edge-detection

问题 I have tried implementing a basic version of shi-tomasi corner detection algorithm. The algorithm works fine for corners but I came across a strange issue that the algorithm also gives high values for slanted(titled) edges. Here's what i did Took gray scale image computer dx, and dy of the image by convolving it with sobel_x and sobel_y Took a 3 size window and moved it across the image to compute the sum of the elements in the window. computed sum of the window elements from the dy image and

问题 I have little background knowledge of image processing and recognition. I am trying to detect principal edges/grayscale transitions on a grayscale image such as a portrait. The problem is that on some parts, the edge is blurred (because of focus). I am using Canny edge detector with multiple thresholds, but I can never detect those edges (chin, clothes, ears, side of the face, ...) Original image: This is the result I am getting: beard, sharp edges This is what features I'm interested in:

问题 I have little background knowledge of image processing and recognition. I am trying to detect principal edges/grayscale transitions on a grayscale image such as a portrait. The problem is that on some parts, the edge is blurred (because of focus). I am using Canny edge detector with multiple thresholds, but I can never detect those edges (chin, clothes, ears, side of the face, ...) Original image: This is the result I am getting: beard, sharp edges This is what features I'm interested in:

问题 I am trying to segment the blood vessels in retinal images using Python and OpenCV. Here is the original image: Ideally I want all the blood vessels to be very visible like this (different image): Here is what I have tried so far. I took the green color channel of the image. img = cv2.imread('images/HealthyEyeFundus.jpg') b,g,r = cv2.split(img) Then I tried to create a matched filter by following this article and this is what the output image is: Then I tried doing max entropy thresholding:

问题 I am trying to segment the blood vessels in retinal images using Python and OpenCV. Here is the original image: Ideally I want all the blood vessels to be very visible like this (different image): Here is what I have tried so far. I took the green color channel of the image. img = cv2.imread('images/HealthyEyeFundus.jpg') b,g,r = cv2.split(img) Then I tried to create a matched filter by following this article and this is what the output image is: Then I tried doing max entropy thresholding:

问题 I'm trying to find the tilt angle in a series of images which look like the created example data below. There should be a clear edge which is visible by eye. However I'm struggling in extracting the edges so far. Is Canny the right way of finding the edge here or is there a better way of finding the edge? import cv2 as cv import numpy as np import matplotlib.pyplot as plt from scipy.ndimage.filters import gaussian_filter # create data xvals = np.arange(0,2000) yvals = 10000 * np.exp((xvals -

问题 I made a program that implements an edge detection algorithm, but it takes a long time to process. I've read about using lockbits, and unsafe state instead of getpixel and setpixel, but I still don't understand how to use it. This is my example code: private Bitmap SobelEdgeDetect(Bitmap original) { Bitmap b = original; Bitmap bb = original; int width = b.Width; int height = b.Height; int[,] gx = new int[,] { { -1, 0, 1 }, { -2, 0, 2 }, { -1, 0, 1 } }; int[,] gy = new int[,] { { 1, 2, 1 }, {

问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second

问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second

问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second