基于Gabor及深度神经网络的葡萄种子分类

种子成熟度需要受过长期训练的专家通过肉眼进行观察和判断。为了改变传统人工经验判断的方式，该文提出了一种基于Gabor小波特征提取及深度神经网络的葡萄种子图像分类识别算法，以便实现高效、准确的分类识别效果。首先，利用背景差分法在背景图像中分割出兴趣目标，从而完成图像的预处理。然后，通过改进的Gabor小波特征提取，使得Gabor滤波后的图像具有更多的细节纹理信息。最后，将深度卷积神经网络和提取到的纹理特征信息相结合进行分类。实验结果表明，基于机器学习的葡萄种子成熟度识别是切实可行的。且相比于其他类似分类算法，本文算法的图像分类精度有了一定的改善。

Grape Seed Classification Based on Gabor and Deep Neural Network

Seed maturity has a great influence on the quality of wine, and it needs to be observed and judged by the naked eye by experts who have been trained for a long time. In order to change the way of traditional artificial experience judgment, a grape seed image classification and recognition algorithm based on Gabor wavelet feature extraction and deep neural network is proposed to achieve efficient and accurate classification and recognition. First, the background difference method is used to segment the interest target in the background image, thereby completing the image preprocessing. Then, the improved Gabor wavelet feature extraction makes the Gabor filtered image have more detailed texture information. Finally, the deep convolutional neural network and the extracted texture feature information are combined to classify. The experimental results show that the recognition of grape seed maturity based on machine learning is feasible. The proposed image classification accuracy exhibits a certain improvement compared with other similar classification algorithms.