小波矩和HMM的三维CAD模型归类与检索算法

Classification and Retrieval Algorithm of 3D CAD Model based on Wavelet Moment and HMM

为了在工程应用中检索已有的三维CAD模型,以便重用相应零件的设计信息,节省设计和加工成本,提出一种基于小波矩和仿射不变矩特征融合的隐马尔科夫模型(HMM)三维CAD模型归类与检索算法。对三维模型图进行归一化处理,并分别提取归一化图像的小波矩特征值和仿射不变矩特征值;通过K-W检验算法选择出鲁棒性好、稳定性高的组合不变矩特征并进行编码;构造五类三维模型的样本集,将上述特征值作为HMM的输入观测值,通过修正的添加比例因子的多观测序列Baum-Welch(B-W)算法进行模型的训练与识别。将本文算法与其他三种算法进行实验对比,结果表明,本文所提出的算法具有较好的识别率和检索效率,具有一定的实用价值。

In engineering applications,retrieval of existing 3D CAD models and reuse of their design information can reduce the time and cost of design and product.A classification and retrieval algorithm for three-dimensional CAD model with hidden Markov models(HMM)based on wavelet moment and affine invariant moment is proposed in this study.Firstly,the 3D model image is normalized,and the wavelet moment eigenvalues and affine invariant moment eigenvalues of the normalized image are extracted.Then the combined invariant moment features with high robustness and stability are selected by the K-W test algorithm and encoded.Finally constructing five type samples of three-dimensional models and using the above eigenvalues as input observations of HMM.The modified Baum-Welch(BW)algorithm with scaling and multi-observation sequences is used to train and identify the model.The proposed algorithm is compared with other three algorithms.The results show that the proposed algorithm has better recognition rate and retrieval efficiency,and more practical engineering value.