Automated detection of fully and partially riped mango by machine vision

Advances in Intelligent and Soft Computing

Volumn 131 AISC, Issue VOL. 2, 2012, Pages 153-164

  Chhabra, Manish ^a   Gupta, Abhishek ^a   Mehrotra, Prateek ^b   Reel, Smarti ^a  

^a THAPAR UNIVERSITY   (India)

^b JAYPEE UNIVERSITY OF INFORMATION TECHNOLOGY   (India)

Author keywords

Feature Extraction; Image Recognition; Image Segmentation; Mango Quality Assessment; MATLAB GUI; neural network; watershed algorithm

Indexed keywords

AUTOMATED DETECTION; INPUT DATAS; MARKET REQUIREMENTS; MATHEMATICAL CALCULATIONS; MORPHOLOGICAL OPERATIONS; NETWORK OPTIMISATION; OUTPUT NEURONS; QUALITY ASSESSMENT; WATER-SHED ALGORITHM;

ALGORITHMS; COMPUTER VISION; FEATURE EXTRACTION; IMAGE RECOGNITION; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; NEURAL NETWORKS; PROBLEM SOLVING; SOFT COMPUTING; TREES (MATHEMATICS); WATERSHEDS;

FRUITS;

EID: 84861209420     PISSN: 18675662     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-81-322-0491-6_15     Document Type: Conference Paper

References (17)

1. Leta, F.R., et al.: Discussing Accuracy in an Automatic Measurement System using Computer Vision Techniques. In: COBEM 2005 (2005)
2. Rohs, M.: Real-World Interaction with Camera-Phones
3. Roerdink, J.B.T.M., Meijster, A.: The Watershed Transform: Definitions, Algorithms and Parallelization Strategies. Fundamenta Informaticae 41, 187-228 (2001)
4. Matlab neural network toolbox documentation. Math- Works Inc. (2007), http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/radial10.html#8378, Image Processing Toolbox
5. Spring, K., Russ, J., Davidson, M.: Basic Properties of Digital Images. Olympus America Inc.
6. The Scientist and Engineer's Guide to Digital Signal Processing, ch. 15, p. 277
7. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)
8. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison- Wesley, New York (1992)
9. Digabel, H., Lantuéjoul, C.: Iterative algorithms. In: Actes du Second Symposium Européen d'Analyse Quantitative des Microstructur es en Sciences des Matériaux, Caen, October 4-7
10. Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: Proc. International Workshop on Image Processing, Real-Time Edge and Motion Detection/ Estimation, Rennes (September 1979)
11. Bieniek, A., Moga, A.: An efficient watershed algorithm based on connected ponents. Pattern Recognition 33, 907-916 (2000)
12. Khiyal, M.S.H., Khan, A., Bibi, A.: Modified Watershed Algorithm for Segmentation of 2D Images. Issues in Informing Science and Information Technology 6 (2009)
13. Haralick, R., Shapiro, L.: Computer and Robot Vision, vol. 1, pp. 346-351. Addison-Wesley Publishing Company (1992)
14. Forsythe, M.E.: Neural Network. Prentice-Hall, New York (1983)
15. Specht, D.F.: Probabilistic neural networks for classification mapping, or associative memory. In: Proceedings of IEEE International Conferenceon Neural Networks, vol. 1 (1988)
16. Color tracking turret project page, software, http://sites.google.com/ site/colortrackingturret/
17. Color tracking turret source hosting, http://www2.cs.uregina.ca/~dbd/ cs831/notes/confusion-matrix/confusion-matrix.html