On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Motion Field and Optical Flow: Qualitative Properties
IEEE Transactions on Pattern Analysis and Machine 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
The computation of optical flow
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new object motion estimation technique for video images, based on a genetic algorithm
IEEE Transactions on Consumer Electronics
Optical flow using color information: preliminary results
Proceedings of the 2008 ACM symposium on Applied computing
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This paper illustrates a new optical flow estimation technique that builds upon a genetic algorithm (GA). First, the current frame is segmented into generic shape regions, using only luminance and color 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 initialize 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 with a multi-resolution version of the well-known Lucas-Kanade differential algorithm. Our simulations demonstrate that, with respect to Lucas-Kanade, it significantly reduces the energy of the motion-compensated residual error.