Image Thresholding by Indicator Kriging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry Refinement of 3D Surfaces Using Kriging
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Texture interpolation using ordinary kriging
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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Statistical Kriging models for spatial data yield optimal linear estimates of unknown samples. In this work, we present a collection of statistical models defined over regions from a dyadic partition of the discrete Fourier spectrum. Spectral covariance models of the 2D Fast Fourier Transform (FFT) of aerial images allow for Kriging interpolation of magnitude and phase spectra from a small number of spectral samples. The reconstructed spectral components are compared to other widely used 2D interpolation algorithms (cubic splines, nearest neighbor, and bilinear interpolators). We approach this problem by exploring the magnitude and phase spectra independently.