This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Threshold Selection Using Quadtrees
January 1982 (vol. 4 no. 1)
pp. 90-94
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.
Citation:
Angela Y. Wu, Tsai-Hong Hong, Azriel Rosenfeld, "Threshold Selection Using Quadtrees," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 1, pp. 90-94, Jan. 1982, doi:10.1109/TPAMI.1982.4767203
Usage of this product signifies your acceptance of the Terms of Use.