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Identification of Different Food Grains by Extracting Colour and Structural Features using Image Segmentation Techniques
Objectives: This paper proposes a method of identifying the different food crops which is automated. Identification of food crops can be automated using image segmentation techniques which reduces manual work. Method: Images of various food grains like wheat, paddy, cereals, barley and many more are captured using high resolution camera. Grab Cut algorithm helps in extraction only the part of image that the user is interested in. The colour features and outer boundaries of the food grains are extracted using Watershed algorithm and Canny detect detection. Findings: Extracting only the required part of an image i.e. the food grains, have made the processing easier as less features will be extracted. This reduction becomes very important when large data-set is considered. Many of the techniques used for segmenting grains consider only one feature, either colour or structural feature. The proposed system uses both colour and structural features which makes segmenting more accurate. Canny edge detection and Watershed algorithm takes less time for computation when compared with other techniques. Applications: An electronic hardware component can be built and the software part which identifies the grains can be embedded with it and used in the agricultural fields for separating food grains.
Canny Edge, Detection, Feature Extraction, GrabCut Algorithm, Image segmentation, Watershed Algorithm
- Shapiro GL, Stockman CG. Computer Vision New Jersey.Prentice-Hal. 2001; 279–325.
- Barghout L, Lee L. Perceptual information processing system. Paravue Inc. U.S. Patent Application. 2003.
- Athani S, Tejeshwar CH. Content-based Text Retrieval using Image Processing Techniques. International Journal of Computer Science and Information Security (IJCSIS). 2016; 14(11):556–61.
- Pham DL, Xu C, Prince LJ. Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering. 2000; 2:315–37. Crossref PMid:11701515
- Delmerico JA, David P, Corso JJ. Building façade detection segmentation and parameter estimation for mobile robot localization and guidance. International Conference on Intelligent Robots and Systems. 2011. p. 1632–9. Crossref
- Neuman MR, Sapirstein HD, Shwedyk E, Bushuk W. Wheat grain colour analysis by digital image processing II. Wheat class discrimination. Journal of Cereal Science. 1989; 10(3):183–8. Crossref
- Hobson DM, Carter RM, Yan Y. Rule based concave curvature segmentation for touching rice grains in binary digital images. Instrumentation and Measurement Technology Conference I2MTC ‘09 IEEE. 2009. p. 1685–9. Crossref
- Hobson DM, Carter RM, Yan Y. Characterisation and Identification of Rice Grains through Digital Image Analysis. Instrumentation and Measurement Technology Conference Proceedings IEEE. 2007. p. 1–5. Crossref
- Yao Q, Guan Z, Zhou Y. Application of Support Vector Machine for Detecting Rice Diseases using Shape and Colour Texture Features. International Conference on Engineering Computation IEEE. 2009. p. 79–83. PMid:18973760
- Hua S, Shi P. GrabCut colour image segmentation based on region of interest. 7th International Congress on Image and Signal Processing. 2014. p. 392–6.
- Canny_edge_detector. Available from: https://en.wikipedia. org/wiki/Canny_edge_detector. Date accessed: 23/05/2017.
- Canny J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; 8(6):679–98. Crossref PMid:21869365
- Wang YH. Tutorial Image Segmentation. National Taiwan University Taipei Taiwan ROC. 2010; 1–36.
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