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Research on Automatic Detection Technique for Pebrine Image Based on Computer Vision

机译:基于计算机视觉的盆地图像自动检测技术研究

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Aiming to achieve the automatic detection and accurate identification of pebrine images, the fuzzy contrast enhancement algorithm was utilized to enhance the contrast of the target image in order to improve the image's quality; Owing to the color character of light green for the pebrine, the image segmentation technique based on the HSI model can be applied to extract the pebeine image, at the same time, the morphology theory can be adopted to remove the noises such as the hole noise and point noise, and to separate the bond particles; The region labeling can be done on the binary image after the image segmentation, then the shape parameters of pebrine can be extracted, by making full use of the feature parameters, the method of neural network based on genetic algorithm is applied to recognize the pebrine image. These experiments show that the method has achieved satisfactory image recognition results.
机译:旨在实现卵石图像的自动检测和准确识别,利用模糊的对比增强算法来增强目标图像的对比度,以提高图像的质量;由于卵石的浅绿色的彩色特征,可以应用基于HSI模型的图像分割技术来提取观图像,同时可以采用形态学理论来消除诸如孔噪声之类的噪声和点噪声,并分离粘合颗粒;该区域标记可以在图像分割之后在二进制图像上完成,然后可以通过充分利用特征参数来提取卵藻的形状参数,基于遗传算法的神经网络的方法应用于识别卵石图像。这些实验表明,该方法已经实现了令人满意的图像识别结果。

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