Title :
Development a zooplankton recognition method for dark field image
Author :
Yajuan Wei ; Xinsheng Yu ; Yali Hu ; Dong Li
Author_Institution :
Coll. of Inf. Sci. & Eng., Ocean Univ. of China, Qingdao, China
Abstract :
In this paper, an automated method for detection and recognition of marine plankton in the dark field images is proposed and evaluated. The features extracted with shape, invariant moment and texture information are used for the support vector machine (SVM) classifier. Three species of planktons were used for the performance evaluation and 86% classification accuracy was achieved. It is shown that the recognition accuracy based on watershed algorithm is better than that method based on adaptive threshold segmentation algorithm. The result shows this is a promising method for real-time application.
Keywords :
ecology; feature extraction; geophysical image processing; image classification; image segmentation; image texture; microorganisms; real-time systems; shape recognition; support vector machines; SVM classifier; Zooplankton recognition method; adaptive threshold segmentation algorithm; automated method; dark field image; feature extraction; marine plankton detection; marine plankton recognition; performance evaluation; real-time application; shape invariant moment; support vector machine classifier; texture information; watershed algorithm-based recognition accuracy; Accuracy; Feature extraction; Image recognition; Image segmentation; Oceans; Support vector machines; Training; feature extraction; marine plankton; object segmentation; support vector machine;
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-0965-3
DOI :
10.1109/CISP.2012.6469941
