Image marking method combining local image constraint and overall target constraint

A technology of global constraints and local constraints, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as large computational complexity

Active Publication Date: 2017-04-19
NINGBO UNIV
View PDF5 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Chen F, Yu H, Hu R, et al.Deep Learning Shape Priors for ObjectSegmentation[C]//Computer Vision and Pattern Recognition,2013 (Chen Fei, Yu Huimin, Hu Haoji, Zeng Xunxun, deep learning shape priors for images Segmentation [C]//Computer Vision and Pattern Recognition Conference, 2013), which proposes to combine ...

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 marking method combining local image constraint and overall target constraint
  • Image marking method combining local image constraint and overall target constraint
  • Image marking method combining local image constraint and overall target constraint

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0050] An image marking method that combines image local constraints and object global constraints proposed by the present invention, its flow chart is as follows Figure 7 As shown, it includes the following steps:

[0051] ① Select an image library, which contains M original images and the mask images corresponding to each original image, and record the mth original image in the image library as Will The corresponding mask image is recorded as Then, the superpixel segmentation method is used to perform superpixel segmentation on each original image in the image library to obtain the superpixel region node image corresponding to each original image in the image library, and The corresponding superpixel area node image is denoted as Will the nth of m A superpixel area node is denoted as Then find out the mask region corresponding...

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 marking method combining a local image constraint and an overall target constraint. The method comprises the following steps of acquiring a super pixel area node image of each original image, finding a mask area corresponding to each super pixel area node of the corresponding super pixel area node image in a mask image corresponding to each original image and marking; then, using a characteristic set of the super pixel area node image and an area label set corresponding to the mark image to train a conditional random field model, using a virtual label set corresponding to images acquired after all the mask areas are marked in the mask image to train a shape Boltzmann machine model; and through a grid partitioning technology, effectively combining the conditional random field model and the shape Boltzmann machine model. The local image constraint and the overall target constraint are tightly combined and accuracy of image marking is increased. The method can be suitable for a condition in which a data set is small and image resolution is low. And computation complexity is low.

Description

technical field [0001] The invention relates to an image marking technology, in particular to an image marking method combining image local constraints and object global constraints. Background technique [0002] Image segmentation and image labeling are the core technologies in computer vision. Nowadays, many high-level applications in computer vision rely on accurate image segmentation results or image labeling results, such as object recognition and scene analysis applications. Image segmentation and image labeling have always been one of the most challenging tasks in computer vision due to problems such as occlusion, shadows, and similarity between objects and background features in images. [0003] As the basis and challenging task of various vision applications, image marking technology has been widely concerned in the field of computer vision. How to better mark images has been deeply studied by relevant institutions at home and abroad. In recent years, since Conditi...

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): G06T7/11
CPCG06T2207/20081G06T2207/30204
Inventor 王浩郭立君张荣
Owner NINGBO 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