International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
Identifying the Defects in Glass Bottles Using Particle Swarm Optimization
1
Mrs. Anupama B S1, Mr. Prasanna K B2
Assistant Professor, Information Science and Engineering, Channabasaveshwara Institute of Technology, Tumkur, India,
2
Assistant Professor, Computer Science and Engineering, Channabasaveshwara Institute of Technology, Tumkur, India,
Abstract— This paper aims at designing and developing a
suitable tool for identifying defects in glass bottles through
visual inspection based on segmentation algorithm. Defects
are identified in three stages namely Image acquisition,
Pre-processing and filtering and Segmentation. In the
Image acquisition stage, samples of real time images are
taken and are converted into monochrome images. In the
Pre-processing and filtering stage, the image acquired is
passed through median filters. The de-noised images are
further sent to the third stage which is Segmentation. In
this paper, Segmentation is done using Particle Swarm
Optimization (PSO). The defects in the images are
segmented and highlighted and the areas of defects are
spotted out. The Particle Swarm segmentation has
produced high Sensitivity, high Specificity, high Accuracy
and produced effective results and hence this tool shall be
useful for food processing industries for the Quality
Inspection of the glass bottles.
Keywords--- Defects in glass, Filtering, PSO Algorithm,
and Segmentation.
sources of food, and avoid predators by implementing an
“information sharing” approach[1].
Quality is a very important factor in Glass industries
which has to be considered during the production of glass
bottles. During processing stages, there are possibilities of
occurring cracks or breaks or bubbles or accommodation of
any other external materials such as hair, dust etc. on the glass
surface. Here the main defect under consideration is the
accommodation of external materials on the surface of the
glass bottles.
II. INSPECTION OF GLASS BOTTLES
Image Acquisition
Pre-processing and filtering
I. INTRODUCTION
The PSO (Particle Swarm Optimization) is a
computational method that optimizes a problem by iteratively
trying to improve a candidate solution with regard to a given
measure of quality. Initially, Developing computer software
simulations of birds flocking around food sources, then later
realized how well their algorithms worked on optimization
problems. A basic variant of the PSO algorithm works by
having a population (called a swarm) of candidate solutions
called particles. These particles are moved around in the
search-space according to a few simple formulae. The
movements of the particles are guided by their own best
known position in the search-space as well as the entire
swarm's best known position. When improved positions are
being discovered these will then come to guide the
movements of the swarm. The Term Digital Image Processing
refers to the processing of a two dimensional picture by a
digital Computer.
In PSO, a set of randomly generated solutions (initial
swarm) propagates in the design space towards the optimal
solution over a number of iterations (moves) based on large
amount of information about the design space that is
assimilated and shared by all members of the swarm. PSO is
inspired by the ability of flocks of birds, schools of fish, and
herds of animals to adapt to their environment, find rich
ISSN: 2231-5381
Segmentation using PSO
Segmented Image Evaluation
Figure 1: Inspection of glass bottles using image processing
A. Image Acquisition
The process of capturing image is called as Image
acquisition. Real time images are captured using camera or
sensor. With reference to the figure 1, After the Image
acquisition process through camera/scanners, it is then
subjected to the following stages [2].
B. Pre-processing and Filtering
Pre-processing refers to the initial processing of raw
image. Pre-processing is done to improve the image quality by
removing the undesired distortions referred as noise. Noise is
any undesirable signal. Noise is everywhere and thus we have
to learn to live with it. Noise gets introduced into the data via
any electrical system used for storage, transmission, and/or
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
processing. Filtering is perhaps the most fundamental
operation of image processing and computer vision.
Filtering is done using Progressive switched median
filter (PSMF), Decision based Algorithm (DBA), Modified
Decision Based Binary Algorithm (MBDA) etc.
C. Segmentation
present[] = present[] + v[] ---------(b)
v [] is the particle velocity, present[] is the current particle
(solution). P best [] and g best[] are defined as stated before.
rand () is a random number between (0,1). c1, c2 are learning
factors. Usually c1 = c2 = 2.
Formally, algorithm for the procedure is as follows [3]
The process of cutting adding and feature analysis of
images is called as Image Segmentation. It aims at dividing an
image in to regions that have a strong co-relation with objects
or area of interest using the principal of matrix analysis.
