Image registration method based on density clustering

A technology of density clustering and image registration, which is applied in the field of image processing to achieve the effect of saving memory and computing power, improving processing speed, and intuitive features.

Active Publication Date: 2021-09-17
ARMY MEDICAL UNIV
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Registration algorithms based on deep learning have the highest accuracy on specific datasets, but require a large amount of data for training

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
  • Image registration method based on density clustering
  • Image registration method based on density clustering
  • Image registration method based on density clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0087] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

[0088] refer to Figure 1-9 , the present invention provides an image registration method based on density clustering:

[0089] Such as figure 1 As shown, the image registration method based on density clustering includes:

[0090] S1. Extracting the first final feature point set of the template image and the second final feature point set of each frame of the target image.

[0091] Specifically, if the target image is one frame, the S1 includes:

[0092] S11. Extracting the first initial feature point of the template image an...

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 discloses an image registration method based on density clustering. The method comprises the following steps: extracting a first final feature point set of a template image and a second final feature point set of each frame of a target image; generating description vectors of all first final feature points in the first final feature point set and all second final feature points in the second final feature point set; matching the first final feature points with the second final feature points of the current frame of the target image according to the similarity of the description vectors; calculating registration parameters according to all successfully matched first final feature points and the second final feature points of the current frame of the target image; and performing image transformation on the current frame of the target image according to the registration parameters. According to the method, the feature points of the target image are quickly updated frame by frame through the density clustering method, and the registration processing speed is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image registration method based on density clustering. Background technique [0002] The configuration is to select a fixed reference image as a template, and an image with a relative offset as the target, and calculate the coordinate offset of the template image through an algorithm, and then make rigid or non-rigid changes to the target image to make the target image It is in the same relative coordinates as the template image. The mainstream algorithms include: registration algorithm based on feature point matching, registration algorithm based on similarity measure, and registration algorithm based on deep learning. [0003] The registration algorithm based on feature point matching is a global search strategy by traversing all pixel points, describing the gradient of a certain point neighborhood and defining points that meet certain rules as feature points; such...

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): G06K9/62
CPCG06F18/23G06F18/22
Inventor 刘伟壹谌小维廖祥
Owner ARMY MEDICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap