I used two pictures in this activity, one from my personal library and one from the internet. However, I used the latter image as my main image in analysis because it is smaller. (Faster! :p)
Sunset at the beach.
The Fray.
Below is the initial conversion to grayscale of the 'fray' image.
Now, a quantitative property of grayscale images are Probability Distribution Functions (PDFs) and Cummulative Distribution Functions (CDFs). The PDF of an image is basically the distribution of the pixels in the image corresponding to the gray levels equal to 0-255. On the other hand, the CDF on an image is the like the cummulative summation of the PDF. Below are the normalized PDF and CDF of the grayscale fray image.
With this information, we can remap the CDF to correspondingly adjust the levels of the image. Below are the transformed CDFs and images, linearly, exponentially and parabolically (coined term? hehe) respectively.
I also did this for the sunset image, from left to right and then top to bottom: the original conversion to grayscale, the linear CDF, the exponential CDF, and the prabolic CDF.
Histogram manipulation can also be done using software available, such as GIMP. Open an image, set its mode to grayscale and then go to Colors > Curves.
I must admit that I did this activity in MATLAB. It's too costly to run a virtual Windows on Mac and to process images at the same time. I was not very much progressive during the time we did this in class, so I finished this at home.
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I'd like to thank Aivin, Che and BA for the helpful discussions. Also to Kuya Jeric and Tisza for their codes.
http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_distribution_function.htm
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Technical Correctness: 5
Quality of Presentation: 4
Initiative: processed other images to see the effects






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