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

A Massive Data Multi-resolution Volume Rendering Method Based on Tensor Approximation

A multi-resolution, massive data technology, applied in the field of volume rendering, can solve problems such as unsatisfactory, reduce data volume and extract structural features, reduce processing time, reduce overall resolution size, and realize multi-resolution processing Effect

Active Publication Date: 2018-07-10
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At this time, multi-resolution processing based on information entropy can no longer meet the requirements of effectively reducing the amount of data and extracting structural features.

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
  • A Massive Data Multi-resolution Volume Rendering Method Based on Tensor Approximation
  • A Massive Data Multi-resolution Volume Rendering Method Based on Tensor Approximation
  • A Massive Data Multi-resolution Volume Rendering Method Based on Tensor Approximation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] In order to facilitate those skilled in the art to understand the technical content of the present invention, the content of the present invention will be further explained below in conjunction with the accompanying drawings.

[0044] Such as figure 1As shown, a kind of massive data multi-resolution volume rendering method based on tensor approximation of the present invention comprises the following steps:

[0045] S1: Divide the original data into blocks to obtain several data blocks;

[0046] S2: performing tensor decomposition on each data block obtained in step S1;

[0047] S3: Perform multi-resolution processing on each data block obtained in step S2;

[0048] S4: Reconstruct each data block obtained in step S3, and create a two-dimensional texture, and perform seismic data drawing according to the reconstructed data block.

[0049] The step S1 is specifically: the size of the block size setting is directly related to the amount of information in each data bloc...

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 a multi-resolution volume rendering method for massive data based on tensor approximation. Firstly, the original data is divided into blocks to obtain several data blocks, and then each data block is subjected to tensor decomposition and multi-resolution processing, and finally Reconstruct each data block after tensor decomposition and multi-resolution processing, and create a two-dimensional texture to complete the drawing of seismic data. By adopting the rank truncation method to effectively filter the noise in the original data, and using the factor matrix and core tensor rank truncation to replace the tensor decomposition for each rank trial, the rank of each data block is accurately determined The size of the data block can save the time of selecting the best rank of the data block, and according to the data obtained after the rank truncation, the level of detail of each data block can be selected, and the overall resolution size of the data can be quickly and effectively reduced, and the processing time can be reduced. Resolution processing, and better rendering results than traditional multi-resolution based on information entropy.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a volume rendering technology. Background technique [0002] The visualization technology of volume data is a very common technology, which can be widely used in many fields, such as: medical field, fluid physics field, meteorological field, geological exploration field and so on. Since human beings are most sensitive to the stimulation of visual signals, visualization technology can convert documents, pictures, tables, etc. containing a large amount of information into three-dimensional images, which is convenient for researchers to observe and analyze them intuitively. [0003] Since in real life, the common three-dimensional objects are their surfaces, therefore, in the model based on information entropy, the surface representation is often used to draw a three-dimensional body. However, many times, what people care about is precisely the internal structure of the ...

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 Patents(China)
IPC IPC(8): G06T15/08
Inventor 鲁才张力彬曹琛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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