classification of dates varieties and effect of motion blurring
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7/29/2019 Classification of Dates Varieties and Effect of Motion Blurring
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O R I G I N A L P A P E R
Classification of dates varieties and effect of motion blurringon standardized moment features
Gabriel Thomas A. Manickavasagan
R. Al-Yahyai
Received: 30 July 2012 / Accepted: 11 October 2012
Springer Science+Business Media New York 2012
Abstract Computer vision technology has been used as
a successful non-destructive quality assessment tool forvarious food products. In general, several features are
extracted from the images of interest, and used for the
classification models. Furthermore, in most of the studies,
static images have been used in the calibration and evalu-
ation models. Classification models with a reduced number
of features, and a mechanism to test the capability of the
algorithm for moving objects by means of simulating the
blurring effect on the static images would be beneficial to
determine the performance of the system in real-time
quality monitoring in industries. Using three date varieties
as model food, motion was simulated for the dates images
and a successful neural network classifier was designed
with only three statistical features (mean, standard devia-
tion, and skewness). The reduced number of features and
simplicity of the classifier yielded a solution that can be
potentially implemented in hardware fast enough so that to
consider the case of classification of the dates in a conveyor
belt. To test the solution under such conditions, a blurring
degradation function was used to verify that the classifierwould work. The effects that motion blurring causes to
these statistical moments in a general sense were examined
using random numbers drawn from the distribution in the
Pearson system. Because motion blurring showed a ten-
dency to change the distribution to a Gaussian density, the
same features and classifier yielded similar results despite
of motion.
Keywords Classification Neural network Bayes classifier Pearson random numbers Statistical
moment Image motion
Introduction
In computer vision (CV) technology, objects are imaged
and analyzed to characterize their quality. This technique
has great potential to be used as a non-destructive and
objective quality measurement method. It is a reliable
technique for the measurement of various quality attributes
of agricultural and food products [14]. Attribute charac-
terization using images taken while the objects are at static
could be implemented at quality control laboratories.
However, the efficiency of the developed classification
models for the objects moving on a conveyor at various
speeds would be highly beneficial to determine their ability
for online quality monitoring in the real-time production
facilities in food industries. Therefore the objective of this
study was to determine the classification effects caused by
simulated motion of date varieties with three grayscale
features at static and motion blurred conditions. Motion
was simulated via low pass filtering which system response
is usually calculated in order to eliminate motion bluring
G. Thomas (&)
Department Electrical and Computer Engineering, Faculty of
Engineering, University of Manitoba, E3-555 Engineering and
Information Technology Complex, Winnipeg, MB R3T5V6,
Canadae-mail: Gabriel.Thomas@ad.umanitoba.ca;
thomasg@cc.umanitoba.ca
A. Manickavasagan
Department of Soils, Water and Agricultural Engineering,
College of Agricultural and Marine Sciences, Sultan Qaboos
University, P.O. Box 34, 123 Al-Khoud, Sultanate of Oman
R. Al-Yahyai
Department of Crop Science, College of Agricultural and Marine
Sciences, Sultan Qaboos University, P.O. Box 34,
123 Al-Khoud, Sultanate of Oman
123
Food Measure
DOI 10.1007/s11694-012-9129-9
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using deconvolution techniques. Different methods propose
alternative models for the low pass impulse response of
the system and the expression that corresponds to linear
motion in one direction as defined in [12] is used here; a
conveyor belt motion scenario reduces the model to this
case only. Dates are used as model images and this tech-
nique can be used as such in other products.
Date is an important commodity in Oman, and around50 % of the cultivable lands are under date palm vegeta-
tion. Although annual production of dates in Oman is
255,891 Mt, only 9,000 Mt (2.53.5 % of production) is
exported due to various reasons [5]. Quality assurance has
always been a major problem for Omani dates to compete
in international markets [6]. Varietal purity, color, unifor-
mity of size, and absence of defects are some of the
important quality parameters for dates in domestic and
international market. Generally, manual grading of dates is
followed in handling and processing facilities to identify
date varieties. This method has many constraints such as
subjectivity, influence of mental stress, influence of envi-ronment, efficiency of individuals at various times of the
shift, and so on. An automated variety identification
method using CV technique would be beneficial to the date
industries in Oman which would correspond to a first step
to assess the quality by avoiding contamination from dif-
ferent varieties.
Materials and methods
Image acquisition
Three date varieties namely Khalas, Fard, and Madina were
used in this study. Samples for each variety were obtained
from at least three shops in Oman, and the varietal purity
was confirmed by a date variety expert at Sultan Qaboos
University. A conglomerate sample of 108 dates was taken
for each variety (n = 108 for each variety) and the sample
was imaged (single date images) with a color camera
(model: D3, Nikon, Japan, Resolution4,256 9 2,832
pixels). The date samples were illuminated with halogen
lamp (Visa tech, model SOLO 1600B) during imaging.
Then all images were converted into gray scale images
using Matlab software, and analyzed.
