Chi-square goodness-of-fit test of 3d point correspondence for model similarity measure and analysis

Authors:
Jun Feng;Horace H. S. Ip
Affiliations:
Image Computing Group, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Image Computing Group, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Venue:
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Year:
2005

Citing 9
Cited 3

A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Local Invariants For Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition

International Journal of Computer Vision
Surfacing Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette-Based Isolated Object Recognition through Curvature Scale Space

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
View organization and matching of free-form objects

ISCV '95 Proceedings of the International Symposium on Computer Vision
Matching 3D Models with Shape Distributions

SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Correspondence Matching with Modal Clusters

IEEE Transactions on Pattern Analysis and Machine Intelligence

Robust point correspondence matching and similarity measuring for 3D models by relative angle-context distributions

Image and Vision Computing
Three-dimensional facial feature points matching based on a combined support vector machine

Proceedings of the First International Conference on Internet Multimedia Computing and Service
Iterative 3D point-set registration based on hierarchical vertex signature (HVS)

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

Accurate and robust correspondence calculations are the pre-requisite step in many 3D model query and retrieval process. However, the correspondence problem is particularly difficult for 3D biomedical model surfaces, especially for roundish and approximate symmetric organs such as liver, stomach, kidney etc. In this paper, we define a new feature representation called the Neighborhood Relative Angle context Distribution (NRACD) for each vertex and, based upon it, we apply the Chi-Square Goodness-of-Fit test to establish 3D point correspondence. We further define the similarities between correspondence ready models by Chi-Square test statistic values. The experimental results demonstrate that this approach is efficient and robust for surface point matching and is particularly applicable to the retrieval and analysis of 3D deformable objects.