CN107220988B - Part image edge extraction method based on improved canny operator - Google Patents
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Abstract
The invention discloses a part image edge extraction method based on an improved canny operator, which comprises the steps of firstly obtaining a part color image and converting the part color image into a gray scale image; then, carrying out frequency domain filtering on the gray level image by utilizing Fourier transform; performing convolution sharpening on the filtered gray level image by using prewitt operator templates in 4 directions to obtain a maximum gradient image of the image; then calculating a gray level histogram of the maximum gradient map, obtaining a threshold value T by utilizing a valley bottom method, dividing the gray level map into 2 regions by utilizing the threshold value T, and calculating a high threshold value and a low threshold value by respectively using a maximum inter-class variance method for the 2 regions; and finally, binarizing the gray level image according to the high and low threshold values, taking the binary image generated by the high threshold value as an edge basis, and connecting the edge basis by using the binary image generated by the low threshold value to obtain a complete edge binary image. The method improves the edge extraction precision, adaptively generates high and low thresholds and improves the efficiency.
Description
Technical Field
The invention belongs to the field of image processing and machine vision, and particularly relates to a part image edge extraction method based on an improved canny operator.
Background
An image edge is one of the most basic features of an image and contains most of the information of the image. In the field of image processing and computer vision, image edge detection is the most basic technique, and plays an important role in subsequent processing. In the image of the part, the accurate detection of the edge of the part plays an important role in checking the qualification degree of the part.
The image edge detection can be divided into the following 3 steps:
1. denoising an image: when the first-order and second-order derivatives of the image gray scale are used for image edge detection, the derivative calculation of the image is greatly influenced by noise, so that the image is denoised firstly to improve the performance of edge detection.
2. Edge enhancement: calculating the magnitude of the image gradient can highlight the points in the neighborhood of points in the image or points where the local intensity changes are significant.
3. And (3) detection: in general, the gradient amplitude of the image edge points is relatively large, but in practice, there are many points with large image gradient amplitude, and these points are not all edges sometimes, so that an appropriate method is used to determine which are edge points. Edge detection methods are generally classified into operator detection methods of first-order derivatives and second-order derivatives.
The edge is where the change of the gray level of the image is significant, and the derivative value at the abrupt change of the gray level of the image is also large, so the change of the gray level is generally expressed by the magnitude of the derivative of the gray level. Image point ux(x, y) and point uyThe gradient of (x, y) is defined as the following vector:
the magnitude of the gradient is given by:
depending on the size and weight of the convolution template, many gradient operators are present.
However, since it is always difficult to simultaneously ensure the denoising and the positioning accuracy of the image edge, when the noise is smoothed, it is not desirable to smooth the edge information, and also, when the noise is sharpened, it is desirable to sharpen only the edge without receiving the interference of the noise. How to balance the relationship between the two is a core problem in image edge extraction. Meanwhile, in the edge operator, most templates only have good response to horizontal and vertical edges, and have poor extraction capability to edges at other angles.
The Canny operator is an optimization operator that combines image smoothing, edge enhancement, and detection. During edge detection, firstly a Gaussian function is used for carrying out convolution on an image to remove noise, then a first-order difference template is used for calculating the direction and gradient amplitude of each pixel point, then a non-maximum suppression principle is used for highlighting the point with obvious change of each point neighborhood intensity value of the image so as to achieve the effect of edge enhancement, and finally two thresholds are used for detecting and connecting edges to finally obtain an edge image.
However, the conventional first-order difference template only has good response to vertical and horizontal edges, and has poor effect on detecting edges in other directions. Meanwhile, in the final selection of the two thresholds, the optimal threshold is difficult to select by adopting a manual selection method, so that the final edge selection and edge connection are influenced. Improper threshold selection can cause the image to generate many false edges and noise, which interfere with further processing and adaptation of the image at a later stage.
Disclosure of Invention
The invention aims to provide a part image edge extraction method based on an improved Canny operator, which is used for automatically generating a single-pixel binary image containing clear target part edges and facilitating the subsequent analysis of each parameter of a part.
