Small target detection-orientated image threshold segmentation method adopting fast kernel density estimation
A technology of kernel density estimation and small target detection, which is applied in image analysis, image data processing, calculation, etc., can solve the problem of parameter setting that troubles researchers and other problems, and achieves the effect of easy implementation, good robustness, and simple process
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Embodiment 1
[0046] Read image 3 shown in the image, image 3 The size of the image shown is 256×256, and the parameter gate value is set to 500.
[0047] The segmentation result image obtained by the method of the present invention is Figure 4 , Figure 4 The size of the image shown is 256×256.
Embodiment 2
[0049] Read Figure 5 shown in the image, Figure 5 The size of the image shown is 256×256, and the parameter gate value is set to 600.
[0050] The segmentation result image obtained by the method of the present invention is Figure 6 , Figure 6 The size of the image shown is 256×256.
Embodiment 3
[0052] Read Figure 7 shown in the image, Figure 7 The size of the image shown is 256×256, and the parameter gate value is set to 700.
[0053] The segmentation result image obtained by the method of the present invention is Figure 8 , Figure 8 The size of the image shown is 256×256.
[0054] The simulation effect of grayscale image threshold segmentation of the present invention is as follows Figure 4 , Figure 6 , Figure 8 As shown, it can be seen that the method of the present invention can obtain ideal segmentation results. On the premise that the parameter ε is fixed, the time spent by the algorithm fastKDET on each image is shown in Table 1.
[0055] Table 1
[0056]
[0057] As can be seen from Table 1, the time spent by the fast kernel density estimation image threshold segmentation method for small target detection of the present invention can be controlled within an acceptable range, which provides a solid guarantee for its efficient application to high...
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