Web-based volumetric data retrieval
VRML '95 Proceedings of the first symposium on Virtual reality modeling language
Fast polygon mesh querying by example
ACM SIGGRAPH 99 Conference abstracts and applications
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
ACM Transactions on Graphics (TOG)
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
3D zernike descriptors for content based shape retrieval
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Content-Based Search Engine for VRML Databases
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Shape-Similarity Search of Three-Dimensional Models Using Parameterized Statistics
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Directional histogram model for three-dimensional shape similarity
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Recently, there are remarkable progress in similarity computing for 3D geometric models. Few focus is put on the research of the similarity between volumetric models. This paper proposes a novel approach for performing similarity computation between two volumetric data sets. For each data set, it is performed by four stages. First, the volume data set is resampled into a unified resolution. Second, the data set is band-pass filtered and quantized to reveal its physical attributes. The resulting voxels are then normalized into a canonical coordinate system concerning the center of mass and scale. Subsequently, a series of uniformly spaced concentric shells around the center of mass are constructed, based on which spherical harmonics analysis (SHA) is applied. The coefficients of SHA constitute a rotation invariant spectrum descriptor which are used to measure the similarity between two data sets. The algorithm has been performed on a set of clinical CT and MRI data sets and the preliminary results are fairly inspiring.