The technical solution for realizing the purpose of the invention is as follows: a part image edge extraction method based on an improved canny operator comprises the following steps:
step 2, carrying out frequency domain filtering on the gray level image;
step 3, carrying out convolution sharpening on the filtered gray level image by using prewitt operator templates in 4 directions to obtain a maximum gradient image of the image;
step 4, calculating a gray level histogram of the maximum gradient map, obtaining a threshold value T by using a valley bottom method, dividing the gray level map into 2 regions by using the threshold value T, and calculating a high threshold value and a low threshold value by using a maximum inter-class variance method for the 2 regions respectively;
and 5, binarizing the gray level image according to the high and low threshold values, taking the binary image generated by the high threshold value as an edge basis, and connecting the edge basis by using the binary image generated by the low threshold value to obtain a complete edge binary image.
The step 2 of performing frequency domain filtering specifically comprises the following steps:
step 2.1, obtaining F (u, v) by adopting a fast Fourier algorithm to the gray-scale image, and moving a zero-frequency point of the F (u, v) to the central position of the spectrogram;
step 2.2, calculate the product G (u, v) of the Gaussian function filter function H (u, v) and F (u, v), shift the zero frequency point of the spectrum G (u, v) back to the upper left corner of the spectrogram, wherein
The gaussian filter function is expressed as:
where u and v are frequency domain variables, and M, N are the number of horizontal and vertical pixels of the image, respectively. Sigma is a sigma parameter of a Gaussian function;
the spectrum is represented as:
G(u,v)＝H(u,v)×F(u,v)
and 2.3, performing inverse discrete Fourier transform on G (u, v) to obtain G (x, y), and taking a real part of G (x, y) as a filtered result image.
The specific step of solving the maximum gradient map in the step 3 is as follows:
step 3.1, respectively convolving the filtered gray level images by utilizing templates in 45 degrees, 135 degrees, horizontal and vertical directions of prewitt;
and 3.2, taking the maximum value of the 4 convolution results of each pixel as a gradient value, wherein the direction corresponding to the maximum value is the maximum gradient direction, and obtaining the maximum gradient image.
The specific method for calculating the low threshold in the step 4 is as follows:
let Q (x, y) be the maximum gradient map, and the gray scale range of Q (x, y) be [0, L-1%]If the threshold value is T by using the valley bottom method, the threshold value T divides Q (x, y) into 2 ranges [0, T ]]And [ T, L-1]]Let n be the number of pixels in the image with i gray scaleiIn the gray scale range of [0, T]The total number of pixels in the interior is:
the probability of each gray level occurrence is:
at [0, T]In the threshold value T1Divide it into class 2C0And C1，C0From [0, T1-1]Composition C of1From [ T ]1,T]Composition, then region C0And C1The probabilities of (c) are respectively:
P1＝1-P0
C0and C1The average gray levels of (a) are:
where μ is the average gray scale of [0, T ]:
μ＝P0μ0+P1μ1
the total variance of the two regions is:
σB 2＝P0(μ0-μ)2+P1(μ1-μ)2＝P0P1(μ0-μ1)2
let T be [0, T ]1-1]Take values in turn, so that sigmaB 2The maximum value of T is the best choice for the low threshold.
In a similar way, in [ T1,L-1]The above steps are repeated, and the optimal selection of the high threshold value can be obtained.
Compared with the prior art, the invention has the following advantages: 1) the invention preprocesses the image through Gaussian filtering and well filters the noise. 2) In the sharpening operator, prewitt operators in four directions are used as gradient templates, and templates in two directions of 45 degrees and 135 degrees are added on the basis of two templates, namely a vertical template and a horizontal template, so that good response is achieved on edges in 8 directions, and the edge extraction precision is improved. 3) In the aspect of binary high and low threshold selection, a histogram valley method and a maximum inter-class variance method are combined, the high and low thresholds are generated in a self-adaptive manner, the efficiency is improved, and the precision is improved.
Drawings
Fig. 1 is an overall schematic diagram of an image edge extraction process.
Fig. 2 is a schematic diagram of a frequency domain filtering process.
Fig. 3 is a schematic diagram of an edge sharpening process.
FIG. 4 is a diagram illustrating adaptive threshold selection and final edge binarization.
FIG. 5 is a graph of edge binary values generated by the method of the present invention.
