8.3.3 Segmentation by Thresholding, Quantization, or Relaxation

Chapter Contents (Back)
Quantization. Relaxation. Segmentation, Thresholds. Segmentation, Binarization. Segmentation, Relaxation. Adaptive Threshold. Thresholding. 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

Sullivan, J.R.[James R.],
Image processing method including image segmentation,
US_Patent4,764,971, Aug 16, 1988
WWW Link. constant contrast or variance BibRef 8808

Khotanzad, A.[Alireza], Bouarfa, A.[Abdelmajid],
Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm,
PR(23), No. 9, 1990, pp. 961-973.
Elsevier DOI 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.
Elsevier DOI 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 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 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.
Elsevier DOI 0001

See also On estimation of the number of image principal colors and color reduction through self-organized neural networks. BibRef

Tseng, D.C.[Din-Chang], Huang, M.Y.[Mao-Yu], Tseng, D.C., and Huang, M.Y.,
Automatic Thresholding Based on Human Visual-Perception,
IVC(11), No. 9, November 1993, pp. 539-548.
Elsevier DOI BibRef 9311

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

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

Keeler, K.,
MAP Representations and Coding-Based Priors for Segmentation,
CVPR91(420-425).
IEEE DOI 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 DOI 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.
Elsevier DOI 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.[Ian], Patel, D.[Devesh], Davies, E.R.[E. Roy],
The Use of Variance and Entropic Thresholding Methods for Image Segmentation,
PR(28), No. 8, August 1995, pp. 1135-1143.
Elsevier DOI BibRef 9508
Earlier: A2, A1, A3:
Foreign object detection via texture analysis,
ICPR94(A:586-588).
IEEE DOI 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.
Elsevier DOI 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.
DOI Link 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.
Elsevier DOI 0401
BibRef

Beghdadi, A., Le Negrate, A., Viaris de Lesegno, P.[Patrick],
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

Phillips, T.Y.[Tsai-Yun], Rosenfeld, A.[Azriel], Sher, A.C.[Allen C.],
O(log n) Bimodality Analysis,
PR(22), No. 6, 1989, pp. 741-746.
Elsevier DOI 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 Link. BibRef

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

Rosin, P.L.[Paul L.],
Unimodal Thresholding,
PR(34), No. 11, November 2001, pp. 2083-2096.
Elsevier DOI 0108
BibRef
Earlier: SCIA99(633-642).
PDF File.
See also Thresholding for Change Detection. BibRef

Yen, J.C., Chang, F.J., and Chang, S.,
A New Criterion for Automatic Multilevel Thresholding,
IP(4), No. 3, March 1995, pp. 370-378.
IEEE DOI A variation on the entropy function to move the log to outside the loop. BibRef 9503

Robinson, D.C.[David C.],
Apparatus and method for segmenting an input image in one of a plurality of modes,
US_Patent5,339,172, August 16, 1994.
WWW Link. BibRef 9408

Pan, H.P.[He-Ping],
Two-Level Global Optimization for Image Segmentation,
PandRS(49), No. 2, 1994, pp. 21-32. Two levels. Pixel and Region. MDL principle. BibRef 9400

Naveen, T., Woods, J.W.,
Subband Finite-State Scalar Quantization,
IP(5), No. 1, January 1996, pp. 150-155.
IEEE DOI BibRef 9601

Ng, W.S., Lee, C.K.,
Comment on Using the Uniformity Measure for Performance-Measure in Image Segmentation,
PAMI(18), No. 9, September 1996, pp. 933-934.
IEEE DOI Thresholding. The measure by Levine and Nazif (
See also Dynamic Measurement of Computer Generated Image Segmentations. ) is the same as that by Otsu (
See also Threshold Selection Method from Grey-Level Histograms, A. ). BibRef 9609

Wiman, H.,
Array Algebra Polynomial Fitting for Image Segmentation,
JMIV(6), No. 1, January 1996, pp. 7-13. 9608
BibRef

Yan, H.[Hong],
Unified Formulation of a Class of Image Thresholding Techniques,
PR(29), No. 12, December 1996, pp. 2025-2032.
Elsevier DOI 9701
BibRef

Chang, J.S.[Jung-Shiong], Liao, H.Y.M.[Hong-Yuan Mark], Hor, M.K.[Maw-Kae], Hsieh, J.W.[Jun-Wei], Chern, M.Y.[Ming-Yang],
New Automatic Multilevel Thresholding Technique for Segmentation of Thermal Images,
IVC(15), No. 1, January 1997, pp. 23-34.
Elsevier DOI 9702
BibRef

