摘要
研究水果图像自动识别问题。由于外界噪声的干扰,影响自动识别的准确率。针对传统水果分类系统依靠人工或者特殊传感器件的局限,同时识别算法由于水果图像的颜色特征和纹理特征复杂,造成识别难度增大等缺陷,提出了一种新的计算机视觉和图像的水果自动分类识别系统。针对于对水果图像进行特征取值,特别是对颜色不同的水果进行识别。首先利用水果的HSV颜色表达,提取稳定的颜色特征;另外利用小波变换所生成的共生矩阵,提取水果的纹理特征。将这两种特征结合,并利用K最近邻分类器进行水果的分类和识别。经仿真结果证明了利用水果颜色和纹理特征的结合,可以有效的实现不同水果的自动分类,并比较传统单一的特征有更高的分类准确率。改进方法设备简单,分类准确率高,无需训练的优点,特别适宜在农业生产领域广泛应用。
Several fruit recognition techniques are developed based upon color and shape attributes.However,different fruit images may have similar or identical color and shape values.Hence,using color features and shape features analysis methods are still not robust and effective enough to identify and distinguish fruits images.A new fruit recognition system has been proposed,which combines two features analysis methods：color-based and texture-based in order to increase accuracy of recognition.The proposed method classifies and recognizes fruit images based on obtained features values by using K nearest neighbors classification.The proposed fruit recognition system can analyze,classify and identify fruits successfully,and the accuracy can reach to 82%.This system also serves as a useful tool in a variety fields such as educational,image retrieval and plantation science.
出处
《计算机仿真》
CSCD
北大核心
2011年第12期293-295,322,共4页
Computer Simulation
