A low-rank sparse optimization target segmentation method under Laplacian regularization constraint

A sparse optimization and target segmentation technology, applied in the field of computer vision, can solve the problems of low target segmentation accuracy, errors, and no consideration of the spatial correlation of calculated elements, etc., to reduce complexity, improve accuracy, and improve robustness Effect

Pending Publication Date: 2019-04-16
CHENGDU AERONAUTIC POLYTECHNIC
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, many algorithms at home and abroad adopt a step-by-step method in calculating the space-time consistency, and the step-by-step method of this method will lead to the calculation of subsequent steps due to the calculation error of the previous step. For example, the step-by-step method in the prior art Steps: First, establish a template in the feature space for the target to be tracked or segmented according to the known conditions, and then find the area in the feat...

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 low-rank sparse optimization target segmentation method under Laplacian regularization constraint
  • A low-rank sparse optimization target segmentation method under Laplacian regularization constraint
  • A low-rank sparse optimization target segmentation method under Laplacian regularization constraint

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0046] Such as Figure 1-Figure 2 As shown, the present invention provides a target segmentation method for low-rank sparse optimization under the Laplace regular constraint, and its specific implementation steps are as follows:

[0047] (S1) Use SLIC algorithm to divide each frame of image in the video into N super pixels;

[0048] (S2) Extract the features of different convolution layers in the VGG network, calculate the average feature of each super pixel as the feature of the super pixel, and set up the image feature matrix X, the establishment method of the image feature matrix X is:

[0049] Calculate the average feature of each superpixel

[0050]

[0051] Create an image feature matrix X:

[0052]

[0053] Among them, f j The feature vector built for the superpixel, is the number of pixels contained in super pixel i, ∑ j f j In order to accumulate the pixel feature vector contained in a certain super pixel, j=1, 2...n 1 ,n 1 is the number of pixels in ...

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 provides a low-rank sparse optimization target segmentation method under Laplacian regularization constraint. In a first frame of a video image, In the case of a known target contour tobe segmented, extracting the contour of a set target in a subsequent video frame on line by using a video segmentation technology; Firstly, segmenting each frame of image; extracting super pixels andcalculating hierarchical convolution characteristics of each super pixel; Establishing a feature matrix of an image, then, known information and segmented targets are utilized; establishing or updating a feature template, solving the optimal expression mode of the current image to the template by using a low-rank sparse optimization algorithm under the Laplace regularization constraint, establishing a saliency map according to the solved expression coefficient, and finally performing accurate segmentation on the contour of the established target by using an energy minimization principle. The method has the characteristics of low calculation complexity and high segmentation precision, and is particularly suitable for the field of single-target online segmentation in video images. The methodhas very high popularization and application values.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to an object segmentation method for low-rank sparse optimization under Laplace regular constraints. Background technique [0002] Video target segmentation technology is to extract the outline of the given target in the video. It is widely used in behavior recognition, motion estimation, target recognition and tracking systems as an image preprocessing process. rapidly developing field. The segmentation of the target in the video is usually considered as a binary classification problem, where the target to be segmented represents the foreground, usually represented by 1, and the other parts represent the background, usually represented by 0. The key to video object segmentation is temporal and spatial consistency. Temporal consistency describes the similarity of objects in consecutive frames, and spatial consistency describes the ability to distinguish objects and backgr...

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): G06T7/12
CPCG06T7/12G06T2207/10016
Inventor 顾菘王建杜英杰郝炜张伟瑞
Owner CHENGDU AERONAUTIC POLYTECHNIC
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