Feature selection
As the background of these images was deemed to have
little information regarding the classification of each date, a
thresholding operation was done in order to eliminate it
based on the maximization of the between-class variance.
The method is well known as Otsus method [7] and it is
briefly described next.
Starting by considering an image with background class
defined as wb, and object in class wo, with probabilities
P wb PT1
i1 f zi and P wo PL
iT fzi where
z denotes discrete image intensity, f(zi), i = 1, 2, , L is
the corresponding histogram normalized to have area equal
to one, and L is the number of distinct intensity levels; the
threshold T is found by maximizing r2
B
T
p wb lb ltot 2 p wo ltot
2where lb
PT1i1 zifzi,
lo PL
T zifzi and ltot PL
i1 zifzi.
Statistical features in the form of standardized moments
were to be considered [8]:
cn l
n
rn
E z m n
rn1
where Eis the expected value, r the standard deviation and
ln is the nth moment about the mean. For n = 3 one can
find skewness that measures the asymmetry of the proba-
bility density function of the random variable associated to
the image values. For n = 4 the value is known as kurtosiswhich also evaluates the shape of a distribution by quan-
tifying its peakness.
Because processing speed was important, out of the
mean, standard deviation, skewness, and kurtosis, we
decided to work with only three of them which would
eliminate approximately 25 % of the computations. Then,
for feature evaluation, a selection scheme with a backward
sequential search starting with a full feature set and
sequentially removing features was considered to be a
practical solution for the decision of what features to work
with. We decided to use a new feature selection scheme
that is based on fuzzy entropy measures with a similarityclassifier as presented by Luukka [9], which is briefly
described next.
The method starts by forming an ideal vector
vi = (v1(f1), , vi(ft)) that represents the class I as good as
possible by calculating the mean values of available vec-
tors in each class vi(fi) for i = 1, 2, , t features. After-
wards, the similarities Shx; vi between the sample x to beclassified and the ideal vectors v need to be calculated as:
Shx; vi 1
t
Xtr1
1 x fr vfrj j 2
In order to calculate the relevance of the features, fuzzy
entropy values are calculated with similarity values lA xj
as suggested by DeLuca and Termini [10]:
H1 A Xnj1
lA xj
log lA xj
1 lA xj
log 1 lA xj
3
where low entropy indicates high similarity values
and high entropy values are obtained otherwise.
G. Thomas et al.
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Similarly, fuzzy entropy as suggested by Parkash et al. [11]
was used in this work which follows the expression:
H2 A Xnj1
sinplA xj
2
sinp 1 lA xj
2
1
4
Effect of motion
To validate the use of the classifier in motion conditions, a
simulation was conducted where the blurring degradation
caused by motion in one direction was implemented in the
frequency domain as [12]:
H u; v Te
puasin puaejpua 5
where u and v are normalized frequency samples, Te is the
camera exposure time, and a is the rate of movement in the
x axis direction: xo(t) = at/T. When t= T the image has
been displaced by a total distance a.
Results and discussion
Evaluation of features
Figure 1 illustrates three samples of each variety of dates.
The standardized moments were calculated only within the
pixels located within the date and not the background and
were computed for one date image at a time. From the
small image sample, it can be inferred that features such as
the mean value would work for the class Fard, since theylook darker than the other two classes. Figure 2 shows the
mean values m for 108 images of each class calculated with
the expression mean m = ltot. Note how in Fig. 2 the
values for class Fard do appear darker and dissimilar than
the other two.
We tested the four features (mean, standard deviation,
skewness, and kurtosis) and evaluated them according to
Eqs. (3) and (4). The selection process consistently elimi-
nated the values of kurtosis. With this result, the next
section elaborates on the design of the classifier using only
the mean, standard deviation, and skewness as inputs.
Classifier selection and classification results
As described in the previous section, three features were to
be used in the final classifier. Figure 3 shows a scatter plot
of the features and albeit three clusters are visible, there is
still some overlapping. A Lillilifors test [13] of the default
null hypothesis that the values of the different features
come from a Gaussian distribution passed the test con-
firming the Gaussianity of the clusters. Afterwards, a Bayes
classifier for Gaussian pattern classes under the condition
of a 01 loss function was implemented forming three
decision functions dj(x) for j = 1, 2, 3 of the form [12]:
dj x ln P wj
1=2 ln Cj
1=2 x mj T
C1j x mj h i
6
where mj = Ej{x} are the means of the feature grouped invector x, and Cj = Ej{(x2mj)(x2mj)
T}.
This classifier was tested using 80 samples for training
and 28 samples for testing. A 65.48 % of correct classifi-
cations were obtained this way, and in particular there were
only two misclassification for the class Madina yielding a
92.86 % of accuracy for this variety. When kurtosis was
used instead of skewness, a performance of 35.71 % was
obtained. This confirmed that the selection made by
Luukkas approach was correct.
