edge-detection

I have been working with OpenCV for a fairly short time, and have performed Canny Edge Detection on an image, and also performed dilation after that to further separate the object (in my case a square) from the background. My problem, now is to identify graspable regions in 2D using an algorithm that requires me to handle co-ordinates of the points in those edges. Is there any way I can use OpenCV to get the co-ordinates of the corners so I can find the equation of the lines forming the edge of the square? I know the size of the square. My problem involves 2D co-ordinate geometry, and hence

I have an image and I want to create an edge histogram. I divide the image into 1100 image-blocks and try to find edge and its direction (horisontal, vertical, 45° diagonal, 135° diagonal or nondirectional) in each block. How can I extract that information about edges? Do you have any ideas? Regards! user101375 I found the answer in this paper: Efficient Use of MPEG-7 Edge Histogram Descriptor by Won. My goal was to find following edges: Won divide each Image Block into 4 parts, calculate the average gray level in each of them and use the following coefficients: We use this coefficients as

I'm using OpenCV (Canny + findCountours) to find external contours of objects. The curve drawn is typically almost, but not entirely, closed. I'd like to close it - to find the region it bounds. How do I do this? Things considered: Dilation - the examples I've seen show this after Canny, although it would seem to me it makes more sense to do this after findContours Convex hull - might work, though I'm really trying to complete a curve Shape simplification - related, but not exactly what I want Using PolyLine method to draw contours cv2.PolyLine(img, points, is_closed=True, 255, thickness=1,

I am trying to segment cars from image that consist of only one car and an easy background like but what I get from my implementation is this and respectively but it works very easily on almost already segmented images like. giving results like The Code I am using is import cv2 import numpy as np THRESH_TYPE=cv2.THRESH_BINARY_INV def show(name,obj): cv2.imshow(name,obj) cv2.moveWindow(name, 100, 100) cv2.waitKey(0) cv2.destroyAllWindows() def process_end(new): drawing = np.zeros(o.shape,np.uint8) # Image to draw the contours contours,hierarchy =cv2.findContours(new,cv2.RETR_TREE,cv2.CHAIN

For my college project I need to identify a species of a plant from plant leaf shape by detecting edges of a leaf. (I use OpenCV 2.4.9 and C++), but the source image has taken in the real environment of the plant and has more than one leaf. See the below example image. So here I need to extract the edge pattern of just one leaf to process further. Using Canny Edge Detector I can identify edges of the whole image. But I don't know how to proceed from here to extract edge pattern of just one leaf, may be more clear and complete leaf. I don't know even if this is possible also. Can anyone please

I am trying to find contour of a image, before that I am applying Canny's edge detector. It's giving different result for different images.For one image it's giving perfect contours at threshold value - min-40 max-240 and for other image its 30-120. I want to make it generic. In laymen terms, edge detection needs a threshold to tell what difference/change should be counted as edge. For details read here . So, the edges depend on the the content of image ie the level of brightness/darkness/contrast. I suggest you to simply find the mean of whole gray image and take threshold as follows: min

I have image of Tiger's Pugmark (footprint impression) in mud. I want to detect the boundary of the pugmark but the image is uniform in intensity that is foreground and background cannot be distinguished based on intensity variations. What can i do to distinguish between the pugmark and the background.! In segmentation tasks if you have both Good markers; and Strong edges around the object of interest then it is directly solved by a Watershed Transform. The problem, of course, is obtaining these markers, as well enhancing relevant edges as needed. Obtaining these might involve problem-specific