A3 - Image Types and Basic Image Enhancement

PART 1
There are 4 types of images
1. True Color Image

photo from: www.cssnz.org/flower.jpg

Using the command in Scilab
--> imfinfo ('flower1.jpg')
shows the following results

File Name	flower1.jpg
File Size	3348
File Format	jpeg
Width	115
Height	118
Depth	8
Storage Type	truecolor
Number of Colors	0
Resolution Unit	centimeter
X Resolution	0
Y Resolution	0

Using the properties in Windows

Width	115
Height	118
Horizontal Resolution	96 dpi
Vertical Resolution	96 dpi
Bit Depth	24

2. Gray Scale imagephoto from: digital-photography-school.com

Using the command in Scilab
--> imfinfo ('flower2.jpg')
shows the following results

File Name	flower2.jpg
File Size	4225
File Format	JPEG
Width	120
Height	120
Depth	8
Storage Type	indexed
Number of Colors	256
Resolution Unit	inch
X Resolution	76
Y Resolution	76

Using the properties in Windows

Width	120
Height	120
Horizontal Resolution	72 dpi
Vertical Resolution	72 dpi
Bit Depth	8

3.Indexed Imagephoto from:www.digitalphotoartistry.com/rose1.jpg

Using the command in Scilab
--> imfinfo ('flower3.bmp')
shows the following results

File Name	flower3.bmp
File Size	16306
File Format	bmp
Width	106
Height	141
Depth	8
Storage Type	indexed
Number of Colors	256
Resolution Unit	centimeter
X Resolution	28.350000
Y Resolution	28.350000

Using the properties in Windows

Width	106
Height	141
Bit Depth	8

4. Binary Imagephoto from:www.botany.com

Using the command in Scilab
--> imfinfo ('flower4.bmp')
shows the following results

File Name	flower4.bmp
File Size	1550
File Format	bmp
Width	124
Height	93
Depth	8
Storage Type	indexed
Number of Colors	2
Resolution Unit	centimeter
X Resolution	28.350000
Y Resolution	28.350000

Using the properties in Windows

Width	124
Height	93
Bit Depth	1

PART 2:
This part of the activity is concerned with our scanned image.
Cambodian money( c/o Anthony Amarra)
I cropped the image so that the rulers will not be included in reading the area of the scanned image.

Using
-->image = imread('gray2.jpg');
-->imfinfo('gray2.jpg')
shows the following properties
FileName: gray2.jpg

FileSize: 20680
Format: JPEG
Width: 422
Height: 201
Depth: 8
StorageType: indexed
NumberOfColors: 256
ResolutionUnit: inch
XResolution: 96.000000
YResolution: 96.000000

Using the command
-->histplot ([0:1:255], image)
We find the histogram of the image
From the histogram we can say that the image is low in contrast.
We can also check the histogram and threshold by using imageJ.
We can also find the histogram using the following commands:
-->gsval=[]
-->pixelnum=[]
-->counter=1;
-->for i=0:1:255
-->[x,y]=find(image==i);
-->gsval(counter)=i;
-->pixelnum(counter)=length(x);
-->counter = counter+1; -->end;
-->plot(gsval,pixelnum);
This will show the image below:

Using the histogram, i find the best threshold is at 189 or 74.117647% of 255. I used this threshold to convert the image to binary
-->bw = im2bw(image,0.74117647);
--> imshow(bw,2)
It shows the image below
Inverting the image using the commands,
--> binary = abs(bw-1); --> imshow (binary)
shows the image below:

Using the commands, in the previous activity, to find the area
-->[x,y] = follow(binary);
-->size(x)
ans =
924. 1.
-->size(y)
ans =
924. 1.
-->x_2(1) = x(924) ;
-->x_2(2:924) = x(1:923);
-->y_2(1) = y(924) ;
-->y_2(2:924) = y(1:923);
-->area=0.5*sum(x.*y_2-y.*x_2);
--> Area = abs(area)
Area =
84200.

The experimental value for area is 84200 square pixels.
The theoretical value for area is ( 419 x 199) 83381 square pixels. This can be solve by pixel counting
The percent error is 0.9822%.

Acknowledgments:
JULIE D.
ED
BETH
AIYIN
- for answering some of my questions

JORGE
- for the histplot
JERIC
- for the histogram code

GRADE:
10/10 because I did my best in this activity and the error is acceptable.