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首页> 外文会议>ICA;Conference on independent component analyses, wavelets, neural networks, biosystems, and nanoengineering VII; 20090413-17;20090413-17; Orlando, FL(US);Orlando, FL(US) >Classification of Biological and Non-biological Fluvial Particles Using Image Processing and Artificial Neural Network
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Classification of Biological and Non-biological Fluvial Particles Using Image Processing and Artificial Neural Network

机译：利用图像处理和人工神经网络对生物和非生物河流颗粒进行分类

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Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.
机译：与水一起流动的颗粒在很大程度上影响安全饮用水，灌溉，水生生物的保存和水力发电。这项研究描述了导致河流颗粒表征发展的活动，包括检测生物和非生物颗粒以及使用图像处理和人工神经网络（ANN）进行形状表征。基于使用ANN的多光谱图像处理来表征河流颗粒。 630nm和670nm波长的图像被视为尼泊尔巴格马蒂河中发现的生物和非生物颗粒的最鲜明特征。在季风前，季风和季风后的季节收集样品。选择随机样本，并使用MATLAB 6.5处理多光谱图像。从每个样本构建了30个矩阵。将获得的42行60列的数据作为输入训练，并使用42行2列的输出矩阵。使用传递函数创建了Perceptron神经网络模型。该系统首先经过验证，然后在尼泊尔加德满都谷地的巴格马蒂河的18个战略要地进行了测试。该网络对生物和非生物颗粒进行了分类。实时表征河流样品中生物和非生物颗粒的新型非破坏性技术的开发具有重大突破。这种应用的研究方法和结果是用于粒子实时监控的有吸引力的模型，并且具有许多应用程序，可以为许多研究和有效利用水资源提供重要的出口。它为尼泊尔加德满都大学的基础研究和应用研究开辟了新的机遇。

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