FIG. 6 is a graph of edge binary values generated using a conventional method.
FIG. 7 is a diagram of 4 templates of the Prewitt operator.
Detailed Description
The invention will be further explained with reference to the drawings.
The method for extracting the edge of the part image based on the improved canny operator comprises the steps of firstly opening an industrial camera and connecting the industrial camera to a PC (personal computer), fixing a shot part, adjusting a focal length, determining that a clear picture can be collected, shooting a clear map picture after the installation is finished, and transmitting the clear map picture into a computer processing system through the Ethernet, wherein a processing platform is visual studio 2010+ opencv2.4.10. The image processing step is then entered:
Step 2, carrying out frequency domain filtering on the gray level image, and specifically comprising the following steps:
step 2.1, calculating discrete Fourier transform of F (x, y), wherein due to low direct implementation efficiency of DFT, a time-extracted fast Fourier algorithm (DIT-FFT) is adopted to obtain F (u, v), and a zero frequency point of the F (u, v) is moved to the center position of a spectrogram;
step 2.2, calculating a product G (u, v) of the filter functions H (u, v) and F (u, v), namely a frequency spectrum, wherein a Gaussian function is adopted in the method:
wherein u and v are frequency domain variables, and M and N are the number of horizontal and vertical pixels of the image respectively. σ is the sigma parameter of a gaussian function.
The spectrum is the product of the filter functions H (u, v) and F (u, v):
G(u,v)＝H(u,v)×F(u,v)
then, the zero frequency point of the frequency spectrum G (u, v) is shifted back to the position of the upper left corner of the frequency spectrum graph;
and 2.3, calculating Inverse Discrete Fourier Transform (IDFT) on G (u, v) in the step 2.2 to obtain G (x, y), and taking a real part of the G (x, y) as a filtered result image.
And 3, performing convolution sharpening on the filtered gray level image by using prewitt operator templates in 4 directions to obtain a maximum gradient image of the image, wherein the specific steps are as follows:
step 3.1, respectively convolving the filtered gray level map g (x, y) by using templates of 45 °, 135 °, horizontal and vertical directions of Prewitt, wherein 4 templates of the Prewitt operator are shown in fig. 7:
and 3.2, taking the maximum value of the 4 convolution results of each pixel as a gradient value, wherein the direction corresponding to the maximum value is the maximum gradient direction, and thus obtaining a maximum gradient image Q (x, y) after the processing.
And 4, step 4: the maximum gradient image is thresholded according to high and low thresholds, and the traditional threshold selection is finished manually, so that a plurality of tests are required and false edges can be generated. The method for adaptively selecting the high and low thresholds by using the combination method of the histogram valley and the maximum between-class variance comprises the following specific steps:
step 4.1, calculating a gray level histogram of the maximum gradient image Q (x, y), obtaining a threshold value T by using a valley bottom method, dividing the gray level image into 2 areas by using the threshold value T, and dividing the Q (x, y) into 2 ranges [0, T ] and [ T, L-1] by using the threshold value T if the gray level range of the Q (x, y) is [0, L-1 ];
step 4.2, respectively using a maximum inter-class variance method for 2 areas to calculate the high and low thresholds, wherein the specific method comprises the following steps:
let n be the number of pixels with i gray in the imageiIn the gray scale range of [0, T]The total number of pixels in the interior is:
the probability of each gray level occurrence is:
at [0, T]In the threshold value T1Divide it into class 2C0And C1，C0From [0, T1-1]Composition C of1From [ T ]1,T]Composition, then region C0And C1The probabilities of (c) are respectively:
P1＝1-P0
C0and C1The average gray of (d) is:
where μ is the average gray scale of [0, T ]:
μ＝P0μ0+P1μ1
the total variance of the two regions is:
σB 2＝P0(μ0-μ)2+P1(μ1-μ)2＝P0P1(μ0-μ1)2
let T be [0, T ]1-1]Take values in turn, so that sigmaB 2The maximum value of T is the best choice for the low threshold;
in a similar way, in [ T1,L-1]The above steps are repeated to obtain the optimal selection of the high threshold, and the specific method is not repeated.
