Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support
Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect targets. Image
intensity surfaces for the neighborhood of every pixel of the original long-wave infrared image and mid-wave infrared
are well-fitted by mapped LS-SVM respectively. And long-wave and mid-wave infrared image gradient images are
obtained by LS-SVM based on radial basis kernels function. Fusion rule is set up according to the features of gradient
images. At last, segment fused image and targets can be detected with contrast threshold. Compared with wavelet fusion
detection algorithm and morphological fusion detection algorithm, when a target is affected by baits, the experimental
results demonstrate that the proposed approach in the paper based on LS-SVM to fuse and detect weak and small target
is reliable and efficient.
The paper puts forward a fractal dimension algorithm that fuses mid-wave and long-wave infrared images and detects targets. Usually, the targets in infrared images are man-made, and their fractal dimensions are different from that of their natural background. In the fractal dimension algorithm, the source images are first decomposed by wavelet transformation. Then in the wavelet transformation domain, fractal dimensions are calculated and fusion rules that merge corresponding sub images of two matching source images are set up, and new sub images are created. Finally, the image is reconstructed by inverse wavelet transformation and a fused image is obtained. The fusion results have shown that the contrast between the targets and their background has changed significantly, and the targets can be easily detected by applying a contrast threshold. The experimental results have shown that the method using fractal dimension to fuse dualband infrared images and detect targets is superior to the one using mid-wave or longwave infrared images to detect targets alone.
Detection of dim moving small targets at low signal noise ratio is a very important issue and difficult problem in infrared searching and tracking system. Based on analysis of the character of infrared images, a new double energy accumulating method is proposed. Firstly, images are denoised by wavelet transformation with soft threshold. Then, object motion area is detected according to difference images and the target intensity is well enhanced by accumulating energy two times with addition and product operation. Finally, target candidates are separated from background by thresholding process with the selected threshold. Computer experiments are carried out with an infrared image sequence and the experimental results illustrate that the proposed method is effective and efficient.
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