Karssemeijer, N.,
A Relaxation Method for Image Segmentation Using a Spatially Dependent Stochastic Model,
PRL(11), 1990, pp. 13-23. BibRef 9000

Pal, S.K., Rosenfeld, A.,
Image Enhancement and Thresholding by Optimization of Fuzzy Compactness,
PRL(7), 1988, pp. 77-86. BibRef 8800

Pal, S.K., Pal, N.R.,
Segmentation Using Contrast and Homogeneity Measures,
PRL(5), 1987, pp. 293-304. BibRef 8700

Pal, N.R., Pal, S.K.,
Image Model, Poisson Distribution and Object Extraction,
PRAI(5), 1991, pp. 459-483. BibRef 9100

Pal, S.K., Pal, N.R.,
Object Extraction from Image Using Higher Order Entropy,
ICPR88(I: 348-350).
IEEE DOI BibRef 8800

Lu, F.S., Wise, G.L.,
A Further Investigation of Max's Algorithm for Optimum Quantization,
Commun(33), 1985, pp. 746-750. BibRef 8500

Swaszek, P.F.,
Uniform Spherical Coordinate Quantization of Spherically Symmetric Sources,
Commun(33), 1985, pp. 518-521. BibRef 8500

Vasquez, G.,
Comments on 'A Simple Approximation for Minimum Mean-Square Error Symmetric Uniform Quantization',
Commun(34), 1986, pp. 298-300. BibRef 8600

Swaszek, P.F., Thomas, J.B.,
Design of Quantizers from Histograms,
Commun(32), 1984, pp. 240-245. BibRef 8400

Scheunders, P.,
A Genetic C-Means Clustering-Algorithm Applied to Color Image Quantization,
PR(30), No. 6, June 1997, pp. 859-866.
Elsevier DOI 9706

See also Local mapping for multispectral image visualization. BibRef

Scheunders, P.,
A Comparison of Clustering Algorithms Applied to Color Image Quantization,
PRL(18), No. 11-13, November 1997, pp. 1379-1384. 9806
BibRef

Scheunders, P.[Paul],
A Genetic Lloyd-Max Image Quantization Algorithm,
PRL(17), No. 5, May 1 1996, pp. 547-556. 9606
BibRef
Earlier:
A Genetic Approach Towards Optimal Color Image Quantization,
ICIP96(III: 1031-1034).
IEEE DOI
See also Least Squares Quantization in PCM. BibRef

Scheunders, P., van Hove, H., Livens, S.,
On the Local Optimality of Image Quantizers,
ICPR96(IV: 664-668).
IEEE DOI 9608
(RUCA, Univ. of Antwerp, B) BibRef

Livens, S.[Stefan], van Roost, C.[Chris], Scheunders, P.[Paul], and van Dyck, D.[Dirk],
Granulometric Segmentation Using a Gradient Convergence Map,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Scheunders, P.,
Joint Quantization and Error Diffusion of Color Images Using Competitive Learning,
VISP(145), No. 2, April 1998, pp. 137-140. 9806
BibRef
Earlier: Add A2: de Backer, S., ICIP97(I: 811-814).
IEEE DOI BibRef

Revankar, S.V.[Shriram V.], Fan, Z.G.[Zhi-Gang],
Image segmentation system,
US_Patent5,767,978, Jun 16, 1998
WWW Link. to render similar regions similarily BibRef 9806

Friel, N.[Nial], Molchanov, I.S.[Ilya S.],
A new thresholding technique based on random sets,
PR(32), No. 9, September 1999, pp. 1507-1517.
Elsevier DOI BibRef 9909

Saha, P.K.[Punam K.], Udupa, J.K.[Jayaram K.],
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity,
PAMI(23), No. 7, July 2001, pp. 689-706.
IEEE DOI 0108
Account for intensity information (histograms) and region homogeneity with a scale-based formulation. Compared with:
See also Maximum Segmented Image Information Thresholding.
See also Fuzzy connectedness and image segmentation. BibRef

Comaniciu, D.[Dorin], Meer, P.[Peter],
Mean Shift: A Robust Approach Toward Feature Space Analysis,
PAMI(24), No. 5, May 2002, pp. 603-619.
IEEE DOI 0205
BibRef
Earlier:
Mean Shift Analysis and Applications,
ICCV99(1197-1203).
IEEE DOI An estimator of the density gradient. Generate regions from values. For the code:
See also Edison: Edge Detection and Image SegmentatiON system. BibRef

Comaniciu, D.[Dorin], Meer, P.[Peter],
Robust Analysis of Feature Spaces: Color Image Segmentation,
CVPR97(750-755).
IEEE DOI 9704
Code, Segmentation. Code, Segmentation, C++. For the C++ code:
HTML Version. Color quantization for segmentation. Map into another feature space. BibRef

Georgescu, B., Shimshoni, I., Meer, P.,
Mean shift based clustering in high dimensions: A Texture Classification Example,
ICCV03(456-463).
IEEE DOI 0311
BibRef

Stanford, D.C.[Derek C.], Raftery, A.E.[Adrian E.],
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC),
PAMI(24), No. 11, November 2002, pp. 1517-1520.
IEEE Abstract. 0211
Segmentation by quantization. BibRef

Jiang, X.Y.[Xiao-Yi], Mojon, D.,
Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,
PAMI(25), No. 1, January 2003, pp. 131-138.
IEEE DOI 0301
Applied to retina images. Find the blood vessels. BibRef

Sund, T., Eilertsen, K.,
An algorithm for fast adaptive image binarization with applications in radiotherapy imaging,
MedImg(22), No. 1, January 2003, pp. 22-28.
IEEE Top Reference. 0304
BibRef

Lin, K.C.[Ku Chin],
Fast image thresholding by finding the zero(s) of the first derivative of between-class variance,
MVA(13), No. 5-6, 2003, pp. 254-262.
WWW Link. 0304
BibRef

Murtagh, F., Starck, J.L.,
Quantization from Bayes factors with application to multilevel thresholding,
PRL(24), No. 12, August 2003, pp. 2001-2007.
Elsevier DOI 0304
BibRef

Murtagh, F., Barreto, D., Marcello, J.,
Decision Boundaries Using Bayes Factors: The Case of Cloud Masks,
GeoRS(41), No. 12, December 2003, pp. 2952-2958.
IEEE Abstract. 0402
BibRef

Murtagh, F.[Fionn], Raftery, A.E.[Adrian E.], Starck, J.L.[Jean-Luc],
Bayesian inference for multiband image segmentation via model-based cluster trees,
IVC(23), No. 6, 1 June 2005, pp. 587-596.
Elsevier DOI 0505
BibRef

Chung, K.L.[Kuo-Liang], Chen, W.Y.[Wan-Yu],
Fast adaptive PNN-based thresholding algorithms,
PR(36), No. 12, December 2003, pp. 2793-2804.
Elsevier DOI 0310
BibRef

Meyer, F.[Fernand],
Levelings, Image Simplification Filters for Segmentation,
JMIV(20), No. 1-2, January-March 2004, pp. 59-72.
DOI Link 0403
BibRef

Hanbury, A.[Allan], Marcotegui, B.[Beatriz],
Morphological segmentation on learned boundaries,
IVC(27), No. 4, 3 March 2009, pp. 480-488.
Elsevier DOI 0804
BibRef
Earlier:
Waterfall Segmentation of Complex Scenes,
ACCV06(I:888-897).
Springer DOI 0601
Image segmentation; Watershed; Waterfall; Normalised cuts; Segmentation evaluation; Volume extinction values BibRef

Zanoguera, M.F.[M. Francisca], Marcotegui, B.[Beatriz], Meyer, F.[Fernand],
A Toolbox for Interactive Segmentation Based on Nested Partitions,
ICIP99(I:21-25).
IEEE DOI BibRef 9900

Demirkaya, O.[Omer], Asyali, M.H.[Musa H.],
Determination of image bimodality thresholds for different intensity distributions,
SP:IC(19), No. 6, July 2004, pp. 507-516.
Elsevier DOI 0409
BibRef

Benabdelkader, S.[Souad], Boulemden, M.[Mohammed],
Recursive algorithm based on fuzzy 2-partition entropy for 2-level image thresholding,
PR(38), No. 8, August 2005, pp. 1289-1294.
Elsevier DOI 0505
Entropy approach for threshold selection. BibRef

Tong, C.S.[Chong Sze], Zhang, Y.P.[Yong-Ping], Zheng, N.N.[Nan-Ning],
Variational Image Binarization and its Multi-Scale Realizations,
JMIV(23), No. 2, September 2005, pp. 185-198.
Springer DOI 0505
BibRef

