Transcript Technical seminar on Analysis of rice granules using Image

TECHNICAL SEMINAR
ON
CLASSIFICATION OF RICE GRANULES
USING IMAGE PROCESSING AND
NEURAL NETWORK
Presented by:
Kamakhaya Argulewar
Guided by:
Prof. Shweta V. Jain
OVERVIEW

Introduction

Papers Read

Flow Diagram of Classification and Grading Techniques

Technique for classification

Issues in Existing system

Conclusion

Future Work

References
INTRODUCTION

Classification includes a broad range of decision-theoretic approaches
to the identification of images .

All classification algorithms are based on the assumption that the image
in question depicts one or more features and that each of these features
belongs to one of several distinct and exclusive classes.

Image classification analyzes the numerical properties of various image
features and organizes data into categories.
PAPERS READ
Sr Paper Name
.
N
o.
Author
Year
Conclusion
1
Analysis of rice
granules using Image
Processing and Neural
Network
Neelamegam.
P, Abirami. S,
Vishnu Priya.
K, Rubalya
Valantina.S.
IEEE
2013
Back propagation based
neural network well classify
the rice granules.
2
A Grain Quality
Classification
System
L.A.I.Pabamalie IEEE
,H.L.Premaratn 2010
e
This research has been done
to identify the relevant
quality category for a given
rice sample
CONT….

In food handling industry, grading of granular food materials is
necessary because samples of material are subjected to
adulteration.

Existing system work on the feature which were extracted from
images of rice kernels are parameter, Area, Minor-axis Length
and Major-axis Length ,texture feature using Contour detection .
FLOW DIAGRAM OF CLASSIFICATION
IMAGE ACQUISITION :

The first step in classification is image acquisition. This acquire
image is given as input to pre-processing.
PREPROCESSING:

Smoothing: Filtering technique is used to remove noise from image .

Thresholding : It is the method of image segmentation . From a gray
scale image threshold can create a binary image.
EDGE DETECTION TECHNIQUES
1) Sobel Edge Detection:
o
In Sobel edge detection, for each position of the pixel in the
image the gradient is calculated.
o
Series of gradient magnitudes are created using a simple
convolution kernel.
2. CANNY EDGE DETECTION


Canny edge detector is an optimal detector which gives optimal
filtered image.
Canny edge detector also contain weak edges which is connected to
strong edges.
FEATURE EXTRACTION

Extraction of information from the image is base on feature extraction.

Object recognition and classifications are performed based on the
feature extraction.
TEXTURE FEATURE EXTRACTION

At the beginning of texture feature extraction cropped the rice
image from its background.

Which reduces the background effect from the image.

When creating the gray level co-occurrence matrix we have been
considered R, G, B channels separately and creates three matrixes
with 255 * 255*16 size based on these three channels.

Pixel values of R, G, and B channels always in between 0-255.
Therefore, size of the GLCM was 255*255.

They considered four angles which were 0°,45°, 90° and 135° to access
the adjacent pixels from a particular pixel location.

It has been considered four adjacent pixel distances, 1, 2, 3 and 4 for a
particular direction.

Finally, there were sixteen GLCM matrixes have been created for a
particular channel regarding four directions and four pixel distance.

Then, calculate values for those GLCM matrixes.

Extract texture feature values using those sixteen GLCM matrixes and
finally calculate the average value of them.
NEURAL NETWORK

Supervised classification of objects into predefined categories.

Neural network is typically organized in layers. layers are made up of
number of interconnected node .

Pattern are presented to the network via the input layer which
communicate to one or more hidden layers where the actual
processing is done via a system of weighted connection.

The hidden layers then link to an output layer .
NEURAL NETWORK ARCHITECTURE
NEURAL NETWORK SPECIFICATION

neural network was used for the classification based on the
extracted features from the rice samples.

The neural network is built with three neurons in input layer,
seven neurons in the hidden layer and one neuron in the output
layer.

The network used for classification is back propagation
algorithm.
CONT…..

During the training, neural network weights are initiated with
random values.

The weights are stored during the end of training.

When the training has completed, the network can be tested
to calculate the accuracy with stored weights.
BACK PROPOGATION ALGORITHM
Back propagation have two phases:

Forward pass phase: computes ‘functional signal’, feed forward
propagation of input pattern signals through network.

Backward pass phase: computes ‘error signal’, propagates the error
backwards through network starting at output units.
output
Errors
ISSUES IN EXISTING SYSTEM

Neural network can not work well in the presence of
overlapping grains.

Neural network does not accurately classifies the rice granules
when there is overlapping of grains.
CONCLUSION

Back Propagation based Neural Network is able to
classify well when there is no overlapping of granules.
FUTURE WORK

To make the result more accurate more features can be calculated.
REFERENCES

Neelamegam. P, Abirami. S, Vishnu Priya. K, Rubalya Valantina.S.
“Analysis of rice granules using Image Processing and Neural
Network “Proceedings of 2013 IEEE Conference on Informati.on
and Communication Technologies (ICT 2013).

Bhupinder Verma “Image Processing Techniques for Grading &
Classification of Rice” Int’l Conf. on Computer & Communication
Technology .

L.A.I.Pabamalie, H.L.Premaratne” A Grain Quality Classification
System” 2010 IEEE Conference . on Computer & Communication
Technology .
THANK YOU