Roberts Edge Detector how to use?

Question

I am trying to use Roberts edge detection to process an image. Do I just apply both of the masks to the image and perform convolution as normal? Could someone give me the breakdown of how to use this edge detection method, as I am trying to program it to process a greyscale image. I convoluted the image using both kernels separately but the image dent' look right.

Thanks.

Answer 1

Robert's Cross is a little tricky because it's not an odd size (2x2 rather than 3x3 or 5x5). I've done it using numpy+scipy using a padded 3x3 convolution mask.

import sys
import numpy as np
from scipy import ndimage
import Image

roberts_cross_v = np.array( [[ 0, 0, 0 ],
                             [ 0, 1, 0 ],
                             [ 0, 0,-1 ]] )

roberts_cross_h = np.array( [[ 0, 0, 0 ],
                             [ 0, 0, 1 ],
                             [ 0,-1, 0 ]] )
def load_image( infilename ) :
    img = Image.open( infilename )
    img.load() 
    # note signed integer
    return np.asarray( img, dtype="int32" )

def save_image( data, outfilename ) :
    img = Image.fromarray( np.asarray( np.clip(data,0,255), dtype="uint8"), "L" )
    img.save( outfilename )

def roberts_cross( infilename, outfilename ) :
    image = load_image( infilename )

    vertical = ndimage.convolve( image, roberts_cross_v )
    horizontal = ndimage.convolve( image, roberts_cross_h )

    output_image = np.sqrt( np.square(horizontal) + np.square(vertical))

    save_image( output_image, outfilename )

infilename = sys.argv[1]
outfilename = sys.argv[2]
roberts_cross( infilename, outfilename )

From the Wikipedia entry on Robert's Cross. http://en.wikipedia.org/wiki/Roberts_Cross

My script's output.