Transcript Image & Video Processing using Scilab

IMAGE PROCESSING
USING SCILAB
Rajesh B. Raut
Associate Professor, Dept. of E&C
Shri Ramdeobaba K.N. Engg. College, Nagpur
[email protected]
INSTALLING SIVP TOOLBOX
Toolboxes:
SIP (Scilab Image Processing), SIVP (Scilab
Image & Video Processing)
 Tool we discuss: SIVP
 Installation in Windows :
(XP sp3 onwards. Windows 7 is recommended)SIVP through atoms (5.3 onwards)
atomsInstall SIVP

INSTALLING SIVP……
After atomsInstall SIVP, do the proxy settings
 Use help proxy command 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.
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)

BASIC FUNCTIONS:
IMREAD/IMSHOW/IMWRITE



imread
output image= imread(‘input image’)
imshow
imshow(output image)
imwrite
output=imwrite(input image, ‘output image
name’)
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
SCILAB SUPPORTING THE DATATYPES
int8
 int16
 int32
 uint8
 uint16
 double

DATATYPE CONVERSION
im2int8- Convert image to 8-bit signed integers
 im2int16
 im2int32
 im2uint8
 im2uint16
 im2double

u- unsigned
double - double precision
IMAGE TYPE & ITS CONVERSION IN SCILAB
rgb2gray
 Im2bw
 ind2rgb
 rgb2hsv
 hsv2rgb
 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.
BASIC OPERATIONS
Complement
outputimage = imcomplement(inputimage)
 Resize
outputimage = imresize(inputimage, multi.
factor, ‘resizing algo’)
resizing algo. can be ’nearest’, ’bilinear’,
’bicubic’ or ’area’
 Crop
outputimage=imcrop(inputimage, [Xcoord Ycoord
SizeX SizeY])

RESULTS: COMPLEMENT
Original
Complement
RESULTS: CROP
lenacrop = imcrop(lena [200, 200, 200, 200]);
Original
Cropped
RESULTS: RESIZE
Original
Resized by 2
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
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, 2^8 for uint8 and int8, 2^16 for uint16 and
int16, 2^16 for int32, and 10 for double.
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)

NOISE FUNCTIONS

Types
Gaussian- 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’)
IMAGES WITH VARIOUS TYPES OF NOISE
Original
Salt & pepper
Gaussian
Speckle
IMAGE FILTERING USING ‘FSPECIAL’

High Pass filter- used for
sobel
 prewitt
 laplacian

F = fspecial(sobel);

Low Pass filter- used for blurring
gaussian
 Log
 average

HIGH PASS FILTERING & THRESHOLDINGEDGE DETECTION

Kernel usedSobel
 Prewitt
 Log
 canny

TOPICS TO BE EXPLORED
FFT
 Wavelets
 Radon Transform
 Hough Transform

TO CONCLUDE….

Scilab/SIVP is a very powerful numerical
computational tool, it also has number of readyto-use functions for processing an image/2D
matrix elements and hence Image Processing
operation can be performed with equal ease on
Scilab.
for any help on IP using SIVP, pl. feel free to contact:
[email protected]