Open AccessROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER
Author(s) -
P. Srestasathiern,
S. Lawawirojwong,
R. Suwantong,
P Phuthong
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 38
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-7-103-2016
Subject(s) - orthogonal matrix , orthogonal transformation , transformation (genetics) , sampling (signal processing) , rotation (mathematics) , mathematics , rotation matrix , matrix (chemical analysis) , transformation matrix , probability distribution , algorithm , uniform distribution (continuous) , artificial intelligence , pattern recognition (psychology) , computer science , computer vision , statistics , orthogonal basis , biochemistry , chemistry , physics , materials science , kinematics , filter (signal processing) , quantum mechanics , classical mechanics , composite material , gene
This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.