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

Method for extracting local textural roughness of image

A roughness and local technology, applied in the field of image processing, can solve the problems that the surface characteristics of artificial target objects cannot satisfy the fractal model well, and the fractal dimension algorithm is susceptible to noise interference, etc.

Active Publication Date: 2015-02-11
NANJING UNIV OF SCI & TECH
View PDF4 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the fractal dimension algorithm is easily disturbed by noise, and the surface characteristics of the artificial target object often cannot satisfy the fractal model. When calculating the roughness with the fractal dimension, it has certain limitations on the target object.

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
  • Method for extracting local textural roughness of image
  • Method for extracting local textural roughness of image
  • Method for extracting local textural roughness of image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] A method for extracting local texture roughness of an image, characterized in that it comprises the following steps:

[0034] Step 1: Calculate the average gray value of the pixels in the multi-scale active window in the target image, the active window size is not less than 3 × 3,

[0035] The specific method of calculating the average gray value can be shown in formula (1), choose the multi-scale active window size as 4k×4k,

[0036] A k ( x , y ) = Σ i = x - 2 k x + 2 k - 1 Σ j = y - 2 k ...

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 method for extracting local textural roughness of an image. The method comprises the steps of firstly calculating the average gray value of pixels in a multi-scale movable window in the image, respectively calculating average gray difference, between two eccentric overlapping windows, of each pixel in the horizontal direction and the vertical direction, then calculating the optimum size by utilizing the size corresponding to the maximum average gray differential value, and calculating the local roughness of each pixel point according to the optimum size of each pixel point in the image. According to the method disclosed by the invention, the noise robustness is good, and the local textural roughness of the image can be accurately measured.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for extracting local texture roughness of an image. Background technique [0002] Texture is one of the important attributes used to identify objects or regions of interest in an image. It exists on almost all object surfaces and contains important information about the organization and arrangement of the surface structure of objects and their connection with the surrounding environment. It reflects the homogeneity phenomenon in the image. The visual characteristics of , independent of image color or brightness characteristics. Research on visual perception has found that compared with machines, humans have a perfect texture perception feature mechanism and can distinguish small texture differences. Texture feature attributes used by humans to distinguish texture features include: roughness, contrast, complexity, directionality, etc. [0003] Based on the psycho...

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): G06T5/00G06T7/00
Inventor 柏连发张毅金左轮韩静岳江陈钱顾国华
Owner NANJING UNIV OF SCI & TECH
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