Contents

1. Description
2. Instructions to use the applet
3. Toolbar in the image display
4. Choose your own image for the applet
5. Block scheme of the algorithm

This demonstration compares the performances between a classical Canny edge detector and one based on an interpolation algorithm. By interpolating the image, we are able to compute directly the gradient without numerical approximated operator. We also can extend the size of the image, to increase the visibility of little details. The interpolation is performed with cubic splines, which have the advantage to have a finite impulse response, wich makes the implementation very convenient and efficient.

The cubic spline interpolation provides a good reconstruction, and, with a recursive implementation made in the spatial domain, we can have a relative fastness of response. So, by this method, we are able to obtain a really higher degree of precision with an acceptable time of computation.

Note: Even the parts of the algorithms which are identical for both detectors were differently implemented. This implies that some of the differences can be only due to those programming differences.

1. Choose an input image in the list.
2. Choose an extension rate. The red rectangle on the input image (left) shows the part of the image on which the edges will be detected.
3. By clicking on the original image, choose the part of the image on which you want to detect the edges.
4. Choose a sigma to smooth the image.
5. Choose two values for the hysteresis thresholding step.
6. With the "Step" button, you can see the different steps of both algorithms.
   With the "Run all steps" button, you obtain directly the final binary images with edges.

	Get the coordinates and value of a pixel.
	Get the maximum, minimum and the mean value of the image.
	Open a new window containing the image.
	Zoom out by a factor 2. (N/A on Netscape)
	Zoom in by a factor 2. (N/A on Netscape)
	Move the zoomed part of the image. (N/A on Netscape)