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Problem Set 0: Images as Functions

Question 1 - Input Images

A

Two interesting images:

Image 1	Image 2

Question 2 - Colour Planes

A

Swap the red and blue pixels of image 1:

B

Create a monochrome image (img1_green) by selecting the green channel of image 1:

img1_green

C

Create a monochrome image (img1_red) by selecting the red channel of image 1:

img1_red

D

Which looks more like what you’d expect a monochrome image to look like? Would you expect a computer vision algorithm to work on one better than the other?

img1_green looks more like what I would expect to see in a monochrome image. There are no areas that lack detail, this is in contrast to img1_red where there is no/little detail in the sea (it appears uniform black). Therefore I would expect CV algorithms to work better on img1_green.

Question 3 - Replacement of Pixels

A

Take the inner center square region of 100x100 pixels of monochrome version of image 1 and insert them into the center of monochrome version of image 2:

Question 4 - Arithmetic and Geometric operations

A

What is the min and max of the pixel values of img1_green? What is the mean? What is the standard deviation?  And how did you compute these?

Results:

• img1_green max pixel value: 255
• img1_green min pixel value: 0
• img1_green mean pixel value: 141.94
• img1_green std dev: 51.16

Results were calulcated with the numpy library and the following functions:

• image_1_g_max = np.max(image_1_g)
• image_1_g_min = np.min(image_1_g)
• image_1_g_mean = np.mean(image_1_g)
• image_1_g_std = np.std(image_1_g)

B

Subtract the mean from all pixels, then divide by standard deviation, then multiply by 10 (if your image is 0 to 255) or by 0.05 (if your image ranges from 0.0 to 1.0). Now add the mean back in:

C

Shift img1_green to the left by 2 pixels:

D

Subtract the shifted version of img1_green from the original, and save the difference image.:

What do negative pixel values mean anyways?

Negative values indicate the rate of change of img1_green in a certain direction. The image is the derivative of img1_green in the x direction.

Question 5 - Noise

A

Take the original colored image (image 1) and start adding Gaussian noise to the pixels in the green channel. Increase sigma until the noise is somewhat visible:

Sigma used: 3 (image_1_noise_g = gaussian_filter(image_1_noise_g, 3))

B

Now, instead add that amount of noise to the blue channel:

C

Which looks better? Why?

According to this Stack Overflow answer:

most scene illumination (sunlight, incandescent) is deficient in blue light relative to green and red.