Homework #1:
(due: Oct 9, 2001)
1. Use B_PIX to write a program to generate
1. upside-down lena.im
2. right-side-left lena.im
3. diagonally mirrored lena.im
2. Use tk to
1. rotate lena.im 45 degrees clockwise
2. shrink lena.im in half
3. binarize lena.im at 128 to get a binary image (hint: binarize)
Homework #2:
(due: Oct 16, 2001)
Write a program to generate
• a. a binary image (threshold at 128)
• b. a histogram
• c. connected components (regions with + at centroid, bounding box)
Homework #3:
(due: Oct. 23, 2001)
Write a program to do histogram equalization
Reference Equation:

Homework #4:
(due: Nov 6, 2001)
Write programs which do binary morphological dilation, erosion, opening, closing, and hit-and-miss transform on a binary image.
(ps. you can use octagon as the kernel (structuring elements) for the first four operations. And the last operation (hit-and-miss), you should find proper elements to detect the right-upper corner.

Homework #5:
(due: Nov 13, 2001)
Write programs which do gray scale morphological dilation, erosion, opening, and closing on a gray scale image.

Homework #6:
(due: Nov 27, 2001) Write a program to generate Yokoi connectivity number

Homework #7:
(due: Dec 4, 2001) Write a program to generate thinned image

Homework #8:
(due: Dec 11, 2001) Write the following programs:

1. Generate additive white Gaussian noise
2. Generate salt-and-peeper noise
3. Run box filter on all noisy images
4. Run median filter on all noisy images
5. Run opening followed by closing or closing followed by opening

Homework #9:
(due: Dec 18, 2001) Write programs to generate the following gradient magnitude images and choose proper thresholds to get the binary edge images:

1. Roberts operator
2. Prewitt edge detector
3. Sobel edge detector
4. Frei and Chen gradient operator
5. Kirsch compass operator
6. Nevatia-Babu 5x5 operator

Homework #10:
(due: Jan ??,2002) Project due Dec. 21
Write the following programs to detect edge:
1. zero-crossing on the following four types of images(methods) to get edge images (choose proper thresholds), p. 349
2.1. Laplacian, Fig. 7.33
3.2. minimum-variance Laplacian, Fig. 7.36
4.3. Laplacian of Gaussian, Fig. 7.37
5.4. Difference of Gaussian, (use tk to generate D.O.G.)
D. Marr, Vision, W.H. Freeman, San Francisco, p.54-74, 1982.
=====Marr, Vision, Fig. 2.9=====
=====Marr, Vision, Fig. 2.16=====
dog (inhibitory 64#64, excitatory 65#65, kernel size=11)

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