Computational Neuroscience 2001b 0368.3013.01
Frequently Asked Questions exercise 2.

1.  Where can I find the article:  "A computational theory of human stereo vision" by D.Marr & T. Paggio ? is it available online ?
A. At the library. I don't know of an online copy of it.

2. I think there is something wrong with the second link in Hezy's on-line papers page (http://www.math.tau.ac.il/~hezy/acoustic.pdf).
A. Try the mirror (from http://www.math.tau.ac.il/~odedsc/cns01b/cns01b.html).

3. Is there another source for David Marr's algorithm ?
A. The actual algorithm could be retrieved and understood from David Marr's book "Vision"

4.  What should our matlab program do - just create the disparity map or perform edge detection on the input as well?
A. You should test the algorithms both with and without performing the edge detection.

5.  What exactly is the DOG filter?
A. Difference of Gaussian ("The mexican hat").

6.  What is the maximum size of the input images? Can we assume that it is 1000x1000 (maximum)?
A. Yes.

7.  We based our 1st part on the "Cepstral Filtering" article. The authors admit that the performance of the presented algorithm on natural images is much worse than on RDS (as we have seen when running our implementation). Would this fact be taken into consideration?
A. This is indeed on of the parameters you should consider and discuss when comparing the algorithms.

8. What is a disparity map?
A. A disparity map is a matrix containing the disparity for each pixel of the original images.

9. May we choose implement an algorithm which contains features that weren't studied in class ?
A. Of course, but make sure you have a good understanding of it.

10. We were wondering if we can assume the following regarding the input pictures in the first question (of the second project):
a. Pictures have been taken from the same height, that is, all disparities will only have an X element (and no "diagonal" disparities).
b. The Axes of the two pictures will be parallel, that is, the X axis of the first image is parallel to the X axis of the second image, and the same goes for the Y axis.
A. Yes for both.

11. Regarding the first question of the project: will the project be tested with files we provide or on other files?
A. On other files. But you are encouraged to add some running results on the document, and add the input pictures to the projects directory.

12. Is  the resolution of the disparity map to be the same as the original pictures ?
A. Not necessarily.

13. Can you give us some links for searching journals ?
A. You have a few at : http://www.wisdom.weizmann.ac.il/~yoad/

14. Should we attach a copy of the 2nd paper we used (not one of the two mentioned in the definition) ?
A. Please do.

15. Can we invent an algorithm of ourselves instead of using a 2nd paper ?
A. Yes, as long as it is a good one.

16. Is there an example for the 2nd part (auditory) ?
A. Yes: here.

17. What format should we use to save the disparity matrix ?
A. Bitmap (.bmp).

18. I found an article that applies a method to calculate a disparity map,
but the explanation of it's relevancy to the human vision is somewhat
shady and not elaborated enough.  Could I add my own justifications for the validity of such a model ?
A. Yes. If you think it is not relevant (at least in some aspects) you may note that too.

19. What type of file should the project work on?  Is it gray scale with color map of length 256, starting from [0 0 0] and ending with [1 1 1] in a linear way?
A. The input is a gray scale bitmap file. You should retrieve the color map or any other detail from there.

20.  In the link you gave us for stereo pictures, the disparity gets larger as the object gets further.  It is inconsistent with the eyes as far as I understand.  Are these "valid" pictures?
A. Put a finger about 20 cm in front of you, concentrate on it, an close you left eye and then and your right eye. Note what happens to the background. Now do the same when you fixate on the background instead of fixating on your finger. What is the different ? For further details, look for 'disparity' in Marr's book.

21. Hezy states in his article that a 16000 x 16000 pixel (conventional constant resolution) must be used to achieve the desired results.  You said the maximum size will be 1000 x 1000.  How does the 2 combine?
A. Your program will be tested using a maximum 1000 x 1000 bitmaps. However you may use any pictures to check it yourself, and add the results to your report.

22. In ex 1 we are supposed to produce gif files, but (and I quote): MATLAB does not produce GIF files (due to patent
restrictions) what format should we use?
A. The output file should be BITMAP (.bmp) and NOT (as written in the project's description) gif.

23. We are supposed to produce one output file disparity.bmp. What about other images (during the process; with various parameters; with/without edge detection)?
A. You may include any image you want to discuss or demonstrate in the documentation. However the only output required for the run test is the disparity map.

24. Concerning the cepstral filtering paper: What should be the assumptions about the input resolution? How far are the images? In the paper the writers assumed that the images are in a constant distance, and 6-12 minutes correspond to about 20 pixels. But that requires large input images to produce good results (larger than 1000X1000).
A. You cannot assume the distance of the image - note that stereo vision works in various distances. However you may assume the angle per pixels assumed in the article.

25. Does it matter in what color map we save the image?
A. Please use the 'gray' colormap (choose the parameter to show best your expected results).

26. DoG filter - different kinds of pictures "need" slightly different parameters of the gaussian to give nice edges. Is it possible that the edge detection will be checked after trying a few different parameters ?
A. This may be right. But you can do so only if you think this way is a good model.

27. If I add examples of running tests to the documentation - will these images count in deducing points from the grade?.
A. You are encouraged to add relevant pictures of your result, but within the page limits.

28. If we use an algorithm that outputs both positive and negative disparities; Should we omit the sign (use the same color for +a and -a ) or shift the disparity so we don't have negative disparities ?
A. Shift it.

29. You wrote the output should be in a BMP format . The input files , as far as I understand - stay 'gif'?
A. No. It is BMP as well.

30.  May we assume we are not suppose to recognize disparity larger than 10 pixels?
A. Yes.

31. ( On "Columnar Cepstral..."): When do they use a 1 dimensional Fourier transform and when a 2 dimensional one?
A. The examples (images) imply a 2D FFT, but the appendix use 1D. The algorithm itself calls for
a 2D FFT, since it is performed on an image. If only an horizontal shift is expected (or allowed), it will mean that the peak should be looked for only on the X axis of the cepstrum - but it is a 2D FFT in any case. The reason a 1D FFT have been used in the appendix is just for the sake of the simplicity of
the explanation,

32. Regarding the CNS project, should we scale the disparity map to produce a more comprehensible picture or should the values in the disparity map represent the actual disparity values (and thus be values between 1 and 10 which look all black to dark gray)?
A. The output bitmap file should be visually checkable; that is  - the colormap you choose should make relevant disparity visible.

33. I have some question about a matlab function. Who may I ask ?
A. You may ask the system for the phone of the Matlab advisor.

34. Are we suppose to output the DoG as well ?
A. No. But you are to implement it as written in the project description. When using edge detector in the process of finding the disparity map you are to use the DoG you created, and NOT any ready made edge detector.