3 November 2008 Snail identification based on the invariant moments
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Proceedings Volume 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments; 714520 (2008) https://doi.org/10.1117/12.813054
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Based on the monitoring of Satellite Remote Sensing Images, a lot of big progresses have been made in environment analysis and researches about the schistosome snail breeding ground and the distribution of snails in marshland. This paper focuses on the identification of the Schistosome snail individual goals. Based on the image segmentation, the objects, including snails, are segmented from the background. Pattern features of the snails are extracted by calculating the invariant moments of typical snails. By calculating the invariant moments parameter of objects to be recognized and the Euclid distance of the feature parameters of swatches, the snail targets are identified. In the laboratory environment, the recognizing rate can reach over 90% and it has robust in rotation, scaling and translation. The steps can be described as follows: Step 1, by gray level modification, noise elimination, edge sharpening and binarization, the objects are segmented from the background. Step 2, typical snails' boundary is extracted by contour tracking and the central moments are calculated. Step 3, the central moments is normalized. The 7 invariant moments are calculated as the pattern features of the snails. Step 4, the boundaries of these objects are extracted by contour tracking and the central moments are calculated. Step 5, the central moments of the objects are normalized and the 7 invariant moments of the are calculated. Step 6, the Euclid distances of The 7 invariant moments between the objects and the typical snail are calculated. The objects with small distance will be judged as snails and the objects with large distance will not be judged as snails.
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Donghong Hu, Donghong Hu, Hao Wang, Hao Wang, Jianming Yang, Jianming Yang, Ling Zhang, Ling Zhang, Ying Wang, Ying Wang, } "Snail identification based on the invariant moments", Proc. SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments, 714520 (3 November 2008); doi: 10.1117/12.813054; https://doi.org/10.1117/12.813054
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