CAJ | 학술논문

为了对未知分类信息的三维模型进行分类，提出三维模型分类识别算法。首先以改进的形状直径函数（shape diameter function ，SDF）特征描述符为基础对所有三维模型提取特征向量，并将未知分类信息的三维模型作为测试模型，在已知分类的三维模型数据库中找到与测试模型最相似的 k个模型；然后在这 k个模型中利用稀疏表示分类方法对测试模型进行识别；最后确定测试模型在三维模型数据库中的分类信息。实验结果表明，该算法简单且易于实现，具有较高的识别准确率及较强的鲁棒性。
위료대미지분류신식적삼유모형진행분류，제출삼유모형분류식별산법。수선이개진적형상직경함수（shape diameter function ，SDF）특정묘술부위기출대소유삼유모형제취특정향량，병장미지분류신식적삼유모형작위측시모형，재이지분류적삼유모형수거고중조도여측시모형최상사적 k개모형；연후재저 k개모형중이용희소표시분류방법대측시모형진행식별；최후학정측시모형재삼유모형수거고중적분류신식。실험결과표명，해산법간단차역우실현，구유교고적식별준학솔급교강적로봉성。
To classify 3D models whose classification information is unknown prior ,this paper proposes a recognition algorithm for 3D models .Firstly ,the algorithm extracts feature vectors for each 3D model based on an improved shape diameter function (SDF ) feature descriptor .Secondly ,each 3D model ,whose classification information is unknown ,is regarded as the test model .And then the algorithm finds k models ,which are similar with the test model ,in the 3D models database where each model's classification information is know n in advance .Finally the sparse representation classifier is applied to the test model and the k models to determine the classification information of the test model in the 3D models database . Experimental results show that the algorithm is simple and easy to implement .Besides ,the algorithm is highly accurate and robust .