Linear programming
Pattern Recognition Letters
The Method of Normalization to Determine Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New One-Parametric Fitting Method for Planar Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time motion analysis with linear programming
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Reliable and Efficient Pattern Matching Using an Affine Invariant Metric
International Journal of Computer Vision
Invariant Fitting of Planar Objects by Primitives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affines Matching planarer Punktmengen mittles Normalisierung über diskrete Momente
Mustererkennung 1996, 18. DAGM-Symposium
On affine registration of planar point sets using complex numbers
Computer Vision and Image Understanding
A novel approach for affine point pattern matching
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
| Hi-index | 0.00 |
This paper presents a general solution for the problem of affine point pattern matching (APPM). Formally, given two sets of two-dimensional points (x,y) which are related by a general affine transformation (up to small deviations of the point coordinates and maybe some additional outliers). Then we can determine the six parameters aik of the transformation using new Hu point-invariants which are invariant with respect to affine transformations. With these invariants we compute a weighted point reference list. The affine parameters can be calculated using the method of the least absolute differences (LAD method) and using linear programming. In comparison to the least squares method, our approach is very robust against noise and outliers. The algorithm works in O(n) average time and can be used for translation and/or rotations, isotropic and non-isotropic scalings, shear transformations and reflections.