Keith Price Bibliography Segmentation by Thresholding, Quantization, or Relaxation

8.3.3 Segmentation by Thresholding, Quantization, or Relaxation

Chapter Contents (Back)
Quantization. Relaxation. Segmentation, Thresholds. Segmentation, Binarization. Segmentation, Relaxation. Adaptive Threshold. Multiple Thresholds. See also Binarization -- Threshold selection for documents, Character Enhancement.

Conners, R.W., and Harlow, C.A.,
Equal Probability Quantizing and Texture Analysis of Radiographic Images,
CGIP(8), 1978, pp. 447-463. Segmentation, Texture. BibRef 7800

Smith, R.C., Rosenfeld, A.,
Thresholding Using Relaxation,
PAMI(3), No. 5, September 1981, pp. 598-605. See also Shape Segmentation Using Relaxation. BibRef 8109

Richards, J.A., Landgrebe, D.A., and Swain, P.H.,
Supervised Pixel Relaxation Labeling as a Means for Utilizing Ancillary Information in the Classification of Remote Sensing Image Data,
RSE(12), 1982, pp. 463-477. BibRef 8200

Richards, J.A., Landgrebe, D.A., and Swain, P.H.,
Pixel Labeling by Supervised Probabilistic Relaxation,
PAMI(3), No. 2, March 1981, pp. 188-191. BibRef 8103

Richards, J.A., Landgrebe, D.A., and Swain, P.H.,
Overcoming Accuracy Deterioration in Pixel Relaxation Labeling,
ICPR80(61-65). BibRef 8000

Pal, S.K., King, R.A., and Hashim, A.A.,
Automatic Grey Level Thresholding Through Index of Fuzziness and Entropy,
PRL(1), 1983, pp. 141-146. BibRef 8300

Reddi, S.S., Rudin, S.F., and Keshavan, H.R.,
An Optimal Multiple Threshold Scheme for Image Segmentation,
SMC(14), No. 4, July/August 1984, pp. 661-665. Segmentation, Quantization. Iterative technique to choose the optimal threshold values so that the mapping of the image values to the averages of the thresholds results in the minimum error. It is a simple technique that seems to get all there is in one histogram, there are references to other origins for the basic idea. BibRef 8407

Cohen, M.[Martin],
Explicit Derivation and Analysis of an Optimal Multiple Threshold Scheme,
NTRC Report#85-12R, Northrop Research and Technology Center, 1985. This explicitly derives a technique for the See also Optimal Multiple Threshold Scheme for Image Segmentation, An. technique for multiple thresholds. BibRef 8500

Khotanzad, A., and Bouarfa, A.,
Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm,
PR(23), No. 9, 1990, pp. 961-973.
WWW Version. Segmentation, Histogram. Clustering. Use mode analysis of the multi-dimensional histogram, find the clusters. BibRef 9000

Rodriguez, A.A.[Arturo A.], Mitchell, O.R.[O. Robert],
Image Segmentation by Succesive Background Extraction,
PR(24), No. 5, 1991, pp. 409-420.
WWW Version. BibRef 9100

Mitchell, O.R., and Lutton, S.M.,
Segmentation and Classification of Targets in FLIR Imagery,
DARPAN78(59-65). BibRef 7800

Lutton, S.M., and Mitchell, O.R.,
Adaptive Segmentation of Unique Objects,
ICPR80(548-550). BibRef 8000

Ackah-Miezan, A., and Gagalowicz, A.,
Discrete Models for Energy-Minimizing Segmentation,
ICCV93(200-207).
IEEE DOI Link Segment the image and generate an approximation to it (values for the regions). BibRef 9300

Chou, P.B., and Brown, C.M.,
The Theory and Practice of Bayesian Image Labeling,
IJCV(4), No. 3, 1990, pp. 185-210.
Springer DOI Link Bayes Nets. BibRef 9000
Earlier:
Multimodal Reconstruction and Segmentation with Markov Random Fields and HCF Optimization,
DARPA88(214-221). BibRef
And:
Probabilistic Information Fusion for Multi-Modal Image Segmentation,
IJCAI87(779-782). Segmentation, Histogram. BibRef

Chen, P.B., Brown, C.M.,
Multi-Modal Segmentation Using Markov Random Fields,
DARPA87(663-670). BibRef 8700

Postaire, J.G., Ameziane, M.,
A Pattern Classification Approach to Multilevel Thresholding for Image Segmentation,
CVIP92(307-328). BibRef 9200

Papamarkos, N., Gatos, B.,
A New Approach For Multilevel Threshold Selection,
GMIP(56), No. 5, September 1994, pp. 357-370. BibRef 9409

Papamarkos, N., Strouthopoulos, C., Andreadis, I.,
Multithresholding of color and gray-level images through a neural network technique,
IVC(18), No. 3, February 2000, pp. 213-222.
WWW Version. 0001
See also On estimation of the number of image principal colors and color reduction through self-organized neural networks. BibRef

Tseng, D.C., and Huang, M.Y.,
Automatic Thresholding Based on Human Visual-Perception,
IVC(11), No. 9, November 1993, pp. 539-548.
WWW Version. BibRef 9311

Tseng, D.C., Chang, C.H.,
Color segmentation using perceptual attributes,
ICPR92(III:228-231).
IEEE DOI Link 9208
BibRef

Banerjee, S.[Saibal], Rosenfeld, A.[Azriel],
MAP Estimation of Piecewise Constant Digital Signals,
CVGIP(57), No. 1, January 1993, pp. 63-80.
WWW Version. BibRef 9301

