Abstract
In this paper we present a neural computation model for histogram based range image segmentation. An optimal thresholding vector for the range histogram is determined. The number of elements in the vector is characterized by the histogram. Since our model is the parallel implementation of maximum interclass variance thresholding, the time for convergence will be much faster. Together with a real-time histogram builder, real time adaptive range image segmentation can be achieved.
The multithresholding criterion is derived from maximizing the interclass variance and hence the average of the c.g. (center of gravity) of two neighboring class pixel values should be equal to the interclass threshold value. The learning (weight matrix evolution) procedure of the neural model is developed based on the above condition. We use a three-layer neural network with binary weight synapses. The number of neurons in the first layer equals to that of the level of the range image and complex number inputs are used because the arguments of second layer outputs represent the c.g. of the class. The third layer neurons receive the argument output of the second layer and give an indication of the reach of the optimum condition.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
P.K.Sahoo, S.Soltani, A.K.C. Wong and Y.C.Chen, “A survey of thresholding techniques,” CVGIP vol. 41, pp233–260, 1988.
D. Bhandari, N.R.Pal and D.D.Majumder, “Fuzzy divergence, probability measure of fuzzy events and image thresholding,” Pattern Recognition Letter, vol.13, pp857–867, 1992.
J.Kitter and J.Illingworth, “Minimum error thresholding,” Pattern Recognition, vol.19, No.1, pp41–47, 1986.
T.Kurita, N.Otsu and N. Abdelmalek, “Maximum likelihood thresholding based on population mixture models,” Pattern Recognition, vol. 25, No.10, pp1231–1240, 1992.
R.Brunelli, “Optimal histogram partitioning using a simulated annealing technique,” Pattern Recognition Letter, vol.13, pp581–586, 1992.
A. Rosenfeld and P.De La Torre, “Histogram concavity analysis as an aid in threshold selection,” IEEE Trans SMC, vol.13, pp231–235, 1983.
N. Otsu, “A threshold selection method from gray level histograms,” IEEE Trans SMC, vol-9, No.1, pp62–66, 1979.
S. Ghosal, R. Mehrotra, “Application of neural networks in segmentation of range images,” IJCNN, vol.3, p297–302, 1992.
R. Hoffman and A.K.Jain, “Segmentation and Classification of range images,” IEEE Trans. PAMI, vol.9, no. 5, pp608–620, 1987.
W.P.Cheung, C.K.Lee and K.C.Li., “Multithresholding for arithmetic logic pattern matching,” Electronics Letters, Vol.29 No.5 pp. 481–483, 1993.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheung, W.P., Lee, C.K., Li, K.C. (1995). Segmentation of range images: A neural network approach. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_261
Download citation
DOI: https://doi.org/10.1007/3-540-59497-3_261
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-59497-0
Online ISBN: 978-3-540-49288-7
eBook Packages: Springer Book Archive