On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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This paper illustrates a new optical flow estimation technique, which builds upon a genetic algorithm (GA). First, the current frame is segmented into generic shape regions, using only brightness information. For each region a two-parameter motion model is estimated using a GA. The fittest individuals identified at the end of this step are used to initialise the population of the second step of the algorithm, which estimates a six-parameter affine motion model, again using a GA. The proposed method is compared against a multiresolution version of the well-known Lukas-Kanade differential algorithm. It proved to yield the same or better results in term of energy of the residual error, yet providing a compact representation of the optical flow, making it particularly suitable to video coding applications.