Yang, Y.[Yong], Zheng, C.X.[Chong-Xun], Lin, P.[Pan],
Spatially Weighted Fuzzy C-Means Clustering Algorithm for Image Thresholding,
GVIP(05), No. V3, 2005, pp. xx-yy
HTML Version. BibRef 0500

Tizhoosh, H.R.[Hamid R.],
Image thresholding using type II fuzzy sets,
PR(38), No. 12, December 2005, pp. 2363-2372.
Elsevier DOI 0510
For comment:
See also Comment on: Image thresholding using type II fuzzy sets. Importance of this method. BibRef

Tizhoosh, H.R.[Hamid R.],
Adaptive lambda-enhancement: Type I versus type II fuzzy implementation,
CIIP09(1-7).
IEEE DOI 0903
BibRef

Tizhoosh, H.R.[Hamid R.],
Interval-valued versus intuitionistic fuzzy sets: Isomorphism versus semantics,
PR(41), No. 5, May 2008, pp. 1829-1830.
Elsevier DOI 0711
BibRef

Shokri, M., Tizhoosh, H.R.,
Q-Lambda -based image thresholding,
CRV04(504-508).
IEEE DOI 0408
BibRef

Othman, A.A.[Ahmed A.], Tizhoosh, H.R.[Hamid R.],
Neural Image Thresholding Using SIFT: A Comparative Study,
ACIVS10(I: 38-49).
Springer DOI 1012
BibRef

Vlachos, I.K.[Ioannis K.], Sergiadis, G.D.[George D.],
Comment on: 'Image thresholding using type II fuzzy sets',
PR(41), No. 5, May 2008, pp. 1827-1828.
Elsevier DOI 0711

See also Image thresholding using type II fuzzy sets. BibRef

Wang, S.T.[Shi-Tong], Chung, F.L.,
Note on the equivalence relationship between Renyi-entropy based and Tsallis-entropy based image thresholding,
PRL(26), No. 14, 15 October 2005, pp. 2309-2312.
Elsevier DOI 0510
BibRef

Blayvas, I.[Ilya], Bruckstein, A.M.[Alfred M.], Kimmel, R.[Ron],
Efficient Computation of Adaptive Threshold Surfaces for Image Binarization,
PR(39), No. 1, January 2006, pp. 89-101.
Elsevier DOI 0512
BibRef
Earlier: CVPR01(I:737-742).
IEEE DOI 0110
BibRef

Sifre-Maunier, L.[Laurence], Taylor, R.G.[Richard G.], Berge, P.[Philippe], Culioli, J.[Joseph], Bonny, J.M.[Jean-Marie],
A global unimodal thresholding based on probabilistic reference maps for the segmentation of muscle images,
IVC(24), No. 10, 1 October 2006, pp. 1080-1089.
Elsevier DOI 0609
Unimodal thresholding; Segmentation; Muscle BibRef

Bazi, Y.[Yakoub], Bruzzone, L.[Lorenzo], Melgani, F.[Farid],
Image thresholding based on the EM algorithm and the generalized Gaussian distribution,
PR(40), No. 2, February 2007, pp. 619-634.
Elsevier DOI 0611
Image thresholding; Expectation-Maximization algorithm; Generalized Gaussian distribution; Genetic algorithms BibRef

Peng, T.G.[Tie-Gen], Wang, Y.H.[Yin-Hua], Wu, T.H.[Ti-Hua],
Mean shift algorithm equipped with the intersection of confidence intervals rule for image segmentation,
PRL(28), No. 2, 15 January 2007, pp. 268-277.
Elsevier DOI 0611
Keywords: Intersection of confidence intervals (ICI); Mean shift; Image segmentation; Kernel function; Bandwidth selection BibRef

Wang, S.T.[Shi-Tong], Chung, F.L.[Fu-Lai], Xiong, F.S.[Fu-Song],
A novel image thresholding method based on Parzen window estimate,
PR(41), No. 1, January 2008, pp. 117-129.
Elsevier DOI 0710
Parzen window; Thresholding; Image segmentation BibRef

Cao, L.[Li], Bao, P.[Paul], Shi, Z.[Zhongke],
The strongest schema learning GA and its application to multilevel thresholding,
IVC(26), No. 5, May 2008, pp. 716-724.
Elsevier DOI 0803
Multilevel thresholding; Otsu method; Kapur method; Genetic algorithms; Schema
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Chabrier, S., Rosenberger, C.[Christophe], Emile, B., Laurent, H.[Helene],
Optimization-Based Image Segmentation by Genetic Algorithms,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0804
BibRef

