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| K.V. Mardia, T.J. Hainsworth, "A Spatial Thresholding Method for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 919-927, November, 1988. | |||
| BibTex | x | ||
| @article{ 10.1109/34.9113, author = {K.V. Mardia and T.J. Hainsworth}, title = {A Spatial Thresholding Method for Image Segmentation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {10}, number = {6}, issn = {0162-8828}, year = {1988}, pages = {919-927}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.9113}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - A Spatial Thresholding Method for Image Segmentation IS - 6 SN - 0162-8828 SP919 EP927 EPD - 919-927 A1 - K.V. Mardia, A1 - T.J. Hainsworth, PY - 1988 KW - computerized picture processing; spatial thresholding method; image segmentation; model-based algorithms; median filtering; multispectral k-population images; computerised picture processing; filtering and prediction theory VL - 10 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Several model-based algorithms for threshold selection are presented, concentrating on the two-population univariate case in which an image contains an object and background. It is shown how the main ideas behind two important nonspatial thresholding algorithms follow from classical discriminant analysis. Novel thresholding algorithms that make use of available local/spatial information are then given. It is found that an algorithm using alternating mean thresholding and median filtering provides an acceptable method when the image is relatively highly contaminated, and seems to depend less on initial values than other procedures. The methods are also applicable to multispectral k-population images.
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