Gaussian Blob Trackers

For more info about our services contact : help@bestpfe.com

Table of contents

1 Introduction 
2 Asynchronous Part-based Shape Tracking 
2.1 Introduction
2.2 Stream of Visual Events
2.3 Part-Based Shape Tracking
2.3.1 Gaussian Blob Trackers
2.3.2 Spring-Like Connections
2.3.3 Using the energy as a matching criterion
2.3.4 Remarks
2.3.5 Global algorithm
2.4 Results
2.4.1 Tracking a planar grid
2.4.2 Face tracking
2.4.3 Computational Time
2.5 Discussion
3 Event-Based Line and Segment Detection 
3.1 Introduction
3.2 Event-Based Line Detection
3.2.1 Event-Based Visual Flow
3.2.2 Event-Based Least-Squares Line Fitting
3.2.3 Optimization strategy
3.2.4 Event-Based Line Detection Algorithm
3.3 Event-Based Segment Detection
3.3.1 Activity of each pixel
3.3.2 Generation of Endpoint Events
3.3.3 Event-Based Segment Detection Algorithm
3.4 Results
3.4.1 Controlled scene
3.4.2 Urban scenes
3.4.3 Computational time
3.5 Discussion
4 Event-Based 3D Pose Estimation 
4.1 Introduction
4.2 Event-based 3D pose estimation
4.2.1 Problem formulation
4.2.2 Rotation formalisms
4.2.3 2D edge selection
4.2.4 3D matching
4.2.5 Rigid motion estimation
4.2.6 Global algorithm
4.3 Experiments
4.3.1 Icosahedron
4.3.2 House
4.3.3 2D matching using Gabor events
4.3.4 Fast spinning object
4.3.5 Degraded temporal resolution
4.3.6 Computational time
4.4 Discussion
5 An Event-Based Solution to the Perspective-n-Point Problem 
5.1 Introduction
5.2 Event-based solution to the PnP problem
5.2.1 Problem Description
5.2.2 Rotation formalisms
5.2.3 Object-space collinearity error
5.2.4 Optimal Translation
5.2.5 Rotation
5.2.6 Global algorithm
5.3 Results
5.3.1 Synthetic scene
5.3.2 Real recordings
5.3.3 Computational time
5.4 Discussion
6 Conclusion and Future Perspectives 
6.1 Conclusion
6.2 Future Perspectives