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  • 1
    Publication Date: 2014-09-11
    Description: Geometric and probabilistic models for subpixel accuracy are developed. The geometric models bound the error in offset estimation using the pixels in an observed digital straight line. One probabilistic model bounds the estimate of error offset for continuous images. The other model bounds the error for discrete images given that one is in the correct pixel.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Texas A and M Univ. Proc. of the NASA Symp. on Math. Pattern Recognition and Image Analysis; p 327-412
    Format: text
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  • 2
    Publication Date: 2011-08-19
    Description: The area of subpixel accuracy in image registration and edge detection was studied. Two main directions of research were pursued, edge detection and matching based on the digital geometry of edges, and random field models for probablistic analysis of registration error. In the edge detection approach, error bounds and error probabilities were computed using theoretical models. Algorithms were developed and tests on simulated imagery. The methods appear promising for high accuracy edge position estimation and registration, though further refinement of the procedures is required. Using random field models, a statistical measure of the quality of the cross correlation peak as an estimate of the offset between a sensed and a reference image was developed. Simulations were performed to determine the validity of this estimte with real imagery and to study the results of interpolating digital correlation functions to estimate the translation offset to subpixel accuracy.
    Keywords: NUMERICAL ANALYSIS
    Type: Texas A and M Univ. Proc. of the 2nd Ann. Symp. on Math. Pattern Recognition and Image Analysis Program; p 489-594
    Format: text
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