Fig. 1 Gray scale images of date samples (first row Fard, second row
Khalas, third row Madina)
0 20 40 60 80 100
60
70
80
90
100
110
120Mean
Sample Index
Value
Khalas (blue)Fard (green)
Madina (red)
Fig. 2 Average gray level values of date samples
Dates varieties and effect of motion blurring
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At this point, a two layer neural network was used with
hyperbolic tangent sigmoid transfer functions using Matlab
as the software platform for development. The training
algorithm used was the scaled conjugate gradient. In order
to define the number of hidden neurons N, neural networks
were trained 30 times with different numbers of hiddenneurons. The initial weights are calculated randomly by
Matlab and these averages would give an idea of the best
number of hidden neurons to be used. Because only three
features are used, the expectation was that the network
would not need many neurons. Table 1 shows the average
results after training the different networks 30 times with
respect to mean squared error (MSE) as well as time
required for training. Because of the small numbers of
neurons used, the training time was very short for all of
them.
From these results, it was decided that the number of
hidden neurons to use was five, not only because it had theminimum MSE value in Table 1 but also because of the
computational savings of not having to compute more
multiplications once the network had to perform classifi-
cations. Figure 4 shows the performance obtained using
such a network. The total number of epochs needed was 23,
for each class 65 out of 108 images were used for training,
best performance was obtained with MSE values of 3.81 %
for testing, 9.7 % for training and a needed training time of
0.78 s. Once trained, classifications were performed in less
than 0.025 s using an Asus Ee Slate tablet computer with
an Intel i5 1.33 GHz processor and 4 GB of RAM memory.
At this point the specified classification efficiency of
more than 90 % was achieved with a simple neural net-
work architecture to allow the option of a future imple-mentation in hardware rather than in a computer based
software solution. Thus, the solution was considered to be
fast enough to actually be implemented in a scenario where
a conveyor belt with continuous motion could be used.
Effect of motion
Figure 5 shows blurring simulated using Eq. (5) for a = 20
for the images shown in Fig. 1. Because a bright illumi-
nation set up was contemplated so that to eliminate any
possible shadows of the dates in bulk, faster shutting
camera times are expected and the blurring in Fig. 5 wasconsidered an extreme case.
In order to have an idea of the effects this blurring will
cause to the features, 5,000 images of size 256 9 256
formed by random numbers drawn from the distribution in
the Pearson system with specified l, r, c3, and c4, were
generated and blurred for different values of a. Figure 6
shows how these features changed as the blurring pro-
gresses. As the degradation function in (5) is a low pass
filter, blurring can be seen as a weighted averaging and no
changes in mean values are expected. The slight changes in
Fig. 6a correspond to the darkening of the beginning and
final values of the blurred images caused by discreteimplementation of the filter that viewed in the discrete time
domain, convolution values have this windowing effects.
The standard deviation is reduced as expected after low
pass filtering and skewness tend to go to zero, making the
samples more Gaussian as what we would see by averaging
random samples and explained by the central limit
theorem.
We used these features using Bayes classifiers in order
to identify the image blurring effects on classification.
60
80
100
120
15
20
25
30
35
-2
0
2
mean
standard deviation
skewness
KhalasMadinaFard
Fig. 3 Sctatter plot of the selected features for date samples
Table 1 Mean squared error (MSE) obtained while using a two layer
neural network with different number of hidden neurons
No. of neurons
n = 3 n = 5 n = 10 n = 15 n = 20
MSE (%) 12.73 11.04 12.27 13.62 13.79
Time (s) 1.0915 1.0130 1.0031 1.0441 1.0711
0 5 10 15 20 250
0.2
0.4
0.6
0.8
Epoch
MSE Train
Test
Fig. 4 Mean squared error (MSE) using a two layer neural network
with five hidden neurons. Matlab defines MSE as the measurement of
the networks performance according to the mean of squared
classification errors
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Figure 7 shows the results when using both skewness and
kurtosis. As it can be seen, motion blurring at these rates
did not cause any major differences and it was further
confirmed that rejecting the kurtosis feature was correct as
indicated by the feature selection section. Similar two layer
networks as the one used in Fig. 4 were used for different
values of a, and Fig. 8 shows the consistent good results.
Results with accuracies similar to the ones found in
Pydipati [14] for detection of citrus disease were found
using a simpler neural network and reduced number of
features. Because of this simplicity, real time classification
under motion blurring caused by examining the dates on a
conveyor belt was deemed feasibly. Thus, we developed a
methodology to introduce motion via a degradation func-
tion implemented in the frequency domain to confirm the
robustness of the proposed method.
Acknowledgments We thank The Research Council (TRC) of
Sultanate of Oman for funding this study (Project No. RC/AGR/
SWAE/11/01-Development of Computer Vision Technology for
Quality Assessment of Dates in Oman).
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Fig. 5 Motion blurred images of date samples (first row Fard, second
row Khalas, third row Madina)
0 5 10 15 20126
127
128
129
mean
(a)
0 5 10 15 200
2
4
6
stan
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(b)
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(c)
Fig. 6 Results of motion blurring on selected features of date
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Fig. 8 Mean squared error (MSE) obtained while using a two layerneural network with blurred images (results of test images)
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