III. PROPOSED METHOD
A. PSO Algorithm
PSO learned from the scenario and used it to solve
the optimization problems. In PSO, each single solution is a
"bird" in the search space. We call it "particle". All of particles
have fitness values which are evaluated by the fitness function
to be optimized, and have velocities which direct the flying of
the particles. The particles fly through the problem space by
following the current optimum particles.
The algorithm keeps track of three global variables:
Target value or condition
Global best (gBest) value indicating which particle's
data is currently closest to the Target
Stopping value indicating when the algorithm should
stop if the Target isn't found
Each particle consists of:
Data representing a possible solution
A Velocity value indicating how much the Data can
be changed
A personal best (pBest) value indicating the closest
the particle's Data has ever come to the Target
B. Image Segmentation using PSO
PSO is initialized with a group of random particles
(solutions) and then searches for optima by updating
generations. In every iteration, each particle is updated by
following two "best" values. The first one is the best solution
(fitness) it has achieved so far. (The fitness value is also
stored.) This value is called pbest.
Another "best" value that is tracked by the particle
swarm optimizer is the best value, obtained so far by any
particle in the population. This best value is a global best and
called gbest. When a particle takes part of the population as its
topological neighbors, the best value is a local best and is
called pbest.
After finding the two best values, the particle updates
its velocity and positions with following equation (a) and (b).
Let S be the number of particles in the swarm, each having a
position xi ∈ ℝn in the search-space and a velocity vi ∈ ℝn.
Let pi be the best known position of particle i and let g be the
best known position of the entire swarm.
For each particle i = 1, ..., S do:
Initialize the particle's position with a uniformly
distributed random vector: xi ~ U(blo, bup), where blo and bup
are the lower and upper boundaries of the search-space.
Initialize the particle's best known position to its
initial position: pi ← xi
If (f(pi) < f(g)) update the swarm's best known
position: g ← pi
Initialize the particle's velocity: vi ~ U(-|bup-blo|, |bupblo|)
Until a termination criterion is met (e.g. number of
iterations performed, or a solution with adequate
objective function value is found), repeat:
For each particle i = 1, ..., S do:
Pick random numbers: rp, rg ~ U(0,1)
For each dimension d = 1, ..., n do:
Update the particle's velocity: vi,d ← ω vi,d + φp rp
(pi,d-xi,d) + φg rg (gd-xi,d)
Update the particle's position: xi ← xi + vi
If (f(xi) < f(pi)) do:
Update the particle's best known position(pbest):
pi ← xi
If (f(pi) < f(g)) update the swarm's best known
position: g ← pi
Now g holds the best found solution.
The parameters ω, φp, and φg are selected by the
practitioner and control the behaviour and efficacy of the
PSO method
v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() *
(gbest[]
present[])
----(a)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 4 – Dec 2014
V. CONCLUSION
The defects in the images are segmented and
highlighted. Thus the areas of defects are spotted out. The
PSO segmentation has produced high Sensitivity, Specificity
and Accuracy. Thus the Proposed method has produced
effective results and hence this tool can be used for food
processing industries for the Quality inspection of the Glass
bottles and thus the productivity can be increased.
REFERENCES
[1] “A Copmarison of Particle Swarm Optimization and the Genetic
Algorithm”, by Rania Hassan, Babak Cohanim, Olivier de Weck
[2] George Mathew, jainaGeorgejaya, Janardhana S, Sabareesan K J
(2013).”Quality Analysis of food products through computer aided
visual inspection: A review” Current Trends in Engineering and
Technology.10.1109/ICCTET.2013.6675921
[3] Clerc, M. (2012). "Standard Particle Swarm Optimization". HAL
open access archive.
Figure 2: Proposed flowchart for Image segmentation using
PSO
In this PSO, the particles (pixels) from the denoised image are
represented in binary form (particles). Particles thus obtained
and the fitness evaluations of these particles are done. 
A Comparison with the parent image is being done and the
areas if match with the defined original pixels are turned to
black. If it does not match, next particle is taken and same
process is repeated. Hence the defective portion of the image
is spotted out as white area. 
IV. RESULT
Figure 4: Segmentation of various glasses using PSO
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