A method that adaptively extracts the gray-tone distribution of the background of the image without a priori knowledge is described, and the method's performance is shown to be superior when log-transformed image data is used. The image is decomposed into rectangular regions to adaptively extract the background's gray-tone distribution throughout the image. The background distribution of each region is modeled with a left-half and right-half Gaussian to compensate for its asymmetrical nature. Statistical criteria are used to classify each rectangular region as background-homogeneous, object-homogeneous, or uncertain. Measured statistical parameters of background homogeneous regions are propagated throughout the image to estimate the statistics of object-homogeneous regions and uncertain regions. The local background of each region is extracted by using the measured or estimated statistical parameters to compute the left and right shoulder thresholds of the background distribution. A logarithmic transformation implementation for gray-tone image data and a procedure to map log-transformed data into integer-valued histograms are proposed. The background extraction method is shown to yield superior results when log-transformed image data is used. The proposed algorithm is robust by virtue of the logarithmic transformation implementation; it can perform over a wide range of applications without parameter adjustments or human interaction. The algorithm performs successfully whether the image contains objects darker and/or brighter than the background, or no object at all. The method is demonstrated on background-only scenes imaged under different lighting conditions, industrial scenes, and outdoor scenes of moderate complexity that exhibit different smooth backgrounds within the same image.

Date Published: 1 October 1991
PDF: 18 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48378

Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)