edge-detection

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. The best option is to filter the image before applying the edge detector. In order to keep the sharp edges you need to use a more sophisticated filter

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. UPDATE 23-Apr-2018: it seems that the kernels defined in the link below are not true Sobel kernels (for 5x5 and above) - they may do a reasonable job of edge

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? I use Python, but the main idea is the same. If you directly do cvtColor: bgr -> gray for img2, then

I have this image of a battery: I would like to determine the dimensions of the battery (in pixels). The problem I have is that the battery is rotated by an unknown angle. How can I detect the dimensions of this rotated battery? I was thinking of these algorithmic steps: First, I would have to convert this image to a black and white image (thresholding). After that, I would have to find a center point and draw a rectangle in the white pixels. Then, I have to turn the rectangle 360 degrees and locate the position of the rectangle (and so the dimensions). I am somewhat inexperienced, and I would

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; cvtColor( image, gray_image, CV_RGB2GRAY ); cv::Canny(image,contours,10,350); cv::namedWindow("Image"); cv: