Improved-canny-operator-based part image edge extraction method

An image edge and extraction method technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problems of poor edge detection effect, interfere with image processing and application, difficult to choose, etc., to improve efficiency and improve edge extraction. Accuracy, the effect of improving accuracy

Active Publication Date: 2017-09-29
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0014] However, the traditional first-order difference template only has a good response to vertical and horizontal edges, and the edge detection effect on other directions is poor.
At the same time, in the selection of the final two thresholds, it is difficult to select the optimal threshold by manual selection, which affects the final edge selection and edge connection.
Improper threshold selection will make the image produce a lot of false edges and noise, which will interfere with the further processing and application of the image in the later stage.

Method used

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  • Improved-canny-operator-based part image edge extraction method
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  • Improved-canny-operator-based part image edge extraction method

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with accompanying drawing.

[0060] The edge extraction method of parts images based on the improved canny operator, firstly turn on the industrial camera and connect it to the PC, fix the parts to be photographed, adjust the focal length, make sure that a clear picture can be collected, and take a clear picture after the installation is completed The map image is transmitted to the computer processing system through Ethernet, and the processing platform is visual studio 2010+opencv2.4.10. Next enter the image processing steps:

[0061] Step 1. Perform grayscale conversion on the collected RGB color image to generate a grayscale image. According to the threshold value of the grayscale image, the grayscale image can be regarded as an f(x, y) function.

[0062] Step 2. Perform frequency domain filtering on the grayscale image, the specific steps are:

[0063] Step 2.1, calculate the discrete Fourier tr...

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Abstract

The invention discloses an improved-canny-operator-based part image edge extraction method. A part color image is obtained and the obtained part color image is converted into a grey-scale map; frequency-domain filtering is carried out on the grey-scale map by using Fourier transform; with prewitt operator templates in four directions, convolution sharpening is carried out on the filtered grey-scale map to obtain a maximum gradient graph of the image; a grey level histogram of the maximum gradient graph is calculated, a threshold T is obtained by using a lowest point method, the grey-scale map is divided into two regions by using the threshold T, the two regions are processed by using an OTSU method to calculate a high threshold and a low threshold; and then binarization of the grey-scale map is carried out based on the high threshold and the low threshold, with a binary image generated by the high threshold as an edge base, edge base connection is carried out by using a binary image generated by the low threshold to obtain a complete edge binary image. Therefore, the edge extraction precision is improved; the high and low thresholds are generated adaptively; and the efficiency is improved.

Description

technical field [0001] The invention belongs to the field of image processing and machine vision, and in particular relates to a part image edge extraction method based on an improved canny operator. Background technique [0002] Image edge is one of the most basic features of the image, which contains most of the information of the image. In the field of image processing and computer vision, image edge detection is the most basic technology and plays an important role in subsequent processing. In the component image, the precise detection of the edge of the component plays an important role in checking the qualification of the component. [0003] Image edge detection can be divided into the following three steps: [0004] 1. Image denoising: When using the first-order and second-order derivatives of image grayscale for image edge detection, the calculation of image derivatives is greatly affected by noise, so first denoise the image to improve the performance of edge dete...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/13
CPCG06T7/13G06T2207/20024
Inventor 黄成金威宋跃磊陈嘉方杰徐志良
Owner NANJING UNIV OF SCI & TECH
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