A fast algorithm for statistically optimized orientation estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Signal and noise adapted filters for differential motion estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
| Hi-index | 0.00 |
The problem of estimating local motion in the presence of noise deserves a thorough system-theoretical analysis. In current textbooks, the predominant procedures are classified into differential ('optical flow') approaches and matching (or correlation) methods. From a system-theoretical point of view, both classes can be interpreted as a set of linear filters followed by a simple nonlinear operation. However, neither the definition of motion in terms of spatiotemporal gradients nor the minimization of loss functions on two subsequent image patches captures the full essence of what motion means, i.e. the preference direction of a spatiotemporal signal of reduced intrinsic dimensionality embedded in noise. We will discuss the elements that influence the possible precision of local motion estimation methods, and describe estimation-theoretic approaches which subsume traditional methods, on one hand, and give perspectives for signal-dependent optimization, on the other hand.