Abstract
The fundamental feature of Computer vision involves consolidating image processing, pattern recognition and classification procedures. Extricating data from a digital picture relies on upon first distinguishing essential objects or dividing the picture into homogenous sectors or objects termed as segmentation and afterward allotting out these sectors or objects to specific classes termed as classification procedure. The term homogeneous may allude to the shade of the region or an object, however it additionally may utilize different characteristics, for example, composition and shape. This study concentrates on implementing image segmentation and classification on six different fish species using the watershed and the nearest neighbor classifier (kNN) algorithm.
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References
Khan, A. M., & Ravi, S. (2013). Image Segmentation Methods: A Comparative Study.
Pandey, Madhulika. “An Amalgamated Strategy for Iris Recognition Employing Neural Network and Hamming Distance.” Information Systems Design and Intelligent Applications. Springer India, 2016. 739–747.
Fu, K. S., & Mui, J. K. (1981). A survey on image segmentation. Pattern recognition, 13(1), 3–16.
Bhatia, M., Yadav, D., Gupta, P., Kaur, G., Singh, J., Gandhi, M., & Singh, A. (2013, September). Implementing edge detection for medical diagnosis of a bone in Matlab. In Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on (pp. 270–274). IEEE.
W.X Kang, R.R Liang. “The comparative research on image segmentation algorithms”, IEEE conference on ETCS, 2009.
Boiman, O., Shechtman, E., & Irani, M. (2008, June). In defense of nearest-neighbor based image classification. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1–8). IEEE.
Harini, R and C. Chandrashekhar. “Image Segmentation using nearest neighbor classifier on kernel formation”, International conference on pattern recognition Informatics and Medical engineering”, 2012.
Rafael C, Gonzalez, Richard E Woods. Digital Image Processing (Second Edition). Beijing: Publishing House of Electronics Industry, 2007.
Yang, Q., & Kang, W. (2009). General research on image segmentation algorithms. International Journal of Image, Graphics and Signal Processing (IJIGSP), 1(1), 1.
Agarwal, Rashi: Image Processing: [http://www.learningsquare.com].
Bhatia, M., Bansal, A., Yadav, D., & Gupta, P. (2015). A Proposed Stratification Approach for MRI Images. Indian Journal Of Science And Technology, 8(22). doi:10.17485/ijst/2015/v8i22/72152.
Wang, L., Shi, J., Song, G., & Shen, I. F. (2007). Object detection combining recognition and segmentation. In Computer Vision–ACCV 2007 (pp. 189–199). Springer Berlin Heidelberg.
Tsai, A., Yezzi Jr, A., Wells, W., Tempany, C., Tucker, D., Fan, A.,… & Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. Medical Imaging, IEEE Transactions on, 22(2), 137–154.
[Unattributed]: Fish species: [https://dani20294.wordpress.com/].
Bhatia, M., Bansal, A., Yadav, D., & Gupta, P. (2015). Proposed Algorithm to Blotch Grey Matter from Tumored and Non Tumored Brain MRI Images. Indian Journal Of Science And Technology, 8(17). doi:10.17485/ijst/2015/v8i17/63144.
Bansal, A. (2013). Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions. International Journal of Computer Applications, 61(9).
Dr. (Mrs.) G. Padmavathi, Dr. (Mrs.) P. Subashini and Mrs. A. Sumi “Empirical Evaluation of Suitable Segmentation Algorithms for IR Images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2, July 2010.
X. Munoz, J. Freixenet, X. Cuf_ı, J. Mart, “Strategies for image segmentation combining region and boundary information”, Pattern Recognition Letters 24, page no 375–392, 2003.
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Bhatia, M., Pandey, M., Kumar, N., Hooda, M., Akriti (2017). Enacting Segmentation Algorithms for Classifying Fish Species. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-10-3818-1_5
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DOI: https://doi.org/10.1007/978-981-10-3818-1_5
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