CLASSIFICATION OF SQUIDS USING MORPHOMETRIC MEASUREMENTS
Rising interest in conservation and biodiversity increased the demand for accurate and consistent identification of biological objects, such as species. Among the identification issues, squids identification at the species level has been strongly addressed. Squid, is a carnivorous marine cephalopod mollusk. Each species of the squids has got its own characteristic patterns and to accurately classify the squids. In this paper we used to extract the morphometric features of the squids using image processing techniques. Here, the process begin with removing the noise of images, and then crop the images by using Region of Interest (ROI) for specified features. After then applying edge detection methods can be employed to characterize edges to represent the image for further implementation and then measure the morphometric features to estimate the type of squid based on external features like squid mantle, fin and head. Finally, For classification of squids the Artificial Neural Network (ANN) has used to classify the species based on extracted morphometric features.
Erica A.G. Vidal “Advances in Marine Biology”, Academic Press, Vol. 67, pp: 235-359, (2014).
WaheedM.Emam,AbdELhalimA.Saad And RafikRiad et.al., “Morphometric study and length-weight relationship on the squid Loligoforbesi (cephalopoda; Loliginidae) from the Egyptian Mediterranean waters”, International Journal of Enviromental Science And Engineering(IJESE),Vol.5, pp: 1-13, (2014).
E. G. Silas “Cephalopod Bionomics. Fisheries And Resources of The Exclusive Economic Zone Of India”, (1985).
Hanlon.R.T. “Mariculture cephalopods life cycles”, vol.2, pp:291-305, (1997).
Internet: Online http://www.squid-world.com/squid-pictures.
Internet: Online http://ocean.nationalgeographic.com/ocean/photos/squid/#/squid01-caribbean-reef-squid_18209_600x450.jpg.
Yeong-Hwa Kim and Yong Jun Cho, “Feature and Noise Adaptive Unsharp Masking Based on Statistical Hypotheses Test” IEEE Transactions on Consumer Electronics, vol. 54, pp.823-830, (2008).
M.B. Ahmad and T.S. Choi, “Local Threshold and Boolean Function Based Edge Detection”, IEEE Transactions on Consumer Electronics, Vol. 45, No3,(1999).
V.Sucharita, S.Jyothi and D.M.Mamatha “A Comparative Study on Various Edge Detection Techniques used for the Identification of Penaeid Prawn Species” International Journal of Computer Applications, Vol. 78, No.6, (2013).
Yuan-Hui Yu, Chin-Chen Chang “A new edge detection approach based on image context analysis” Image and Vision Computing pp: 1090–1102,( 2006).
Meghana D.More and G.K.Andurkar “Edge Detection techniques: a comparative approach” proceedings of NCACC’12’,(2012) .
ZainalAbidin Muchlisin, BatmiZulkarnaini, SyahrulPurnawan et.al.,“Morphometric variations of three species of harvested cephalopods found in northern sea of Aceh Province, Indonesia” Vol 15, Pages: 142-146 DOI: 10.13057/biodiv/d150205, (2014).
GurpreetKaur, Abhilash Sharma " Feature Extraction techniques for Classification of Emotions in Speech signals" International Journal of advanced Research in Computer Science and Software Engineering, Vol:4, (2014).
Mark S. NixonAlberto S. AguadoAmsterdam “Feature Extraction and Image Processing” Academic Press, Second Edition, pp:424, (2008).
Md.imdadul Islam, mahbubul Alam,Sarnali Basak et.al., “Image Based Measurement and Distance of an Inaccessible Object” International Journal of Advanced Computer Research, Vol.3, (2013).
Lee kien Leow, Li-Lee Chew,Ving Ching Chong et.al., “Automated identification of copepods using digital image processing and artificial neural network” international Conference on bioinformatics, pp: 9-11, (2015).
Alsmadi M. K. S., K. Omar, and S. A. Noah “Back Propagation Algorithm: The Best Algorithm among the Multi-layer Perceptron Algorithm”, International Journal of Computer Science and Network Security, pp. 378-383, (2009).