Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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

Active Publication Date: 2013-01-30
JIANGNAN UNIV
View PDF1 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the difficulty of obtaining relevant prior information, the parameter setting problem closely related to it has always been a difficult problem for researchers.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Small target detection-orientated image threshold segmentation method adopting fast kernel density estimation
  • Small target detection-orientated image threshold segmentation method adopting fast kernel density estimation
  • Small target detection-orientated image threshold segmentation method adopting fast kernel density estimation

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a small target detection-orientated image threshold segmentation method adopting fast kernel density estimation. The method comprises the steps of: reading in an image, obtaining a greyscale matrix of the image in a computer, and setting a parameter gate value; taking pixel points with the same greyscale in the image as a set, and if the number Ni of the pixel points in the image is greater than the gate, then using a FRSDE (Fast Compression Set Density Estimator) for compressing; or else, then using an RSDE (Compression Set Density Estimator) for compressing; and establishing a relation matrix M to represent the interrelations among different greyscales on the image. The problem of extreme value evaluation for a target function is converted to be the problem of minimization sum evaluation for elements based on a matrix region, so that the optimal threshold is obtained. Compared with the prior art, the method disclosed by the invention has the advantages that the process is simple, the realization is easy, the robustness is good, high in the solution efficiency is high, and the like. Therefore, via the method disclosed by the invention, feasible scheme is provided for the problem of small target detection for a high-definition image; and simultaneously, an effective technical basis is provided for detection for a small target image in a complex background.

Description

technical field [0001] The invention relates to a fast kernel density estimation method, in particular to a small target detection-oriented fast kernel density estimation image threshold segmentation method. Background technique [0002] Threshold segmentation is a classic image segmentation method. It refers to determining a gray value as a threshold and dividing the image into two parts, the target and the background. Threshold segmentation is a classic image segmentation technique with low cost and high speed, and it is still widely used. [0003] Small target images widely exist in the fields of national defense, military and industrial detection, and threshold segmentation of small target images is of great significance in practical applications. In a small target image, the background region contains most of the pixels in the image, while the target region only contains a small number of pixels. Threshold segmentation algorithms based on intra-class variance, such a...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 王骏王士同邓赵红钱鹏江应文豪蒋亦樟倪彤光
Owner JIANGNAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products