2002-2003 FYP Presentataion CK1 Intelligent Surface Modeler By Yu Wing TAI Kam Lun TANG Advised by Prof. Chi Keung TANG.

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Presentation transcript:

FYP Presentataion CK1 Intelligent Surface Modeler By Yu Wing TAI Kam Lun TANG Advised by Prof. Chi Keung TANG

FYP Presentataion Overview of presentation  Motivation  Tensor Voting Algorithm  Implementation  Results  Conclusion Presented by: Yu Wing TAI Kam Lun TANG theory and applications tensor and voting data representation and communication bunny dragon etc  Motivation  Tensor Voting Algorithm  Implementation  Results  Conclusion

FYP Presentataion Motivation  Theoretical interest –Emulate human visual perception  Applications –3D modeling Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion Overview of presentation  Motivation  Tensor Voting Algorithm  Implementation  Results  Conclusion Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion What is Tensor Voting?  Representation  Constraint propagation  Data communication Presented by: Yu Wing TAI Kam Lun TANG TENSOR VOTING FIELDS VOTING ALGORITHM

FYP Presentataion Presented by: Yu Wing TAI Kam Lun TANG Tensor = Ellipse SMOOTH CURVE POINT JUNCTION + = ELLIPSE (TENSOR)

FYP Presentataion Presented by: Yu Wing TAI Kam Lun TANG - 2D tensor Ball Tensor- 100% uncertainty in all directions Stick Tensor - 100% certainty in normal directions Tensor = Ellipse ball tensor stick tensor -

FYP Presentataion 2D Stick Voting Field  Encode smoothness ? Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion 2D Ball Voting Field –Derived from 2D stick voting field  Rotation and integration Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion Presented by: Yu Wing TAI Kam Lun TANG Voting Algorithm voting = summation of tensor votes accumulated in a neighborhood Each input site propagates its information in a neighborhood += += += +=

FYP Presentataion 3D Tensor Voting pointscurvelssurfels Decompose surface saliency map Feature Extraction surface Encode ballsplatessticks Sparse Tensor Voting stick voting field plate voting field ball voting field tensor tokens Dense Tensor Voting dense tensor map Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion Results  Noisy data  Sparse data  Large scale reconstruction  Efficient neighborhood searching in 3D space  Code Optimization  Qualitative and quantitative analysis Presented by: Yu Wing TAI Kam Lun TANG  Noisy data  Sparse data  Large scale reconstruction  Efficient neighborhood searching in 3D space  Code Optimization  Qualitative and quantitative analysis

FYP Presentataion 100% noise300% noise500% noise1000% noise1500% noise2000% noise3000% noise5000% noise Result: Robustness

FYP Presentataion Large scale reconstruction Presented by: Yu Wing TAI Kam Lun TANG points 1,153,856 triangles

FYP Presentataion Conclusion  Intelligent surface modeler –3D surface description  Tensor voting  Results –Robustness –Large scale reconstruction  Future work –Multiscale feature segmentation and extraction Presented by: Yu Wing TAI Kam Lun TANG

FYP Presentataion Thank you Q&A