Abstract
The theory of the linear hypothesis model, ANOVA, experimental designs, and robustized stochastic approximation minimum variance least squares (SAMVLS) are united and applied in a pattern recognition framework to edge element detection and enhancement of large arrays of three-dimensional pictorial data. New three-dimensional recursive parallelepiped masks (TDRPM) suitable for real-time parallel processing and detection of stationary and moving edge elements are developed from multiple pictures taken of a scene in unspecified noise. The TDPRM is implemented by SAMVLS as a 2 × 2 × k and by ANOVA as a 2 × 2 × 5 mask. The concept of relative sensitivity efficiency (RSE) is introduced to allow comparisons with larger two-dimensional masks. Computer simulations verify the theory and demonstrate the successful performance of TDPRM either as a stationary or a moving-edge detector.
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