In the infrared scene, locally adaptive regression kernels (LARK) feature has the advantage of sensitive to the change of small structure. The probability distribution of the target in the continuously adaptive mean -shift (CamShift) algorithm of single target tracking is weighted by the similarity of feature global matching . It can weaken the interference of background. In order to robustly track infrared target with shape changes, global matching is turned into local statistical matching according to the invariance of target local structure. The number of similar characteristics in the area around a point is used as the weights.
In order to achieve adaptive unsupervised clustering in the high precision, a method using Gaussian distribution to fit the similarity of the inter-class and the noise distribution is proposed in this paper, and then the automatic segmentation threshold is determined by the fitting result. First, according with the similarity measure of the spectral curve, this method assumes that the target and the background both in Gaussian distribution, the distribution characteristics is obtained through fitting the similarity measure of minimum related windows and center pixels with Gaussian function, and then the adaptive threshold is achieved. Second, make use of the pixel minimum related windows to merge adjacent similar pixels into a picture-block, then the dimensionality reduction is completed and the non-supervised classification is realized. AVIRIS data and a set of hyperspectral data we caught are used to evaluate the performance of the proposed method. Experimental results show that the proposed algorithm not only realizes the adaptive but also outperforms K-MEANS and ISODATA on the classification accuracy, edge recognition and robustness.
Infrared and LLL image are used for night vision target detection. In allusion to the characteristics of night vision
imaging and lack of traditional detection algorithm for segmentation and extraction of targets, we propose a method
of infrared and LLL image fusion for target detection with improved 2D maximum entropy segmentation. Firstly,
two-dimensional histogram was improved by gray level and maximum gray level in weighted area, weights were
selected to calculate the maximum entropy for infrared and LLL image segmentation by using the histogram. Compared
with the traditional maximum entropy segmentation, the algorithm had significant effect in target detection, and the
functions of background suppression and target extraction. And then, the validity of multi-dimensional characteristics
AND operation on the infrared and LLL image feature level fusion for target detection is verified. Experimental results
show that detection algorithm has a relatively good effect and application in target detection and multiple targets
detection in complex background.
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