Example Operator Networks

To illustrate the tableau capabilities and also show some standard image processing sequences, we provide some example networks:

1. Adaptive thresholding example in file athdemo.hjv: This demo compares the adaptive thresholding operator with an attempt to normalize by the illumination field and then threshold. Which is more successful? Does changing the amount of smoothing or type of smoothing operator have an effect?
2. Change detection example in file chgdemo.hjv: This example shows change detection when the illumination and scene are largely constant. What is the distribution of pixel difference values?
3. Region segmentation and labeling example in file clsdemo.hjv: Compares the classifacation operator with explicit selective thresholding and merging. Could you also isolate the stripe on the pen as a separate class?
4. Edge detector comparison example in file edgdemo.hjv: Compares the region boundary from the Roberts Cross and Canny edge detectors with the boundary tracked around the region found by thresholding. Can you choose a threshold that will allow all of the object region to be isolated?
5. Intensity manipulation example in file eqdemo.hjv: This example compares the effects of histogram equalization and contrast normalization. Look at the histograms to understand the differences. Which approach is more effective here and why?
6. Another intensity rescaling example in file expdemo.hjv: This example shows effects of the logarithmic, exponential and raise to a power operators. Considerable rescaling was needed to get images where the white regions had similar intensities. Look at their histograms to understand why the result images are very different.
7. FFT example in file fftdemo.hjv: This illustrates how to extract the regular vertical stripes in the image background.
8. Noise removal example in file fltdemo.hjv: Showing the effect of several different filtering algorithms on an image corrupted with speckle noise. Which is better for this particular image and noise? What effect does changing the kernel shape have? Would you get better results with applying median smoothing after conservative smoothing?
9. Geometric transformation example in file geodemo.hjv: This examples shows how an affine transformation can be use to implement a rotation, translation, scale and reflection. The image dimensions and clipping are a consequence of the implementation of the individual operators, and are not fundamental to the operation of the operators. Also, the individual rotation operator rotates about the center of the image, whereas the affine operator rotates about the image origin, which is at the upper left. This means that a different translation value is needed to align the results, but this is not a difference in principle.
10. Hough transform example in file hghdemo.hjv: Overlaying the detected lines over the original image, also using boundary detection on a binary image. What happens if you change the Hough detection parameter to get the missing lines?
11. Hit and Miss operator example in file htmdemo.hjv: This compares the Hit and Miss operator and an alternative approach using the thinning operator. The Hit and Miss operator output is thickened.
12. Line detection example in file lnddemo.hjv: This compares two different approaches to detecting vertical light lines against a dark background. Is there a difference?
13. Image logic example in file logdemo.hjv: This network shows a number of different logical operators in action.
14. Binary image morphology example in file mordemo.hjv: This example compares the effects of the standard four image morphology operators.
15. Bitshift or rescale example in file shfdemo.hjv: This compares two different approaches to dividing image intensities by 2. Is there a difference?
16. Skeleton example in file skldemo.hjv: Illustrates the relationship between the distance transform, the medial axis transform and the skeleton.
17. Edge thinning example in file thndemo.hjv: Compares the thinning possible from a given kernel on the output of the Sobel and Compass edge detectors. Can you add a new kernel for thinning the horizontal-ish edges?
18. Unsharp filtering example in file unsdemo.hjv: This computes the unsharp filter and also the explicit approach with gaussian smoothing and image arithmetic. A histogram shows a few pixels differ significantly and thresholding shows where these pixels are. What is the cause?
19. Zero crossing example in file zcdemo.hjv: Compares the Laplacian of Gaussian, the Laplacian on a hand-smoothed image, and the zero crossings found on the Laplacian output. How well do the operators compare on more complex images?

When loading these demonstration networks, you do not need to specify a full URL, and instead only enter the associated filename.

©2003 R. Fisher, S. Perkins, A. Walker and E. Wolfart.