TY - GEN
T1 - Pyramid transform and scale-space analysis in image analysis
AU - Mochizuki, Yoshihiko
AU - Imiya, Atsushi
PY - 2012
Y1 - 2012
N2 - The pyramid transform compresses images while preserving global features such as edges and segments. The pyramid transform is efficiently used in optical flow computation starting from planar images captured by pinhole camera systems, since the propagation of features from coarse sampling to fine sampling allows the computation of both large displacements in low-resolution images sampled by a coarse grid and small displacements in high-resolution images sampled by a fine grid. The image pyramid transform involves the resizing of an image by downsampling after convolution with the Gaussian kernel. Since the convolution with the Gaussian kernel for smoothing is derived as the solution of a linear diffusion equation, the pyramid transform is performed by applying a downsampling operation to the solution of the linear diffusion equation.
AB - The pyramid transform compresses images while preserving global features such as edges and segments. The pyramid transform is efficiently used in optical flow computation starting from planar images captured by pinhole camera systems, since the propagation of features from coarse sampling to fine sampling allows the computation of both large displacements in low-resolution images sampled by a coarse grid and small displacements in high-resolution images sampled by a fine grid. The image pyramid transform involves the resizing of an image by downsampling after convolution with the Gaussian kernel. Since the convolution with the Gaussian kernel for smoothing is derived as the solution of a linear diffusion equation, the pyramid transform is performed by applying a downsampling operation to the solution of the linear diffusion equation.
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U2 - 10.1007/978-3-642-34091-8_4
DO - 10.1007/978-3-642-34091-8_4
M3 - Conference contribution
AN - SCOPUS:84867854151
SN - 9783642340901
VL - 7474 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 109
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 15th International Workshop on Theoretical Foundations of Computer Vision
Y2 - 26 June 2011 through 1 July 2011
ER -