Issue No. 01 - January (1982 vol. 4)
Angela Y. Wu , Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Tsai-Hong Hong , Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Azriel Rosenfeld , Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
A simple class of piecewise constant approximations to an image is constructed as follows: start with the entire image, subdivide it into quadrants if its gray level standard deviation is high, and repeat the process for each quadrant. This yields a decomposition of the image into blocks, each having low standard deviation, so that each of them can be approximated by a constant value, namely, its mean. The histogram of this approximated image tends to have sharper peaks than that of the original image since the block averaging reduces the variability of the gray levels within homogeneous regions. A possible way of further improving the histogram is based on the fact that small blocks tend to occur near region borders; thus, suppressing these blocks should tend to deepen the valleys on the histogram, making threshold selection (to separate regions of different types) easier. Conversely, the histogram of the small blocks only represents a population of pixels near region borders, and if there are only two types of regions (e.g., objects and background), the mean of this histogram should be a useful threshold for separating them; but in practice, this method is not very reliable since background fluctuations also give rise to border pixels.
A. Rosenfeld, T. Hong and A. Y. Wu, "Threshold Selection Using Quadtrees," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 4, no. , pp. 90-94, 1982.