| 한글제목(Korean Title) |
Three-Dimensional Shape Recognition and Classification Using Local Features of Model Views and Sparse Representation of Shape Descriptors |
| 영문제목(English Title) |
Three-Dimensional Shape Recognition and Classification Using Local Features of Model Views and Sparse Representation of Shape Descriptors |
| 저자(Author) |
Hussein Kanaan
Alireza Behrad
|
| 원문수록처(Citation) |
VOL 16 NO. 02 PP. 0343 ~ 0359 (2020. 04) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
In this paper, a new algorithm is proposed for three-dimensional (3D) shape recognition using local features of model views and its sparse representation. The algorithm starts with the normalization of 3D models and the extraction of 2D views from uniformly distributed viewpoints. Consequently, the 2D views are stacked over each other to from view cubes. The algorithm employs the descriptors of 3D local features in the view cubes after applying Gabor filters in various directions as the initial features for 3D shape recognition. In the training stage, we store some 3D local features to build the prototype dictionary of local features. To extract an intermediate feature vector, we measure the similarity between the local descriptors of a shape model and the local features of the prototype dictionary. We represent the intermediate feature vectors of 3D models in the sparse domain to obtain the final descriptors of the models. Finally, support vector machine classifiers are used to recognize the 3D models. Experimental results using the Princeton Shape Benchmark database showed the average recognition rate of 89.7% using 20 views. We compared the proposed approach with state-of-the-art approaches and the results showed the effectiveness of the proposed algorithm.
|
| 키워드(Keyword) |
Shape Classification
Sparse Representation
3D Local Features
3D Shape Recognition
View Cube
|
| 파일첨부 |
PDF 다운로드
|
