Title :
Statistical morphological filters for binary image processing
Author :
Regazzoni, C.S. ; Venetsanopoulos, A.N. ; Foresti, G.L. ; Vernazza, G.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
Abstract :
A new class of statistical morphological operators for binary image processing is introduced. These operators are based on a digitized version of the mean field approximation. The main advantage of the new operators is provided by the capability of taking into account both noise and shape information. Binary statistical dilation (BSD) and binary statistical erosion (BSE) are considered as a case study. Extensivity properties of BSD and BSE are also discussed
Keywords :
digital filters; image processing; mathematical morphology; noise; set theory; statistical analysis; binary image processing; binary statistical dilation; binary statistical erosion; digitized version; extensivity properties; mean field approximation; noise; shape information; statistical morphological filters; statistical morphological operators; Bayesian methods; Educational institutions; Filters; Image processing; Lattices; Morphology; Noise level; Noise shaping; Probability; Shape;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389550
