connected-components

I was working on my image processing problem with detecting coins. I have some images like this one here: and wanted to separate the falsely connected coins. We already tried the watershed method as stated on the MATLAB-Homepage: the-watershed-transform-strategies-for-image-segmentation.html especially since the first example is exactly our problem. But instead we get a somehow very messed up separation as you can see here: We already extracted the area of the coin using the regionprops Extrema parameter and casting the watershed only on the needed area. I'd appreciate any help with the

Real-world problem: I have data on directors across many firms, but sometimes "John Smith, director of XYZ" and "John Smith, director of ABC" are the same person, sometimes they're not. Also "John J. Smith, director of XYZ" and "John Smith, director of ABC" might be the same person, or might not be. Often examination of additional information (e.g., comparison of biographical data on "John Smith, director of XYZ" and "John Smith, director of ABC") makes it possible to resolve whether two observations are the same person or not. Conceptual version of the problem: In that spirit, am collecting

We're currently trying to detect the object regions in medical instruments images using the methods available in OpenCV, C++ version. An example image is shown below: Here are the steps we're following: Converting the image to gray scale Applying median filter Find edges using sobel filter Convert the result to binary image using a threshold of 25 Skeletonize the image to make sure we have neat edges Finding X largest connected components This approach works perfectly for the image 1 and here is the result: The yellow borders are the connected components detected. The rectangles are just to

I have a matrix with values 0 or 1 and I would like to obtain a list of groups of adjacent 1's. For example, the matrix mat = rbind(c(1,0,0,0,0), c(1,0,0,1,0), c(0,0,1,0,0), c(0,0,0,0,0), c(1,1,1,1,1)) > mat [,1] [,2] [,3] [,4] [,5] [1,] 1 0 0 0 0 [2,] 1 0 0 1 0 [3,] 0 0 1 0 0 [4,] 0 0 0 0 0 [5,] 1 1 1 1 1 should return the following 4 connected components: C1 = {(1,1);(2,1)} C2 = {(2,4)} C3 = {(3,3)} C4 = {(5,1);(5,2);(5,3);(5,4);(5,5)} Does anybody has an idea of how to do it fast in R? My real matrix is indeed rather large, like 2000x2000 (but I expect that the number of connected

I want to use the function cv2.connectedComponents to connect components on a binary image, like the following... I have added the feature to cv2. connectedComponents to eliminate elements with a small number of pixels. Unfortunately, the algorithm is extremely slow for large images due to the extension. Is there a way to rewrite the extension to speed up the algorithm? import cv2 import numpy as np def zerolistmaker(n): listofzeros = [0] * n return listofzeros img = cv2.imread('files/motorway/gabor/eGaIy.jpg', 0) img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] # ensure binary retval,