摘要
提出一种基于子空间的3D目标识别方法。该方法对3D目标进行事先的训练学习,采集目标可能出现时的图像,提取场景中目标的主要特征成分,建立所有目标样本图像和每个目标样本图像对应的两类特征子空间,分别用来确定目标类型和姿态。当输入一幅未知的待识别目标图像,识别系统将其分别向两类特征空间投影,根据它在两类特征子空间中的投影位置并参照目标特征的分布规律识别目标类型和姿态。实验证明,该方法具备对目标多种姿态图像畸变的鲁棒性,对光照变化也有很好的抑制作用,取得良好的目标识别效果。
A new image recognition algorithm - eigenspace-based approach is proposed. Firstly, recognition system obtains the possible object's images. Then, all images and image set of each object are compressed to obtain low-dimensional subspace, called the universal eigenspace and object eigenspace separately. Given an unknown input image, the recognition system projects the image into two kinds of eigenspace. The exact position of the projection in the eigenspace and the distribution formula of samples' projections determine the object's species and poses in the image. Experiments are conducted using several objects with complex appearance characteristics. The results show that the proposed approach could keep robustness for the variable poses of object, and could restrain illumination variety.
出处
《红外与激光工程》
EI
CSCD
北大核心
2004年第6期592-596,共5页
Infrared and Laser Engineering
基金
武器预研基金项目(51476010301JW0501)
关键词
目标识别
子空间
特征空间
Algorithms
Lighting
Projection systems
Radar target recognition
Robustness (control systems)
