| 基于神经网络的食用玫瑰花图像识别算法 |
| |
| 引用本文： | 王彦钧,张云伟,王大龙,殷欣,曾伟军.基于神经网络的食用玫瑰花图像识别算法[J].中国农业大学学报,2014,19(4):180-186. |
| |
| 作者姓名： | 王彦钧 张云伟 王大龙 殷欣 曾伟军 |
| |
| 作者单位： | 昆明理工大学 现代农业工程学院, 昆明 650500;昆明理工大学 信息工程与自动化学院, 昆明 650500;昆明理工大学 现代农业工程学院, 昆明 650500;昆明理工大学 现代农业工程学院, 昆明 650500;昆明理工大学 现代农业工程学院, 昆明 650500 |
| |
| 基金项目： | 国家自然科学基金项目（31060118）；云南省应用基础研究项目（2009ZC041M）；昆明理工大学人才培养项目（2010-07） |
| |
| 摘 要： | 针对单依靠颜色或形状将采摘期玫瑰花从图像中分割出来难度较大的问题,研究一种基于神经网络的食用玫瑰花图像识别算法。将处于采摘期的玫瑰花正面图像作为识别对象,先提取HSI色彩空间下的S分量,用最大类间方差法(Otsu)进行分割;再提取目标图像灰度共生矩阵下的纹理特征,选取区分度高的纹理特征,结合BP神经网络,建立识别模型。试验结果表明:该方法正确识别率85%,识别率主要受试验样本开放标准选取的影响,而受光照影响不敏感,是一种较好的识别方法。
|
| 关 键 词： | 食用玫瑰花 图像分割 纹理特征 神经网络 |
| 收稿时间： | 2013/10/29 0:00:00 |
Recognition algorithm of edible rose image based on neural network |
| |
| WANG Yan-jun,ZHANG Yun-wei,WANG Da-long,YIN Xin and ZENG Wei-jun.Recognition algorithm of edible rose image based on neural network[J].Journal of China Agricultural University,2014,19(4):180-186. |
| |
| Authors: | WANG Yan-jun ZHANG Yun-wei WANG Da-long YIN Xin and ZENG Wei-jun |
| |
| Institution: | Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China;Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China;Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China;Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| |
| Abstract: | It is quite difficult to distinguish the appropriate rose by its color or shape.In order to solve this problem,this paper is aimed to explore an image segmentation and recognition algorithm of edible rose.The roses just before picking period were taken as the recognition object.Firstly,shifted this image to HIS color space;extracted S weight and segmented the image by using Otsu method.Then extracted the textural features of high distinction degree and recognized the picking rose by integrating BP neural network.The result indicated that the correct recognition rate of this method was higher than 85% and the recognition rate was mainly affected by the samples'' openness instead of their sensitive to the illumination. |
| |
| Keywords: | edible rose image segmentation textural features neural network |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《中国农业大学学报》浏览原始摘要信息 |
| 点击此处可从《中国农业大学学报》下载免费的PDF全文 |
|
