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首页> 外文会议>IFIP TC 5/SIG 5.1 conference on computer and computing technologies in agriculture;CCTA 2011 >Rice Kernel Shape Description Using an Improved Fourier Descriptor
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Rice Kernel Shape Description Using an Improved Fourier Descriptor

机译：使用改进的傅立叶描述子的稻仁形状描述

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摘要

A Fourier descriptor is one of the best methods for describing object boundaries, but there are limitations in describing the boundary of rice kernels using traditional Fourier descriptors. An innovative approach was developed to describe rice kernel boundaries by improving a traditional Fourier descriptor. This radius Fourier descriptor (RFD) uses a radius set for rice kernel images as its basis function, and uses amplitude spectrum of Fourier transform for the radius set as its descriptor. This method only retains the first 9 components of RFD, which is simple and the dimension of the feature vector can be reduced greatly without concern for the initial starting point on the contour. The method was validated in terms of area computation, variety distance calculation, shape description, and detection of broken kernels using a backpropagation (BP) neural network for several varieties of rice kernels. The detection accuracy for whole rice kernels of different samples was 96%-100% and for broken rice kernels was 96.5%.
机译：傅里叶描述子是描述对象边界的最佳方法之一，但是使用传统的傅里叶描述子描述水稻籽粒的边界是有局限性的。通过改进传统的傅里叶描述子，开发了一种创新的方法来描述水稻籽粒的边界。半径傅里叶描述符（RFD）使用针对稻仁图像的半径集作为其基函数，并使用傅里叶变换的振幅谱作为半径集作为其描述符。该方法仅保留RFD的前9个分量，这很简单，并且可以大大减小特征向量的维数，而无需考虑轮廓上的初始起点。该方法在面积计算，变种距离计算，形状描述以及使用反向传播（BP）神经网络对几种水稻粒的破损粒检测方面均得到了验证。不同样品的全米粒的检测准确度为96％-100％，而碎米粒的检测准确度为96.5％。

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