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Object-Oriented Automated High-Precision Edge Extraction Method

An edge extraction, target-oriented technology, used in image analysis, instrumentation, computing, etc., can solve the problems of initial position sensitivity, divergence, and inability to detect multiple targets.

Inactive Publication Date: 2017-11-03
SICHUAN AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are two problems with the active contour model: one is that it is very sensitive to the initial position; the other is that due to the non-convexity of the model, it may converge to a local extreme point or even diverge
Therefore, it cannot handle the topology changes during the deformation process, so it cannot be used to detect multiple targets.
Moreover, the active contour model is very sensitive to the initial position, and it is difficult to guarantee the accuracy of automatic extraction of edge information of complex background objects

Method used

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  • Object-Oriented Automated High-Precision Edge Extraction Method

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Experimental program
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Effect test

Embodiment 1

[0041] Embodiment 1 Model Training Phase

[0042] 1.1 Training of cascaded classifiers

[0043] First select an edge extraction target, prepare enough positive samples (target image) and negative samples (any image that does not contain the target), then use the integral image to calculate the HAAR features of the image, and then use machine learning algorithms to extract excellent signs and form A strong classifier, and finally a cascade structure is used to hierarchically combine multiple strong classifiers into a final filter.

[0044] The HAAR feature is a gradient feature. This type of feature template is composed of two or more congruent rectangles adjacent to each other. There are white and black rectangles in the feature template, and the feature value of this feature template is Defined as the sum of white rectangle pixels minus the sum of black rectangle pixels. Commonly used simple features and their rotation features are divided into: edge features, linear featur...

Embodiment 2

[0084] Embodiment 2 Edge extraction stage

[0085] 2.1 Rapid elimination of non-target impact parts

[0086] refer to Figure 8 , using the trained cascade classifier sub-window to perform sliding detection on the image to be extracted, from easy to difficult, the first few classifiers of the cascade classifier can quickly filter the easily distinguishable non-target parts, and finally after many In the screening of the layer classifier, the target will be preserved with as few interference parts as possible to reduce the time and false detection rate of edge extraction.

[0087] 2.2 Initial positioning of target edge

[0088] The intercepted region of interest will be used as the input of the ASM model. After image alignment, the initial edge in the model is determined, and then the edge of the neighborhood is corrected with the help of the Canny operator, and finally a more accurate initial target edge is obtained.

[0089] 2.3 Finalization of edges

[0090] In this stag...

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Abstract

The invention discloses a target-oriented automatic high-precision edge extraction method, which includes a model training stage and an edge extraction stage; wherein the model training stage includes the following steps: A1) training of cascaded classifiers based on HAAR features; A2) based on Canny Operator and ASM model training; A3) Active contour model training; the edge extraction stage includes the following steps: B1) Use cascade structure to quickly eliminate non-target components in the image to be processed; B2) Combine Canny operator and ASM model Find the initial position of the target edge; B3) Use the active contour model to calibrate the initial position; B4) Use the samples that do not meet the edge extraction requirements as training samples in the database to feedback and adjust the entire system.

Description

technical field [0001] The invention relates to image detection technology, in particular to a target-oriented automatic high-precision edge extraction method. Background technique [0002] Edge is the most important feature parameter for people to describe and recognize objects in images (such as faces, hands, various objects, etc.). The extraction of digital image edge information (edge ​​extraction), that is, edge detection (edge ​​detection) is of great significance in image segmentation and target overall operation (target selection, overall copy, cutting, etc.). [0003] Most of the current edge information extraction methods use edge extraction operators (such as Sobel operator, Kirsch operator, Prewitt operator, Roberts operator, Canny operator, etc.). In the edge information extraction method based on edge extraction operators, specific operators can only detect edge information in a specific direction, and are sensitive to noise, especially in complex backgrounds,...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/13
Inventor 徐精文刘双
Owner SICHUAN AGRI UNIV
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