Activity 3 – Image types and basic image enhancement

These are the information of the images above given by the command imfinfo in scilab.

Binary Image	Grayscale Image
FileName: binary.png	FileName: grayscale.png
FileSize: 2660	FileSize: 90505
Format: PNG	Format: PNG
Width: 200	Width: 420
Height: 140	Height: 320
Depth: 8	Depth: 8
StorageType: indexed	StorageType: indexed
NumberOfColors: 2	NumberOfColors: 256
ResolutionUnit: centimeter	ResolutionUnit: centimeter
XResolution: 72.000000	XResolution: 72.000000
YResolution: 72.000000	YResolution: 72.000000

Indexed Image	Truecolor Image
FileName: indexed.png	FileName: truecolor.jpg
FileSize: 25576	FileSize: 4812
Format: PNG	Format: JPEG
Width: 150	Width: 104
Height: 200	Height: 145
Depth: 8	Depth: 8
StorageType: indexed	StorageType: truecolor
NumberOfColors: 256	NumberOfColors: 0
ResolutionUnit: centimeter	ResolutionUnit: centimeter
XResolution: 72.000000	XResolution: 72.000000
YResolution: 72.000000	YResolution: 72.000000

FileSize: 46050

Format: JPEG

Width: 179

Height: 274

Depth: 8

StorageType: indexed

NumberOfColors: 256

ResolutionUnit: inch

XResolution: 96.000000

YResolution: 96.000000

The histogram of my scanned image is shown below. It can be seen that the histogram is quite spread meaning that the image has a good contrast. Also, we see that the image is well separated from the background because we could see a valley. Thank you for Jeric Pineda and Eduardo David for the help with the scilab code.

The scanned image is converted to a binary image by the command im2bw in scilab. We used the program we made in the previous activity and calculate the area of our scanned image. I also used paint to estimate the area of my scanned image.

Using the program made during the previous activity that uses Green’s theorem we obtained an area of 40376 square pixels. Using paint I estimated the area to be 40320 square pixels. Since my image is almost a perfect rectangle I just multiplied the length (252 pixels) by the width (160 pixels) to approximate the area of the scanned image. We see that the calculated area’s are almost the same which means that our method for computing the area using Green’s theorem is applicable.

I rate myself 10 out of 10 since I have successfully plotted the histogram of my scanned grayscale image and I have shown that the calculated area using Green’s theorem is close, if not equal, to the actual pixel area of the scanned image.

Colaborators: Eduardo David, Jeric Pineda, Rafael Jaculbia\

Sources of Image:

binary:http://en.wikipedia.org/wiki/Binary_image

grayscale:http://weblogs.java.net/blog/kirillcool/archive/2007/04/non_photorealis_1.html

indexed:http://en.wikipedia.org/wiki/Indexed_color