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

Improved infrared image segmentation algorithm based on Otsu method

A technique of maximizing inter-class variance and infrared images, applied in the field of infrared imaging, can solve problems such as not being able to obtain better segmentation results

Active Publication Date: 2018-01-19
HARBIN ENG UNIV
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Aiming at the disadvantage that the infrared image segmentation algorithm of the maximum inter-class variance method in the prior art cannot obtain a better segmentation effect when the sky is exposed and the contrast between the sky and the target is low, the present invention aims to provide a An Improved Infrared Image Segmentation Algorithm Based on the Maximum Between-Class Variance Method for Segmenting Areas with High Contrast Between Target and Sky Background

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
  • Improved infrared image segmentation algorithm based on Otsu method
  • Improved infrared image segmentation algorithm based on Otsu method
  • Improved infrared image segmentation algorithm based on Otsu method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The present invention is described in more detail below in conjunction with accompanying drawing:

[0034] Such as figure 1 As shown, an improved infrared image segmentation algorithm based on the maximum inter-class variance method includes the following steps:

[0035] 1. Acquire an infrared image.

[0036] 2. Calculate the average gray value u of the entire image:

[0037] 2.1 Use f(x,y) to represent the infrared image I M×N The gray value at the (x, y) position, the image in this paper is a gray image, and its gray level L=256, then f(x, y) ∈ [0, L-1]. If the number of pixels at the same gray level i is counted as f i , then the occurrence probability of a pixel with gray level i is: where i=0,1,...,255, and

[0038] 2.2 The average gray value u of the entire image is:

[0039] 3. Set the segmentation threshold t to an initial value of 1.

[0040] 4. The pixels whose f(x, y) is less than the threshold t are classified as the background part C0, otherwis...

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 provides an improved infrared image segmentation algorithm based on the Otsu method, and the algorithm can solve a problem of poor segmentation effect caused by sky exposure to the greatest extent, and can enable the segmented target to maintain a more complete shape. Because the contrast of an infrared image is low relatively and the gray scale range is relatively small, the Otsu method cannot achieve the good segmentation of a target. Through giving consideration to the impact on the segmentation from the image gray scale, the number of background pixels of an image and the number of target pixels, the algorithm achieves the improvement of a formula for solving a variance in a conventional Otsu method, irons out the defects that an infrared image segmentation algorithm based on the Otsu method cannot obtain a better segmentation result under the condition of sky exposure and smaller contrast between the sky and the target, and enables the segmentation effect to be goodin a region where the contrast between the target and the sky background is larger.

Description

technical field [0001] The invention belongs to the technical field of infrared imaging, and in particular relates to an improved infrared image segmentation algorithm based on the maximum inter-class variance method. Background technique [0002] In the imaging process of the infrared imaging system, due to its own imaging reasons, the infrared image has the characteristics of low signal-to-noise ratio, blurred image, and low contrast. When UUV shoots infrared reconnaissance images near the sea, due to the influence of UUV movement, the difference in temperature of the shooting scene, and the interference of waves, the quality of the infrared images taken by it is worse than that of ordinary infrared images. Because the contrast of infrared images is relatively low and the range of gray levels is relatively narrow, the target cannot be well segmented by using the method of maximum variance between classes. In order to better segment the target, considering the influence of...

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
IPC IPC(8): G06T7/136G06T7/11
Inventor 张勋时延利张宏瀚严浙平徐健陈涛周佳加
Owner HARBIN ENG 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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com