Unlock instant, AI-driven research and patent intelligence for your innovation.
Image level set segmentation method based on local gray clustering characteristics
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A level set segmentation and local grayscale technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problem of inconsistency of image grayscale
Active Publication Date: 2016-12-07
NORTHEASTERN UNIV
View PDF2 Cites 14 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
However, the traditional level set method does not have the ability to deal with the inconsistency of image gray levels
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
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0063] An image level set segmentation method based on local gray-level clustering features, such as figure 1 shown, including the following steps:
[0064] Step 1: Read the image I to be segmented.
[0065] Step 2: Use the linear weighted sum of M orthogonal basis functions to fit the offset field b, and initialize the weight values of each basis function.
[0066] In this embodiment, M is 15, and the formula of the offset field b(x) is shown in formula (1):
[0067] b=w T G (1)
[0068] Among them, w=(w 1 ,w 2 ,...,w M ) T is the basis function weight column vector, T is the transpose operation symbol, G=(g 1 , g 2 ,..., g M ) T is a column vector composed of M basis functions, g 1 , g 2 ,..., g M is a pairwise orthogonal 4th-order Legendre polynomial function, w 1 =w 2 =...=w M =1 is the weight value of each basis function initialized.
[0069] Step 3: Initialize the level set function set of the image: according to the number N of regions to be divided ...
Embodiment 2
[0096] In the simplest case of the present invention, the image is divided into two regions N=2, and k=1 is calculated according to formula (2).
[0097] Such as Figure 8 In the situation shown, (a) is the four images to be segmented and the initial zero level set segmentation curve, (b) is the segmentation result of the four images to be segmented, where the level set segmentation curve smoothing coefficient v of the four images to be segmented takes values in sequence 0.1×255×255, 0.5×255×255, 0.3×255×255, 0.02×255×255.
Embodiment 3
[0099] The method of the present invention divides the real image with noise, offset field and weak boundary information, divides the image into two regions N=2, and calculates k=1 according to the formula (2). Such as Figure 9 As shown, (a) is the four images to be segmented and the initial zero level set segmentation curve, (b) is the offset field estimation of the four images to be segmented obtained by the method of the present invention, (c) is the image after four offset field corrections , (d) is the segmentation result of the four images to be segmented, where the coefficient v of the level set segmentation curve smoothing term of the four images to be segmented takes values 0.0045×255×255, 0.003×255×255, 0.005×255×255, 0.01 ×255×255.
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
Login to View More
Abstract
The invention provides an image level set segmentation method based on local gray clustering characteristics. The method comprises the steps that images to be segmented are read; linear weighting and fitting bias fields of orthogonal basis functions are used, and the weight value of each basis function is initialized; the level set function set of the images is initialized; the energy functional of image level set segmentation is established, and level set segmentationcontrol parameters are set according to the images to be segmented; a clustering center set, the image level set function set and basis function weight column vectors are respectively updated until meeting the stop criterion for iteration so that the energy functional of iteration is obtained; the subordinating degree function of the images, i.e. the segmentation result of the images to be segmented, is constructed according to the currently updated image level set function set, and bias fieldestimation of the images to be segmented is obtained according to the updated basis function weight column vectors and basis function column vectors. According to the method, the adverse impacts of weak boundary, image noise and gray inconsistency on the accuracy of image segmentation can be overcome by the method so that the method has the effect of image gray correction.
Description
technical field [0001] The invention belongs to the technical field of computer vision, pattern recognition, image processing and analysis, and in particular relates to an image level set segmentation method based on local gray-scale clustering features. Background technique [0002] Image segmentation plays an important role in the fields of computer vision, pattern recognition, and image processing. Its purpose is to divide the image into multiple regions of interest, and the pixels in each region form an object of interest with specific shape and structural characteristics. Although it has been studied for many years and a variety of segmentation methods have been proposed, it is still a challenge to segment objects of interest from images with complex structures in the presence of noise and gray-scale offset fields. The level set segmentation method transforms the image segmentation into an energy minimization problem of the energy functional defined on the level set fu...
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
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to View More 
Login to View More 
