edge-detection | 易学教程

Sobel filter kernel of large size

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问题 I am using a sobel filter of size 3x3 to calculate the image derivative. Looking at some articles on the internet, it seems that kernels for sobel filter for size 5x5 and 7x7 are also common, but I am not able to find their kernel values. Could someone please let me know the kernel values for sobel filter of size 5x5 and 7x7? Also, if someone could share a method to generate the kernel values, that will be much useful. Thanks in advance. 回答1: UPDATE 23-Apr-2018: it seems that the kernels

Canny Edge Image - Noise removal

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问题 I have a Canny edge detected image of a ball (see link below) which contains a lot of noisy edges. What are the best image processing techniques that I can use to remove these noisy edges without removing the edges belonging to the ball? Original image Canny edge image Many thanks everyone in advance for your help and advice, much appreciated! Ps I am trying to clean up the edge image prior to using the Circle Hough Transform to detect the ball. 回答1: The best option is to filter the image

Edge detection on colored background using OpenCV

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问题 I am using following code to detect edges from given document. private Mat edgeDetection(Mat src) { Mat edges = new Mat(); Imgproc.cvtColor(src, edges, Imgproc.COLOR_BGR2GRAY); Imgproc.GaussianBlur(edges, edges, new Size(5, 5), 0); Imgproc.Canny(edges, edges, 10, 30); return edges; } And then I can find the document from this edges by finding largest contour from this. My problem is I can find the document from following pic: but not from following pic: How can I improve this edge detection?

OpenCV's Canny Edge Detection in C++

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问题 I want to extract the edges of hand but I get the following result. I've tried adjusting the low and high threshold but I still can't get the desired output. I have included below the code and its output. What seems to be the problem? This is the output image generated by the code below. #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> int main(){ cv::Mat image= cv::imread("open_1a.jpg"); cv::Mat contours; cv::Mat gray_image;

Smoothing a jagged path

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I was participating in the thread Image/Graphic into a Shape the other day and made a hackish attempt to get the outline of an image by adding a Rectangle iteratively to an Area . That was very slow. This example instead builds a GeneralPath and creates the Area from the GP. Much faster. The image on the upper left is the 'source image'. The two on the right are various stages of processing the outline. Both of them have jagged edges around the circle and along the slanted sides of the triangle. I'd like to gain a shape that has that jaggedness removed or reduced. In ASCII art. Case 1: 1234 1

character reconstruction and filling for OCR

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问题 I am working with text recognition on tires. In order to use an OCR, I must first get a clear binary map. I have processed images and the text appears with broken and discontinued edges. I have tried standard erosion/dilation with circular discs and line element in MATLAB, but it does not really help. Pr1- Any ideas on how to reconstruct these characters and fill the gap in between the strokes of characters? Pr2- The images above are higher resolution and under good illumination. However, if

Image segmentation based on edge pixel map [closed]

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问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . I have trained a classifier in Python for classifying pixels in an image of cells as edge or non edge. I\'ve used it successfully on a few image datasets but am running into problems with this particular dataset, which seems pretty ambiguous even to the human eye. I don\'t know of

Smoothing a jagged path

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问题 I was participating in the thread Image/Graphic into a Shape the other day and made a hackish attempt to get the outline of an image by adding a Rectangle iteratively to an Area . That was very slow. This example instead builds a GeneralPath and creates the Area from the GP. Much faster. The image on the upper left is the \'source image\'. The two on the right are various stages of processing the outline. Both of them have jagged edges around the circle and along the slanted sides of the