Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
3D-2D projective registration of free-form curves and surfaces
Computer Vision and Image Understanding
Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics
Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Registration of CT segmented surfaces and 3-D cardiac electroanatomical maps
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
GPU acceleration of robust point matching
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Medical augment reality using a markerless registration framework
Expert Systems with Applications: An International Journal
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
A novel 3d/2d correspondence building method for anatomy-based registration
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
IPCAI'12 Proceedings of the Third international conference on Information Processing in Computer-Assisted Interventions
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Hi-index | 0.00 |
We present a modification to the iterative closest point algorithm which improves the algorithm's robustness and precision. At the start of each iteration, before point correspondence is calculated between the two feature sets, the algorithm randomly perturbs the point positions in one feature set. These perturbations allow the algorithm to move out of some local minima to find a minimum with a lower residual error. The size of this perturbation is reduced during the registration process. The algorithm has been tested using multiple starting positions to register three sets of data: a surface of a femur, a skull surface and a registration to hepatic vessels and a liver surface. Our results show that, if local minima are present, the stochastic ICP algorithm is more robust and is more precise than the standard ICP algorithm.