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
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Trajectory Space: A Dual Representation for Nonrigid Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Decomposition and dictionary learning for 3D trajectories
Signal Processing
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A new model for describing a three-dimensional (3D) trajectory is introduced in this article. The studied object is viewed as a linear combination of rotatable 3D patterns. The resulting model is now 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. A novel 3DRI decomposition problem consists of estimating the active patterns, their coefficients, their rotations and their shift parameters. Sparsity allows to select few patterns among multiple ones. Based on the sparse approximation principle, a non-convex optimization called 3DRI matching pursuit (3DRI-MP) is proposed to solve this problem. This algorithm is applied to real and simulated data, and compared in order to evaluate its performances.