TY - GEN

T1 - Geometry and Radiometry Invariant Matched Manifold Detection and Robust Homography Estimation

AU - Yavo, Ziv

AU - Francos, Joseph M.

N1 - Publisher Copyright:
© 2018 IEEE.

PY - 2018/8/29

Y1 - 2018/8/29

N2 - We elaborate on the problem of robust homography estimation based on a novel framework of geometry and radiometry invariant matched manifold detection: Any two observations on the same planar surface are related through a geometric transformation described by a homography, and some radiometric transformation. Using the proposed approach the surface image is tessellated into tiles, such that locally on each tile, the geometric transformation is approximately affine, and the radiometric transformation is monotone. Applying to each of the observations on a surface tile, the radiometry invariant universal manifold embedding (RIUME) operator, the set of all possible observations on that tile is mapped to a single linear subspace of some high dimensional Euclidean space-invariant to monotonic amplitude transformations, and to affine geometric transformations. Thus, by tessellating the observed surface into a set of tiles and matching each tile using the RIUME matched manifold detector to the hypothesized corresponding tile in the other observation on that surface, an efficient method for robust and dense matching of large patches on different observations of the surface is established. Due to the high accuracy of the obtained tile matches, the outliers problem is eliminated. Hence a linear algorithm like the DLT yields accurate estimates of the homography parameters.

AB - We elaborate on the problem of robust homography estimation based on a novel framework of geometry and radiometry invariant matched manifold detection: Any two observations on the same planar surface are related through a geometric transformation described by a homography, and some radiometric transformation. Using the proposed approach the surface image is tessellated into tiles, such that locally on each tile, the geometric transformation is approximately affine, and the radiometric transformation is monotone. Applying to each of the observations on a surface tile, the radiometry invariant universal manifold embedding (RIUME) operator, the set of all possible observations on that tile is mapped to a single linear subspace of some high dimensional Euclidean space-invariant to monotonic amplitude transformations, and to affine geometric transformations. Thus, by tessellating the observed surface into a set of tiles and matching each tile using the RIUME matched manifold detector to the hypothesized corresponding tile in the other observation on that surface, an efficient method for robust and dense matching of large patches on different observations of the surface is established. Due to the high accuracy of the obtained tile matches, the outliers problem is eliminated. Hence a linear algorithm like the DLT yields accurate estimates of the homography parameters.

KW - Homography estimation

KW - Manifold learning

KW - Matched manifold detection

KW - Principal angles

KW - Robust estimation

UR - http://www.scopus.com/inward/record.url?scp=85053849699&partnerID=8YFLogxK

U2 - 10.1109/SSP.2018.8450683

DO - 10.1109/SSP.2018.8450683

M3 - Conference contribution

AN - SCOPUS:85053849699

SN - 9781538615706

T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

SP - 169

EP - 173

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -