基于支持向量机的藻类图像分割新方法

Kyle Dannemiller, Kaveh Ahmadi, E. Salari
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引用次数: 15

摘要

淡水水体是包括藻类在内的许多不同类型生物的家园。当所谓的有害藻华发生时,这些藻类会造成伤害,因此需要在藻华发生之前对淡水水体的微图像样本中的藻类进行分类。本文提出了一种提高微藻图像质量和对微藻图像进行分割的新方法,这是图像中藻类自动识别和分类的两个步骤。首先,利用Retinex增强技术提高藻类图像质量。然后,利用支持向量机将改进后的图像中的藻类从背景中分割出来。实验结果表明,该方法的检出率在95%以上。
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A new method for the segmentation of algae images using retinex and support vector machine
Bodies of freshwater act as home to many different types of organisms, including algae. These algae can cause harm when something called a harmful algal bloom takes place, and as such it is desired to classify algae in micro-image samples from the freshwater bodies before a bloom occurs. This paper presents a novel method for improving the quality of the algae micro-image and segmenting the algae in the micro-image, two of the steps involved in the automatic recognition and classification of algae in images. First, the algae image quality was improved through the use of the Retinex enhancement technique. Then, the algae in the improved quality image was segmented from the background using a support vector machine. Experimental results indicate that the detection rate of the proposed method is over 95%.
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