edge-detection

I am new to image processing and I was trying to detect vertical lines using this code- image=imread('benzene.jpg'); BW = im2bw(image); w1=[-1 2 -1 ; -1 2 -1 ; -1 2 -1]; g=(imfilter(double(BW),w1)); g=abs(g); T=max(g(:)); g=g>=T; imshow(g); This was my image- And this is what I got after performming the operations- So my question is why am I getting this output?There are 10 vertical lines if vertical double bonds are counted as 2 distinct vertical lines.Also what if I want to get horizontal,vertical,45 and -45 all the lines,how can I use all the 4 masks to get one single output? One simple

I am looking for the equivalent implementation of the laplacian of gaussian edge detection. In matlab we use the following function [BW,threshold] = edge(I,'log',...) In python there exist a function for calculating the laplacian of gaussian. It is not giving the edges back definitely. scipy.ndimage.filters.gaussian_laplace Any pointer to online implementation or the code Thanks ycyeh What matlab edge() do should be Compute LoG Compute zero crossings on LoG Compute a threshold for local LoG difference Edge pixels = zero crossing && local difference > threshold The LoG filter of scipy only does

I want to make a program that implements sobel edge detection. This is my code : private Bitmap SobelEdgeDetect(Bitmap ori) { 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 }, { 0, 0, 0 }, { -1, -2, -1 } }; int[,] allPixR = new int[width, height]; int[,] allPixG = new int[width, height]; int[,] allPixB = new int[width, height]; int limit = 128 * 128; for (int i = 0; i < width; i++) { for (int j = 0; j < height; j++) { allPixR[i, j] = b.GetPixel(i, j).R

I have to find corners of shapes in an image. i have used Harris corner detection algorithm to find corner, but it is giving total corners present in an image and for finding corners for a particular shape in that image it is not feasible. please suggest some other approach. You could use Harris corner detection algorithm. Corners are junction of two edges, where an edge is a sudden change in image brightness. This algorithm takes the differential of the corner score into account with reference to direction directly (wikipedia). Function cornerSubPix() refines the corner location - it iterates

I have an image of the box. I am trying to detect corners and mark those corner from circles. I am using the following code for this: import cv2 import numpy as np img_file = 'Image.jpg' img = cv2.imread(img_file, cv2.IMREAD_COLOR) imgDim = img.shape dimA = imgDim[0] dimB = imgDim[1] # RGB to Gray scale conversion img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) # Noise removal with iterative bilateral filter(removes noise while preserving edges) noise_removal = cv2.bilateralFilter(img_gray,9,75,75) # Thresholding the image ret,thresh_image = cv2.threshold(noise_removal,220,255,cv2.THRESH_OTSU)

I am trying to implement difference of guassians (DoG), for a specific case of edge detection. As the name of the algorithm suggests, it is actually fairly straightforward: Mat g1, g2, result; Mat img = imread("test.png", CV_LOAD_IMAGE_COLOR); GaussianBlur(img, g1, Size(1,1), 0); GaussianBlur(img, g2, Size(3,3), 0); result = g1 - g2; However, I have the feeling that this can be done more efficiently. Can it perhaps be done in less passes over the data? The question here has taught me about separable filters, but I'm too much of an image processing newbie to understand how to apply them in this