摘要
基于识别目标真假和空间位置的需要,将180幅训练图像分成了4个训练集,分别计算每个训练集的特征向量,分析结果表明每个训练集可以用3个特征向量来表示,这样用12个特征向量就可以建立目标的4个特征空间,使目标分解与重构过程大大简化。利用待识别目标向量与重构向量之间的关系,不仅可以判别目标的真假,还可以确定目标所在的空间位置。模拟结果表明,提出的目标多特征空间的建立方法和目标识别准则是有效的,能够实现离面旋转条件下三维目标真假和空间位置的识别。
In order to recognize an object and determine its spatial position, 180 training images are divided into four image sets, and the feature vectors of all image sets are calculated. It is shown that each image set can be represented with three feature vectors, so the total four feature spaces can be constructed with only twelve feature vectors. The object decomposition and reconstruction are simplified greatly. Based on the relation between object vector and its reconstructed vector, the object can be determined as true or false, and its spatial position can be located. Simulation results show that the method of constructing multiple feature spaces and the object recognition rule are effective, and object recognizing and spatial position determining under out-of-plane rotation can be achieved.
出处
《中国激光》
EI
CAS
CSCD
北大核心
2007年第7期952-956,共5页
Chinese Journal of Lasers
基金
江苏省自然科学基金(BK2006726)
江苏省高校自然科学基金(06KJB140062)资助项目
关键词
图像处理
目标识别
多特征空间
特征向量
image processing
object recognition
multiple feature spaces
feature vector
