Dinoflagellate Dategorisation by Artificial Neural Network

Phil F. Culverhouse; Vincent Herry; Robert Ellis; Beatriz Reguera; Sonsoles Gonzalez-Gil; Serena Fonda Umani; Marina Cabrini; Thomas Parisini

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Dinoflagellate Dategorisation by Artificial Neural Network

机译：用人工神经网络对鞭毛藻进行日期分类

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DiCANN is an advanced patern recognition tool for lab-oratory use that is being developed to automatically categorise specimens of marine dinoflagel-late species involved in harmful algal bloom (HAB) episodes. A prototype of the system demonstrated accurate categorisation of 23 species of dinoflagellates from microscope images. The project partners are also developing calibration techniques and standards for this new class of marine observation method. DiCANN uses artificial neural networks, an Internet-distributed database and advanced image analysis techniques to perform natural object categorisation. The system can achieve 72 to 90 percent recognition accuracy on visually similar HAB species.

机译：DiCANN是一种用于实验室的高级模式识别工具，目前正在开发该工具，以自动对涉及有害藻华（HAB）发作的海洋鞭毛鞭毛藻物种标本进行自动分类。该系统的原型展示了从显微镜图像中对23种藻鞭毛虫的准确分类。项目合作伙伴还正在为这种新型的海洋观测方法开发校准技术和标准。 DiCANN使用人工神经网络，Internet分布式数据库和高级图像分析技术来执行自然对象分类。该系统可以在视觉上相似的HAB物种上实现72％至90％的识别精度。

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《Sea Technology: Worldwide Information Leader for Marine Business, Science & Engineering》 |2002年第12期|共7页
作者
Phil F. Culverhouse; Vincent Herry; Robert Ellis; Beatriz Reguera; Sonsoles Gonzalez-Gil; Serena Fonda Umani; Marina Cabrini; Thomas Parisini;
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中图分类海洋学;
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机译：用人工神经网络对鞭毛藻进行日期分类
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机译：利用人工神经网络对田间鞭毛藻进行自动分类

Dinoflagellate Dategorisation by Artificial Neural Network

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