Instance segmentation method based on period B spline

A spline and periodic technology, applied in image analysis, image data processing, complex mathematical operations, etc., to achieve the effect of reducing difficulty, maintaining accuracy, and reducing the number of points

Active Publication Date: 2020-06-09
中山仰视科技有限公司
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiencies of the prior art, the present invention provides an instance segmentation method based on periodic B-splines that obtains the vectorized representation of the object contour without increasing the complexity of the model

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
  • Instance segmentation method based on period B spline
  • Instance segmentation method based on period B spline
  • Instance segmentation method based on period B spline

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] refer to figure 1 , an instance segmentation method based on periodic B-splines, obtain the periodic B-spline control points by reversely obtaining the periodic B-spline control points by collecting the object contour coordinate points of the picture, and use the periodic B-spline control points combined with neural network regression to obtain the control points of each periodic B-spline Point length representation and angle representation, establish Gaussian heat map, loss function and target construction formula for neural network training, obtain the Cartesian coordinates of periodic B-spline control points, and achieve vectorization by performing periodic B-spline modeling on object contours The purpose is to return the periodic B-spline control point information through the neural network, so as to quickly and accurately obtain the vectorized representation of the object outline without manual intervention; the examples of the collected pictures are from the COCO (...

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 instance segmentation method based on a period B spline. Reversely solving a period B spline control point by acquiring an object contour coordinate point of the picture; length representation and angle representation of the periodic B-spline control points are obtained by combining the periodic B-spline control points with neural network regression; establishing a Gaussian heat map, a loss function and a target construction formula for neural network training; cartesian coordinates of a periodic B spline control point are obtained, the vectorization purpose is achieved by conducting periodic B spline modeling on an object contour, periodic B spline control point information is returned through a neural network, and therefore vectorization representation of the object contour can be quickly and accurately obtained under the condition that manual intervention is not needed.

Description

technical field [0001] The invention relates to an instance segmentation technique, in particular to an instance segmentation method based on periodic B-splines. Background technique [0002] Instance segmentation is one of the basic tasks of computer vision. It can not only accurately classify objects, but also needs to give the location mask of objects. In recent years, with the application of deep learning in computer vision, based on convolutional neural network The accuracy of the instance segmentation method on related data sets is getting higher and higher, but at the same time, the model structure is becoming more and more complex, and the speed and memory usage cannot meet the actual application requirements. The existing instance segmentation method It is mainly divided into three categories: detection-based, segmentation-based and contour-based. The detection-based instance segmentation method first uses the detector to detect the bounding box of the object, and t...

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): G06T7/73G06T7/12G06N3/04G06K9/62G06F17/18
CPCG06T7/12G06T7/75G06F17/18G06N3/045G06F18/214
Inventor 马力欧阳能良王艳芳苏韶生汪方军
Owner 中山仰视科技有限公司
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