Rotation invariant non-rigid shape matching in cluttered scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Stafflines pattern detection using the swarm intelligence algorithm
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
| Hi-index | 0.00 |
We formulate contour correspondence as a Quadratic Assignment Problem (QAP), incorporating proximity information. By maintaining the neighborhood relation between points this way, we show that better matching results are obtained in practice. We propose the first Ant Colony Optimization (ACO) algorithm specifically aimed at solving the QAP-based shape correspondence problem. Our ACO framework is flexible in the sense that it can handle general point correspondence, but also allows extensions, such as order preservation, for the more specialized contour matching problem. Various experiments are presented which demonstrate that this approach yields high-quality correspondence results and is computationally efficient when compared to other methods.