Step 5, obtaining two threshold edge images N by using the high and low thresholds in the step 41[i,j]，N2[i,j]， N1[i,j]Derived from a low threshold, N2[i,j]Resulting from the high threshold. Due to N2[i,j]From the high threshold, the edge is basically true, but there is a break, so N is sought in 8 fields of the break1[i,j]Until N is connected2[i,j]Until they are connected, a complete edge binary map is obtained.
As can be seen from fig. 5 and 6, the edge binary image generated by the conventional prewitt method has a lot of noise and many false edges, and an accurate single-pixel edge cannot be obtained.
The edge binary image generated by the method of the invention firstly effectively filters most of noise, reduces the generation of false edges in the aspect of edges and generates accurate single-pixel edges.
Claims (3)
1. The part image edge extraction method based on the improved canny operator is characterized by comprising the following steps of:
step 1, acquiring a color image of a part, and converting the color image into a gray-scale image;
step 2, carrying out frequency domain filtering on the gray level image;
step 3, carrying out convolution sharpening on the filtered gray level image by using prewitt operator templates in 4 directions to obtain a maximum gradient image of the image;
step 4, calculating a gray level histogram of the maximum gradient map, obtaining a threshold value T by using a valley bottom method, dividing the gray level map into 2 regions by using the threshold value T, and calculating a high threshold value and a low threshold value by using a maximum inter-class variance method for the 2 regions respectively;
step 5, binarizing the gray level image according to the high and low threshold values, taking the binary image generated by the high threshold value as an edge basis, and connecting the edge basis by using the binary image generated by the low threshold value to obtain a complete edge binary image;
the specific method for calculating the low threshold in the step 4 is as follows:
let Q (x, y) be the maximum gradient map, and the gray scale range of Q (x, y) be [0, L-1%]If the threshold value is T by using the valley bottom method, the threshold value T divides Q (x, y) into 2 ranges [0, T ]]And [ T, L-1]]Let n be the number of pixels in the image with i gray scaleiIn the gray scale range of [0, T]The total number of pixels in the interior is:
the probability of each gray level occurrence is:
at [0, T]In the threshold value T1Divide it into class 2C0And C1，C0From [0, T1-1]Composition C of1From [ T ]1,T]Composition, then region C0And C1The probabilities of (c) are respectively:
P1＝1-P0
C0and C1The average gray levels of (a) are:
where μ is the average gray scale of [0, T ]:
μ＝P0μ0+P1μ1
the total variance of the two regions is:
σB 2＝P0(μ0-μ)2+P1(μ1-μ)2＝P0P1(μ0-μ1)2
let T be [0, T ]1-1]Take values in turn, so that sigmaB 2The maximum value of T is the best choice for the low threshold.
2. The method for extracting the edge of the image based on the modified Canny operator as claimed in claim 1, wherein the step 2 of performing the frequency domain filtering specifically comprises the following steps:
step 2.1, obtaining F (u, v) by adopting a fast Fourier algorithm to the gray-scale image, and moving a zero-frequency point of the F (u, v) to the central position of the spectrogram;
step 2.2, calculate the product G (u, v) of the Gaussian function filter function H (u, v) and F (u, v), shift the zero frequency point of the spectrum G (u, v) back to the upper left corner of the spectrogram, wherein
The gaussian filter function is expressed as:
wherein u and v are frequency domain variables, M, N are the number of horizontal and vertical pixels of the image respectively, and sigma is a sigma parameter of a Gaussian function;
the spectrum is represented as:
G(u,v)＝H(u,v)×F(u,v)
and 2.3, performing inverse discrete Fourier transform on G (u, v) to obtain G (x, y), and taking a real part of G (x, y) as a filtered result image.
3. The method for extracting the image edge based on the modified Canny operator as claimed in claim 1, wherein the step 3 of obtaining the maximum gradient map comprises the following specific steps:
step 3.1, respectively convolving the filtered gray level images by utilizing templates in 45 degrees, 135 degrees, horizontal and vertical directions of prewitt;
and 3.2, taking the maximum value of the 4 convolution results of each pixel as a gradient value, wherein the direction corresponding to the maximum value is the maximum gradient direction, and obtaining the maximum gradient image.
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