hough-transform

问题 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 回答1: Here's an article about the Randomized Hough Transform which i believe to be the same as the "probabilistic Hough transform"

问题 Long post - please bear with me. For a better understanding of what the goal is and what I have done so far, I have posted the code. Please let me know if any further information is required. I have an image (as shown) and the goal is to correctly classify the number of positive (blue) and negative (purple) circles . I do not care about the semi-circles in the image . As shown in the image, there are 29 circles (excluding the semi circles) in which there 7 positive ones. But my code detects

问题 Hough Transform can be used to extract lines from images. It can also be used to extract curves - this is a little harder though because higher dimensional Hough transforms are resource consuming. I was wondering whether how one restricts the Hough transform to 2D voting space for a curve of order 3 i.e. x^{3}+ax^{2}+bx+c ? Anyone know any good sites explaining this (can't seem to find any). Or an explanation here if there isn't one :). 回答1: The essence of the Generalised Hough Transform that

问题 Hough Transform can be used to extract lines from images. It can also be used to extract curves - this is a little harder though because higher dimensional Hough transforms are resource consuming. I was wondering whether how one restricts the Hough transform to 2D voting space for a curve of order 3 i.e. x^{3}+ax^{2}+bx+c ? Anyone know any good sites explaining this (can't seem to find any). Or an explanation here if there isn't one :). 回答1: The essence of the Generalised Hough Transform that

问题 Hi guys I am working on a project where we are trying to detect whether the eye is closed or open in a picture ... what we done so far is that we detected the face then the eyes and then we applied hough transform hoping that the Iris would be the only circle when the eye is open the problem is that when the eye is closed ... it produces a circle as well Here is the code: import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.core.MatOfRect; import org.opencv.core.Point;

问题 I am just being adventurous and taking my first baby step toward computer vision. I tried to implement the Hough Transformation on my own but I just don't get the whole picture. I read the wikipedia entry, and even the original "use of the hough transformation to detect lines and curves in pictures" by richard Duda and Peter Hart, but didn't help. Can someone help explaining to me in a more friendly language? 回答1: It's more common to think of a line in rectangle coordinates, i.e. y = mx + b .

问题 I am just being adventurous and taking my first baby step toward computer vision. I tried to implement the Hough Transformation on my own but I just don't get the whole picture. I read the wikipedia entry, and even the original "use of the hough transformation to detect lines and curves in pictures" by richard Duda and Peter Hart, but didn't help. Can someone help explaining to me in a more friendly language? 回答1: It's more common to think of a line in rectangle coordinates, i.e. y = mx + b .

问题 I m trying to obtain a threshold of the calibration chessboard. I cant detect directly the chessboard corners as there is some dust as i observe a micro chessboard. I try several methods and HoughLinesP seems to be the easiest approach. But the results are not good, how to improve my results? import numpy as np import cv2 img = cv2.imread('lines.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray,50,150,apertureSize = 3) print img.shape[1] print img.shape minLineLength

问题 How can I get the intersection points of lines down using opencv Hough lines algorithm? Here is my code: import cv2 import numpy as np import imutils im = cv2.imread('../data/test1.jpg') gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 60, 150, apertureSize=3) img = im.copy() lines = cv2.HoughLines(edges,1,np.pi/180,200) for line in lines: for rho,theta in line: a = np.cos(theta) b = np.sin(theta) x0 = a*rho y0 = b*rho x1 = int(x0 + 3000*(-b)) y1 = int(y0 + 3000*(a)) x2 =

问题 After processing an image by converting it to grey scale and then blurring it, I'm trying to apply a Hough Circle Transformation with these parameters: CV_HOUGH_GRADIENT dp = 1 min_dist = 1 param_1 = 70 param_2 = 100 min_radius = 0 max_radius = 0 Here is one of the many images I've tried: http://i.stack.imgur.com/JGRiM.jpg But the algorithm fails to recognise the ball even with relaxed parameters. (When I try it with an image of a circle created in GIMP it works fine) 回答1: I agree with krzych