edge-detection

I'm trying to apply the Sobel filter on an image to detect edges using scipy. I'm using Python 3.2 (64 bit) and scipy 0.9.0 on Windows 7 Ultimate (64 bit). Currently my code is as follows: import scipy from scipy import ndimage im = scipy.misc.imread('bike.jpg') processed = ndimage.sobel(im, 0) scipy.misc.imsave('sobel.jpg', processed) I don't know what I'm doing wrong, but the processed image does not look anything like what it should. The image, 'bike.jpg' is a greyscale (mode 'L' not 'RGB') image so each pixel has only one value associated with it. Unfortunately I can't post the images here

In openCV after applying canny edge detection I'd like to further process the result (show only horizontal lines, remove short lines, etc..). But the result of canny is just another image. I'd like to get an array of lines describing the detected edges I'm aware of the famous Hough Line Transform , but the result is not always good, that's why I'd like to manually process canny result. input: output canny only: output canny then Hough line transform This is Hough line transform result(red lines) for detecting edges of stairs. 4th line from below is not detected correctly, although canny edge

As part of an application that I'm developing for Android I'd like to show the user an edge-detected version of an image they have taken (something similar to the example below). To achieve this I've been looking at the Sobel operator and how to implement it in Java. However, many of the examples that I've found make use of objects and methods found in AWT ( like this example ) that isn't part of Android. My question is then really, does Android provide any alternatives to the features of AWT that have been used in the above example? If we were to rewrite that example just using the libraries

I'm using graphviz (dot) to generate the graph you can see below. The node in the lower left corner (red ellipse) causes annoyance as its edges cross several edges of the adjacent node. Is there a way to restrain node placement to a certain area? I'm not aware of any means by which to control the placement of individual nodes. It doesn't really make sense, because in order to do that you'd need to know what the final graph will look like, yet placing one node manually would then change how the rest of the graph is rendered. I solved this problem by changing the order in which my nodes are

I would like to capture the number from this kind of picture. I tried multi-scale matching from the following link. http://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/ All I want to know is the red number. But the problem is, the red number is blurry for openCV recognize/match template. Would there be other possible way to detect this red number on the black background? Classifying Digits You clarified in comments that you've already isolated the number part of the image pre-detection, so I'll start under that assumption. Perhaps you can approximate the