Work place: DYPIET/Computer Department, Pune, 411017, India
E-mail: archna.isha@gmail.com
Website:
Research Interests: Image Processing, Image Manipulation, Computational Learning Theory, Computer systems and computational processes
Biography
Archana Chaugule has completed her M.E. (Computer) from University of Pune, Maharashtra, India. She is presently pursuing Ph.D. She is having 16 Years of teaching experience in Computer Engineering and is having 12 international and national journals and conferences publications. She is member of ACM and life member of ISTE. Her areas of interest include Image Processing and Machine Learning.
By Archana A. Chaugule Suresh N. Mali
DOI: https://doi.org/10.5815/ijigsp.2014.12.05, Pub. Date: 8 Nov. 2014
This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.
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