基于MATLAB的植物幼苗识别(论文11000字,外文翻译)

摘要：杂草种类繁多，严重影响了农作物的生产与产量，使用图像处理技术识别区分杂草和作物幼苗已成为一种最科学最有效的方法。通过提取植物图像的有效特征能很好的地来识别分类植物幼苗。

本论文主要研究基于MATLAB的计算机图像处理技术来识别植物幼苗，过程中使用到了MATLAB的图像处理工具箱，以12种植物不同生长阶段的不同图像为研究目标，首先对图像进行预处理，包括背景分离，轮廓提取，形态学处理，大小归一化等；其次从颜色特征、纹理特征和形状特征三个方面分别提取图像特征参数，提取颜色矩作为颜色特征参数，利用灰度共生矩阵，提取能量、熵、惯性矩、相关性作为纹理特征参数，提取HOG特征作为形状参数；最后使用HOG特征，结合支持向量机SVM分类器的算法，训练分类器，完成了对测试图像的分类，通过调整HOG特征，分类识别率最高达到了61.7%。HOG特征容易受拍摄角度和叶片重叠的影响，SVM分类器训练速度慢的缺点也影响着分类效率，因此下一步的工作将是如何更好地处理图像以减小物理因素对结果的影响和何如优化SVM分类器来提高识别率和识别效率。

关键词：特征提取；HOG特征；识别；支持向量机。

Plant seedling identification

Abstract：There are many kinds of weeds in farmland, which seriously affect the production and yield of crops. Using image processing technology to distinguish weeds and crop seedlings has become the most scientific and most effective method. By extracting the effective features of plant images, we can identify and classify plant seedlings very well.

This paper mainly studies the computer image processing technology based on MATLAB to identify plant seedlings. In the process, the image processing toolbox of MATLAB is used. By studying different images of 12 different growth stages of plants, the image is pretreated first, including background separation, contour extraction, morphological processing, and size return. Secondly, the feature parameters are extracted from three aspects of color features, texture features and shape features, and color moments are extracted as color feature parameters, and the energy, entropy, moment of inertia and correlation are extracted as texture parameters by using the gray level co-occurrence matrix, and HOG features are extracted as shape parameters. Finally, HOG is used. Features, combined with the SVM support vector machine classifier, the classifier is trained and the classification of the test images is completed. By adjusting the HOG features, the classification recognition rate is up to 61.7%. HOG features are easily affected by the angle of shooting and the overlap of the blades. The slow training speed of the SVM classifier also affects the classification efficiency, so the next step will be how to better handle the image to reduce the impact of the physical factors on the results and how to optimize the SVM classifier to improve the recognition rate and recognition efficiency.

.Key words：Feature extraction; HOG feature; recognition; support vector machine

目录

摘要    1

Abstract    2

1绪论    3

1.1选题的目的和意义    3

1.2国内外研究状况    3

1.3本文的研究内容    4

2图像获取及图像分割    5

2.1图像的获取与数据库的建立    5

2.2 图像背景分离    5

2.2.1RGB颜色模型下的分量提取    5

2.2.2阈值分割法    7

2.2.3形态学处理    9

3图像特征的选择与提取    11

3.1图像特征的种类    11

3.1.1颜色特征    11

3.1.2纹理特征    11

3.1.3形状特征    13

3.2HOG特征算法分析与提取    13

3.2.1HOG特征原理    13

3.2.2颜色空间归一化    13

3.2.3梯度计算    14

3.2.4梯度方向直方图    14

3.2.5重叠块直方图归一化    15

4 分类器的选取与构建    17

4.1SVM基本原理    17

4.2SVM核函数    18

4.3分类系统设计    19

4.4实验结果    19

5结论和展望    20

5.1结论    20

5.2展望    20

参考文献    22

致谢    23