摘要
针对在视点变化情况下进行目标识别这一问题,作者结合主分量变换提出了一种基于Hausdorff距离的目标匹配算法,该算法在噪声和遮挡下性能稳定,时间代价较小.作者还提出了一种具有平移、旋转、尺度不变性以及对噪声有抗干扰能力的角点特征构造方法,通过BP网络实现目标分类.与其他三种形状特征进行实验对比,结果证明该方法在视点发生变化时对目标的识别更为有效.
Object recognition with changes in 3D viewpoint is a hot spot currently in the domain of image pattern recognition.An algorithm for pattern matching based on Hausdorff distance is proposed in this paper.The principal components transformation is introduced to this algorithm and makes it stable,distinct and almost real time.Meanwhile,a method for extracting corner feature from images is proposed.The extracted feature is invariant to translation,rotation,scale change and is shown robust to addition of noise.Further,a system to recognize the object with changes in 3D viewpoint is presented using this feature and BP network.The results of experiments for comparison demonstrate that the proposed method is more effective than the other three ones.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2011年第3期308-312,共5页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(60903071)
