摘要
This paper presents a real-time alga classifier designed for flow-cytometry-based marine alga monitoring systems.The difficulties of such classification include:1)the shape of the same algae category is deformable,and largely variant due to the individual differences and mature stage;2)the image of algae may vary due to different 3D positions to the imaging plane and partial occlusion;3)the images also contain unknown algae and contaminations.In the proposed method,several shape features were developed,a naive Bayes classifier(NBC)was trained to reject the contaminative objects and unknown algae,and a support vector machine(SVM)was used to classify the algae to taxonomic categories.Our approach achieved greater 90%accuracy on a collection of algal images.The test on contaminated algal image set(containing unknown algae and non-algae objects such as sands)also demonstrated promising results.
This paper presents a real-time alga classifier designed for flow-eytome- try-based marine alga monitoring systems. The difficulties of such classification include： 1） the shape of the same algae category is deformable, and largely variant due to the individual differences and mature stage; 2） the image of algae may vary due to different 3D positions to the imaging plane and parlial occlusion; 3） the images also contain unknown algae and contaminations. In the proposed method, several shape features were developed, a naive Bayes classifier（NBC） was trained to reject the contaminative objects and unknown algae, and a support vector machine （SVM） was used to classify the algae to taxonomic categories, Our approach achieved greater 90% accuracy on a collection of algal images. The test on contaminated algal image set fcontaining unknown algae and non-algae objects such as sands） also demonstrated promising results.
基金
High-Tech Research and Development Program of China
grant number:2003AA635160
