hough-transform

What I'm trying to do is measure the thickness of the eyeglasses frames. I had the idea to measure the thickness of the frame's contours (may be a better way?). I have so far outlined the frame of the glasses, but there are gaps where the lines don't meet. I thought about using HoughLinesP, but I'm not sure if this is what I need. So far I have conducted the following steps: Convert image to grayscale Create ROI around the eye/glasses area Blur the image Dilate the image (have done this to remove any thin framed glasses) Conduct Canny edge detection Found contours These are the results: This

I have a simple grid in an image, I am trying to determine the grid size, e.g. 6x6, 12x12, etc. Using Python and cv2. I am testing it with the above 3x3 grid, I was planning to determine the grid size by counting how many vertical / horizontal lines there are by detecting them in the image: import cv2 import numpy as np im = cv2.imread('photo2.JPG') gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) imgSplit = cv2.split(im) flag,b = cv2.threshold(imgSplit[2],0,255,cv2.THRESH_OTSU) element = cv2.getStructuringElement(cv2.MORPH_CROSS,(1,1)) cv2.erode(b,element) edges = cv2.Canny(b,150,200,3,5) while

I am trying to detect circles in my images. I have written the following code in C# using EmguCV. Most of the times it works, but there are some cases that it detects smaller or larger circles that are slightly shifted to a side. Here is my code: Thread.Sleep(1000); imgCrp.Save(DateTime.Now.ToString("yyMMddHHmmss") + ".jpg"); imgCrpLab = imgCrp.Convert<Lab, Byte>(); imgIsolatedCathNTipBW = new Image<Gray, Byte>(imgCrp.Size); CvInvoke.cvInRangeS(imgCrpLab.Split()[2], new MCvScalar(0), new MCvScalar(100), imgIsolatedCathNTipBW); imgCrpNoBgrnd = imgCrp.Copy(imgIsolatedCathNTipBW.Not());

Does anyone know the particular algorithm for Probabilistic Hough Transform in the OpenCV's implementation? I mean, is there a reference paper or documentation about the algorithm? To get the idea, I can certainly look into the source code, but I wonder if there is any documentation about it. -- it's not in the source code's comments (OpenCV 1.0). Thank you! -Jin Here's an article about the Randomized Hough Transform which i believe to be the same as the "probabilistic Hough transform" used in OpenCV http://en.wikipedia.org/wiki/Randomized_Hough_Transform basically, you dont fill up the

I'm using Hough Lines to do corner detection for this image. i plan to find the intersection of the lines as the corner. This is the image. Unfortunately, Hough return lots of lines for each line I expect How do I tune the Hough Lines so there is only four lines each corresponds to actual line on the image? Rahul galgali Collect the intersection of all line for (int i = 0; i < lines.size(); i++) { for (int j = i + 1; j < lines.size(); j++) { cv::Point2f pt = computeIntersectionOfTwoLine(lines[i], lines[j]); if (pt.x >= 0 && pt.y >= 0 && pt.x < image.cols && pt.y < image.rows) { corners.push

I'm trying to write an application to find the numbers inside an image and add them up. How can I identify the written number in an image? There are many boxes in the image I need to get the numbers in the left side and sum them to give total. How can I achieve this? Edit: i did a java tesseract ocr on the image but i didnt get any correct results. how can i train it? also i did a edge detection i got this: You will most likely need to do the following: Apply the Hough Transform algorithm on the entire page, this should should yield a series of page sections. For each section you get, apply it