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Biologically inspired motion detection neural network models evolved using genetic algorithms.

, , and . AIPR, page 1-8. IEEE Computer Society, (2009)

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Continuous recognition with incremental learning on Grassmann manifolds., and . MWSCAS, page 1477-1480. IEEE, (2017)View Invariant Activity Recognition with Manifold Learning., and . ISVC (2), volume 6454 of Lecture Notes in Computer Science, page 606-615. Springer, (2010)Grassmannian sparse representations., and . J. Electronic Imaging, 24 (3): 033008 (2015)Keypoint matching and image registration using sparse representations., , and . ICIP, page 780-784. IEEE, (2013)Biologically inspired motion detection neural network models evolved using genetic algorithms., , and . AIPR, page 1-8. IEEE Computer Society, (2009)Grassmannian Spectral Regression for Action Recognition., and . ISVC (2), volume 8034 of Lecture Notes in Computer Science, page 189-198. Springer, (2013)3D Action Classification Using Sparse Spatio-temporal Feature Representations., and . ISVC (2), volume 7432 of Lecture Notes in Computer Science, page 166-175. Springer, (2012)Grassmannian Spectral Regression for Learning and Classification., and . Int. J. Artif. Intell. Tools, 24 (4): 1540015:1-1540015:20 (2015)A spatiotemporal descriptor based on radial distances and 3D joint tracking for action classification., and . ICIP, page 769-772. IEEE, (2012)Grassmannian Sparse Representations and Motion Depth Surfaces for 3D Action Recognition., and . CVPR Workshops, page 492-499. IEEE Computer Society, (2013)