Keeler, K.,
MAP Representations and Coding-Based Priors for Segmentation,
CVPR91(420-425).
IEEE Abstract. IEEE Top Reference. Choose the parameters in the stocastic process that created the image. BibRef 9100

Kundu, A.,
A Quantization Approach to Image Segmentation,
Draft1988. This did the same as the earlier See also Optimal Multiple Threshold Scheme for Image Segmentation, An. but did try to do multiple thresholds all at once. BibRef 8800

Mardia, K.V., and Hainsworth, T.J.,
A Spatial Thresholding Method for Image Segmentation,
PAMI(10), No. 6, November 1988, pp. 919-927.
IEEE Abstract. IEEE Top Reference.
WWW Version. Segmentation, Binarization. A heavily statistical based analysis for the two class case. Generate segmentations and apply a spatial (median) processing to correct the errors. BibRef 8811

Arnulfo, P.[Perez], and Gonzalez, R.C.,
An Iterative Thresholding Algorithm for Image Segmentation,
PAMI(9), No. 6, November 1987, pp. 742-751. Segmentation, Binarization. Segmentation, Histogram. This method is designed for bimodal distributions and works in a raster format so that local variations in overall lighting can be handled. It computes an adaptive threshold by row scan or column scan and then ORs the result. BibRef 8711

Davis, L.S., Rosenfeld, A., and Weszka, J.S.,
Region Extraction by Averaging and Thresholding,
SMC(5), May 1975, pp. 383-388. Smoothing. Local smoothing before thresholding to reduce the effects of texture. See also Note on Thinning, A. BibRef 7505

Narayanan, K.A., O'Leary, D.P.[Dianne P.], and Rosenfeld, A.,
Image Smoothing and Segmentation by Cost Minimization,
SMC(12), 1982, pp. 91-96. BibRef 8200

Narayanan, K.A., O'Leary, D.P.[Dianne P.], and Rosenfeld, A.,
Multi-Resolution Relaxation,
PR(16), No. 2, 1983, pp. 223-230.
WWW Version. Relaxation. First find the solution at a low resolution, then apply a few iterations at a higher resolution, thus reducing the number of high resolution iterations. BibRef 8300

White, J.M., and Rohrer, G.D.,
Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction,
IBMRD(27), No. 4, July 1983, pp. 400-411. OCR. Character Recognition. A dynamic thresholding technique. BibRef 8307

Scott, K.C.[Kevin C.],
System and method for bidirectional adaptive thresholding,
US_Patent5,313,533, May 17, 1994.
HTML Version. BibRef 9405

Venkateswarlu, N.B.,
Implementation of Some Image Thresholding Algorithms on a Connection Machine-200,
PRL(16), No. 7, July 1995, pp. 759-768. BibRef 9507

Hannah, I., Patel, D., Davies, E.R.,
The Use of Variance and Entropic Thresholding Methods for Image Segmentation,
PR(28), No. 8, August 1995, pp. 1135-1143.
WWW Version. BibRef 9508

Patel, D., Hannah, I., Davies, E.R.,
Foreign object detection via texture analysis,
ICPR94(A:586-588).
IEEE DOI Link 9410
BibRef

Patel, D., Davies, E.R., Hannah, I.,
The Use of Convolution-Operators for Detecting Contaminants in Food Images,
PR(29), No. 6, June 1996, pp. 1019-1029.
WWW Version. 9606
BibRef

Davies, E.R.,
Stable bi-level and multi-level thresholding of images using a new global transformation,
IET-CV(2), No. 2, June 2008, pp. 60-74.
WWW Version. 0905
BibRef

Huang, L.K.[Liang-Kai], Wang, M.J.J.[Mao-Jiun J.],
Image thresholding by minimizing the measures of fuzziness,
PR(28), No. 1, January 1995, pp. 41-51.
WWW Version. 0401
BibRef

Beghdadi, A., LeNegrate, A., Delesegno, P.V.,
Entropic Thresholding Using a Block Source Model,
GMIP(57), No. 3, May 1995, pp. 197-205. BibRef 9505

Messelodi, S., Modena, C.M.,
Context Driven Text Segmentation and Recognition,
PRL(17), No. 1, January 10 1996, pp. 47-56. BibRef 9601

Philips, T.Y., Rosenfeld, A., and Sher, A.C.,
O(log n) Bimodality Analysis,
PR(22), No. 6, 1989, pp. 741-746.
WWW Version. BibRef 8900

Bhattacharya, P., Yan, Y.K.,
Iterative Histogram-Modification of Gray Images,
SMC(25), No. 3, March 1995, pp. 521-523. BibRef 9503

Venkatesh, S., Rosin, P.L.,
Dynamic Threshold Determination by Local and Global Edge Evaluation,
GMIP(57), No. 2, March 1995, pp. 146-160. BibRef 9503
Earlier: SPIE(1964), 1993, pp. 40-50. Code, Segmentation. The code is available on the vision list archive:
WWW Version. BibRef

Rosin, P.L.,
Edges: Saliency Measures and Automatic Thresholding,
MVA(9), No. 4, 1997, pp. 139-159.
HTML Version. BibRef 9700
Earlier: Techical note No. I.95.58 TRInstitute of Remote Sensing Applications, Ispra Italy., 1995. Extensions of the GMIP paper above.
PDF Version. BibRef