High-resolution navigation on non-differentiable image manifolds Export

@inproceedings{wakin05highres, abstract = {The images generated by varying the underlying articulation parameters of an object (pose, attitude, light source position, and so on) can be viewed as points on a low-dimensional image parameter articulation manifold (IPAM) in a high-dimensional ambient space. In this paper, we develop theory and methods for the inverse problem of estimating, from a given image on or near an IPAM, the underlying parameters that produced it. Our approach is centered on the observation that, while typical image manifolds are not differentiable, they have an intrinsic multiscale geometric structure. In fact, each IPAM has a family of approximate tangent spaces, each one good at a certain resolution. Putting this structural aspect to work, we develop a new algorithm for high-accuracy parameter estimation based on a coarse-to-fine Newton iteration through the family of approximate tangent spaces. We test the algorithm in several idealized registration and pose estimation problems.}, author = {Wakin, M. B. and Donoho, D. L. and Choi, Hyeokho and Baraniuk, R. G.}, booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on}, citeulike-article-id = {3859752}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICASSP.2005.1416493}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1416493}, doi = {10.1109/ICASSP.2005.1416493}, journal = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on}, keywords = {donoho, image, manifold, onur2009}, pages = {v/1073--v/1076 Vol. 5}, posted-at = {2009-06-05 17:57:44}, priority = {2}, title = {High-resolution navigation on non-differentiable image manifolds}, url = {http://dx.doi.org/10.1109/ICASSP.2005.1416493}, volume = {5}, year = {2005} }

TY - CONF ID - wakin05highres L3 - citeulike-article-id:3859752 N2 - The images generated by varying the underlying articulation parameters of an object (pose, attitude, light source position, and so on) can be viewed as points on a low-dimensional image parameter articulation manifold (IPAM) in a high-dimensional ambient space. In this paper, we develop theory and methods for the inverse problem of estimating, from a given image on or near an IPAM, the underlying parameters that produced it. Our approach is centered on the observation that, while typical image manifolds are not differentiable, they have an intrinsic multiscale geometric structure. In fact, each IPAM has a family of approximate tangent spaces, each one good at a certain resolution. Putting this structural aspect to work, we develop a new algorithm for high-accuracy parameter estimation based on a coarse-to-fine Newton iteration through the family of approximate tangent spaces. We test the algorithm in several idealized registration and pose estimation problems. JF - Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on EP - v/1076 Vol. 5 TI - High-resolution navigation on non-differentiable image manifolds VL - 5 T2 - Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on SP - v/1073 KW - donoho KW - image KW - manifold KW - onur2009 AU - Wakin, MB AU - Donoho, DL AU - Choi, Hyeokho AU - Baraniuk, RG PY - 2005/// UR - http://dx.doi.org/10.1109/ICASSP.2005.1416493 ER -