In traditional IHS transformation, the panchromatic image directly replaces the intensity component, spatial information
of the panchromatic image is reserved. But it may cause severe spectral distortion at the same time. Enlightened by
correlation coefficient of two images and its physical meaning, a novel IHS transformation image fusion algorithm is
proposed. It's called local correlation coefficient weighted IHS transformation image fusion algorithm (LCCW-IHS).
The weighted parameter is determined by the local correlation coefficient between the high-resolution panchromatic
image and multi-spectral image's intensity component. Then the two images are fused and the new intensity component
is generated. Finally the fusion image is obtained by inverse IHS transformation. This method furthest synthesized the
region characteristics in the original images to be fused. Both the spectral characteristics of multi-spectral image and the
high- resolution features of the panchromatic image are maintained. And the texture details are also enhanced. The
experimental results of multi-spectral image fusion, analyzed by both subjective and objective evaluations, show the
proposed algorithm is effective for image fusion.
The lateral inhibition mechanism of biologic vision is introduced and applied on edge extraction of images. With the
method, the image's original characters are unchanged. Furthermore, the edge extraction and image enhancement can be
done effectively when the gray changes caused by varying illumination. For increasing the matching ability of resistance
to geometric distortion, a new matching algorithm is established. The edges of the real-time and reference maps are
extracted with the above-mentioned method based on lateral inhibition. Then the concentric circle characteristic vector of
image is defined and the method of vector extraction is proposed. The concentric circle characteristic vectors of the real-time
and reference maps are extracted. Finally they are matched according to the vectors. The resistance of geometric
distortion is improved. A map of roadway in a city obtained by satellite is simulated. The results show that the influence
of gray and geometric distortion on scene matching is effectively overcome. The algorithm is easily implemented with
hardware. The operation speed of the algorithm is also fast. It's worth for the design of real-time scene matching system.
Based on the classical log-polar transformation (LPT), the variable parameter log-polar transformation (VPLPT) is proposed. In the new algorithm, the size of uniform sampling central area is freely adjustable according to different vision task and its high resolution is reserved. The peripheral area is processed with log-polar transformation, which base can be adjusted. The calculation quantity of the two algorithms is analyzed respectively and compared with each other. The result shows the new algorithm's rapidity.
A fault-tolerant integrated navigation system based on neural network information fusion is designed. The neural network filter is used firstly to take the place of the traditional Kalman filter, then the fuzzy neural network is used to detect fault and the neurons for information fusion are used at last. The simulation results show that this approach is effective.
Support vector machine (SVM) can effectively improve the algorithm's generalization capability. This paper proposes a support vector machine target recognition method based on the target infrared feature and edge invariant moments. The simulation result shows it is faster than others. The discrimination is higher than the K-nearby method's one.
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