Post on 29-Jun-2015
description
transcript
BY
ABUBAKAR SADIQ MUHAMMAD
MEVLANA UNIVERSITY KONYA,TURKEY
Email: alsiddiq_uhd@yahoo.com
Introduction
Colour space & models
RGB image edge detection
YUV image edge detection
Analysis of Results
Conclusions
INTRODUCTION Edge detection is an
important aspect of image processing whose goal is extracting edges in an image.
It is often the first step in image segmentation
A lot of researches
have proposed many arithmetic on RGB(colour) image edge detection
However a number of shortcomings were
noted that include
-low speed of processing of each image
-colour losses after processing of each
image
This paper proposes a method of image
edge detection using YUV colour space
and histogram equalisation
The RGB colour space is the simplest colour space that comprises the primary colours Red, Green and Blue
This colour space explores a wide range of colour
when mixed together among which are
YELLOW(RG)
MAGENTA(RB)
CYAN(GB)
The drawback of this model is that it does not suit the intuition(insight) of human psychology
Human intuition bring with it the aspect of lightness of the colour and the amount we use to colour a specific region
Why?
Also the distance
between colour points is
not equal to the vision
characteristics
Thus, it is not easy to obtain the Hue, Saturation and
Brightness attribute in an RGB image
YUV model The YUV model defines a
colour space in terms of one luma and two chrominance (U,V) components. The basic characteristics of these model is that each component is independent of the other
RGB
Y
U
V
Luminance(lightness) : is a measurement of the eye’s
perception of light intensity (brightness).
Luma: is the component of a digital image that carries
a monochrome portion that determines image
lightness- it is often defined as gamma corrected
luminance
Chrominance: stands for the colour components
obtained by deducting the luminance value Y from R
and B
The fact that human visual systems is more sensitive to
difference in lightness than in colour makes the model
application in video standard.
Application
- Used in PAL, SECAM and composite colour video
standards
The colour difference U and V in YUV
colour space are given by the equation
U= 0.493(B -Y)
V=0.877( R –Y)
So that the conversion between RGB and
YUV is as given by the equation
0.299 0.587 0.114
0.14713 0.28886 0.436
0.615 0.51499 0.10001
Y R
U G
V B
R
G
B
1 0.000 1.1400
1 -0.369 -0.581
1 2.029 0.000
Y
U
V
=
The edge detection method employs the
use of three filters
Gradient filter
Laplacian filter
Laplacian with control parameter(α)
each RGB component is processed
independently using the respective filter
Analysing the RGB image Each R,G and B component is computed Each component is separately processed using
the horizontal and vertical sobel operator Each component is separately processed using
the Laplacian operator and Laplacian with control parameter(α)
The resulting RGB image is obtained from the
separately processed components for each operator
The resulting RGB image obtained from
the separately processed components for
each operator is then histogram
equalised.
The basic idea is to process the Y component in the YUV image
The YUV is obtained from the RGB image Each component Y, U & V are computed
from the YUV image The Y component is processed using the
respective filters listed earlier The Y component is then histogram
equalised and the corresponding RGB is obtained
Obtain YUV image from
corresponding RGB
Compute each component
of YUV image
Process the Y component
using respective filters
Obtain the corresponding
RGB image of the HISTEQ
YUV image
Obtain the histogram
equalisation of the Y
component
Flow chart representation of YUV image processing
ORIGINAL RGB IMAGE & YUV IMAGE
Horizontal & Vertical sobel on each RGB component
H & Vertical sobel on Y component of YUV image
Observe that single component in YUV produces equivalent of 3 components in RGB
Laplacian / Laplacian(α) in RGB
Laplacian / Laplacian(α) in YUV
Observe that a single component in YUV produces effect of 3 RGB component
Resulting processed images in RGB
Resulting processed Y components images in YUV
Observe that in RGB images losses its colour when compared to YUV after processing
Histogram EQU on RGB images
Histogram EQU on YUV images
Observe that the colour edges are still visible in YUV as compared to RGB with HISTEQ applied
Corresponding HISTEQ YUV in RGB
It can be observed that the detected
edges are more exact based on the
proposed algorithm
The processing is faster and simple using a single component(Y-YUV) as compared to 3 –RGB components
The colour edges are also detected
effectively when compared to the former
Thanks for
listening