy logic variables, makes different weights to transform coefficients of different levels, and designs a kind of adaptive weighted image fusion strategy. Experimental results validate the reliability and credibility of this method in term of visual quality and objective evaluation, and it can effectively improve the fusion quality.

Image fusion method of SAR and infrared image based on Curvelet transform with adaptive weighting

In this paper, a new algorithm to improve the accuracy of estimating diameter at breast height (DBH) for tree trunks in forest areas is proposed. First, the information is collected by a two-dimensional terrestrial laser scanner (2DTLS), which emits laser pulses to generate a point cloud. After extraction and filtration, the laser point clusters of the trunks are obtained, which are optimized by an arithmetic means method. Then, an algebraic circle fitting algorithm in polar form is non-linearly optimized by the Levenberg-Marquardt method to form a new hybrid algorithm, which is used to acquire the diameters and positions of the trees. Compared with previous works, this proposed method improves the accuracy of diameter estimation of trees significantly and effectively reduces the calculation time. Moreover, the experimental results indicate that this method is stable and suitable for the most challenging conditions, which has practical significance in improving the operating efficiency of forest harvester and reducing the risk of causing accidents.

New Hybrid Algorithms for Estimating Tree Stem Diameters at Breast Height Using a Two Dimensional Terrestrial Laser Scanner.

Catenary image segmentation using the simplified PCNN with adaptive parameters

http://academic.oup.com/nq/article-pdf/s1-III/70/173/21061190/173-c.pdf

D. o. m.

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.

/pdf/image-fusion-algorithm-based-on-contourlet-transform-and-2vyb3died3.pdf

Lei Yan

Papers

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