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IMAGE PROCESSING
USING SCILABRajesh B. Raut
Associate Professor, Dept. of E&C
Shri Ramdeobaba K.N. Engg. College, Nagpur
[email protected]
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INSTALLING SIVP TOOLBOX
Toolboxes:
SIP (Scilab Image Processing), SIVP (Scilab
Image & Video Processing)
Tool we discuss: SIVPInstallation in Windows :
(XP sp3 onwards. Windows 7 is recommended)-
SIVP through atoms (5.3 onwards)
atomsInstall SIVP
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INSTALLING SIVP……
After atomsInstall SIVP, do the proxy settings
Usehelp proxycommand to see the proxy
settings
Eg.atomsSetConfig (useProxy, “True/False”)
atomsSetConfig (ProxyHost, “DNS/IP Address”)
atomsSetConfig (ProxyPort, “Port Address”)
Set the user name & password for proxy, if any.
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DIGITAL IMAGE
Image is a 2D matrix
can be:
Gray scale (M x N, M- rows and N-columns)Color image (M x N x
3)
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BASIC FUNCTIONS:
IMREAD/IMSHOW/IMWRITE
imread
output image=imread(‘input image’)
imshowimshow(output image)
imwrite
output=imwrite(input image, ‘output image
name’)
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INFORMATION OF THE IMAGE
imfinfo- Get the information about image file
info =imfinfo(filename)
Eg: info=imfinfo(‘baboon.png’)
returns the information: filename filesize, width,
height bitdepth, etc.
fileinfo- also provides the information about
image file
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SCILAB SUPPORTING THE
DATATYPES
int8
int16
int32
uint8uint16
double
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DATATYPE CONVERSION
im2int8- Convert image to 8-bit signed integers
im2int16
im2int32
im2uint8im2uint16
im2double
u- unsigned
double - double precision
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IMAGE TYPE & ITS CONVERSION IN
SCILAB
rgb2gray
Im2bw
ind2rgb
rgb2hsvhsv2rgb
rgb2ycbcr
ycbcr2rgb
eg.
bwlena=im2bw(‘lena.bmp’, 0.5)
0.5 (threshold) : specify threshold in the range [0,1],
regardless of the class of
the input image.
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RESULTS: COMPLEMENT
Original Complement
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RESULTS: CROP
lenacrop =imcrop(lena [200, 200, 200, 200]);
Original Cropped
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RESULTS: RESIZE
Original Resized by 2
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OTHER MOSTLY USED FUNCTIONS
imadd: Add two images or add a constant to an
image
imsubtract
imdivide Imabsdiff
mean2: Average or mean of matrix elements
std2:Standard deviation of 2D matrix elements
2- 2D matrix elements
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OTHER IMPORTANT FUNCTION
imhist
[counts, cells] =imhist(im)
[counts, cells] =imhist(im, bins)
Counts- the returned histogram.
Cells- the intervals for bins.
Bins- The number of bins of the histogram.
If bins is not specified, default value will be used by the
function
& is determined by the image type:
2 for Boolean, 2̂8 for uint8 and int8, 2̂16 for uint16 and
int16, 2̂16 for int32, and 10 for double.
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COLOR IMAGE HISTOGRAM
manifests an important global statistics of digital
images
Function available for histogram of gray images,
it can be applied directly for color images as a
combination of 3 independent gray images in
terms of R, G and B.
It can not incorporate the correlation between R,
G and B channels.
Solution: table structure (colors and their
population)
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NOISE FUNCTIONS
TypesGaussian- additive noise
Salt & Pepper- black/white noise
Speckle- multiplicative noise
Localvar- Pixel-specific variance (Zero-mean Gaussian)
Function:imnoise
Outputimage=imnoise(inputimage, ‘noisetype)e.g.
lenaNoised=imnoise(lena,’gaussian’)
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IMAGES WITH VARIOUS TYPES OF
NOISE
Original
Salt & pepper Gaussian Speckle
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IMAGE FILTERING USING
‘FSPECIAL’
High Pass filter- used forsobel
prewitt
laplacian
F = fspecial(sobel);
Low Pass filter- used for blurringgaussian
Log
average
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HIGH PASS FILTERING &
THRESHOLDING- EDGE DETECTION
Kernel used-Sobel
Prewitt
Log
canny
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TOPICS TO BE EXPLORED
FFT
Wavelets
Radon Transform
Hough Transform
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TO CONCLUDE….
Scilab/SIVP is a very powerful numerical
computational tool, it also has number of ready-
to-use functions for processing an image/2D
matrix elements and hence Image Processing
operation can be performed with equal ease onScilab.
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for any help on IP using SIVP, pl. feel free to contact:
[email protected]