Huang, D.Y.[Deng-Yuan], Wang, C.H.[Chia-Hung],
Optimal multi-level thresholding using a two-stage Otsu optimization approach,
PRL(30), No. 3, 1 February 2009, pp. 275-284.
Elsevier DOI 0804
Otsu's method; Image segmentation; Multi-level thresholding
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Chen, Y.B.[Yuan Been], Chen, O.T.C.[Oscal T.C.],
Image Segmentation Method Using Thresholds Automatically Determined from Picture Contents,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link 0904
BibRef

Kwon, S.H.[Soon Hak], Jeong, H.C.[Hye Cheun], Seo, S.T.[Suk Tae], Lee, I.K.[In Keun], Son, C.S.[Chang Sik],
Histogram Equalization-Based Thresholding,
IEICE(E91-D), No. 11, November 2008, pp. 2751-2753.
DOI Link 0804
BibRef

Seo, S.T.[Suk Tae], Lee, I.K.[In Keun], Jeong, H.C.[Hye Cheun], Kwon, S.H.[Soon Hak],
Gaussian Kernel-Based Multi-Histogram Equalization,
IEICE(E93-D), No. 5, May 2010, pp. 1313-1316.
WWW Link. 1006
BibRef

Son, C.S.[Chang Sik], Seo, S.T.[Suk Tae], Lee, I.K.[In Keun], Jeong, H.C.[Hye Cheun], Kwon, S.H.[Soon Hak],
Threshold Selection Based on Interval-Valued Fuzzy Sets,
IEICE(E92-D), No. 9, September 2009, pp. 1807-1810.
WWW Link. 0910
BibRef

Seo, S.T.[Suk Tae], Jeong, H.C.[Hye Cheun], Lee, I.K.[In Keun], Son, C.S.[Chang Sik], Kwon, S.H.[Soon Hak],
Plausibility-Based Approach to Image Thresholding,
IEICE(E92-D), No. 10, October 2009, pp. 2167-2170.
WWW Link. 0910
BibRef

Seo, S.T.[Suk Tae], Lee, I.K.[In Keun], Son, S.H.[Seo Ho], Lee, H.G.[Hyong Gun], Kwon, S.H.[Soon Hak],
Co-occurrence Matrix-Based Image Segmentation,
IEICE(E93-D), No. 11, November 2010, pp. 3128-3131.
WWW Link. 1011
BibRef

Coudray, N.[Nicolas], Buessler, J.L.[Jean-Luc], Urban, J.P.[Jean-Philippe],
Robust threshold estimation for images with unimodal histograms,
PRL(31), No. 9, 1 July 2010, pp. 1010-1019.
Elsevier DOI 1004
Automatic thresholding; Image histogram; Unimodal distribution; Edge detection BibRef

Bustince, H., Barrenechea, E., Pagola, M., Fernandez, J., Sanz, J.,
Comment on: 'Image thresholding using type II fuzzy sets'. Importance of this method,
PR(43), No. 9, September 2010, pp. 3188-3192.
Elsevier DOI 1006
Type II fuzzy set; Interval-valued fuzzy set; Interval-valued fuzzy entropy; Fuzzy entropy; Image thresholding
See also Image thresholding using type II fuzzy sets. BibRef

Bhoyar, K.K.[Kishor Keshaorao], Kakde, O.G.[Omprakash G.],
Color Image Segmentation using Fast Fuzzy C-Means Algorithm,
ELCVIA(9), No. No. 1, 2010, pp. xx-yy.
DOI Link 1011
BibRef

Li, Z.Y.[Zuo-Yong], Yang, J.[Jian], Liu, G.H.[Guang-Hai], Cheng, Y.[Yong], Liu, C.C.[Chuan-Cai],
Unsupervised range-constrained thresholding,
PRL(32), No. 2, 15 January 2011, pp. 392-402.
Elsevier DOI 1101
Thresholding; Image segmentation; Human visual perception; Standard deviation; Unsupervised estimation BibRef

Li, Z.Y.[Zuo-Yong], Liu, C.C.[Chuan-Cai], Zhao, C.R.[Cai-Rong], Cheng, Y.[Yong],
An Image Thresholding Method Based on Human Visual Perception,
CISP09(1-4).
IEEE DOI 0910
BibRef

Beheshti, S., Hashemi, M., Sejdic, E., Chau, T.,
Mean Square Error Estimation in Thresholding,
SPLetters(18), No. 2, February 2011, pp. 103-106.
IEEE DOI 1101
BibRef

Atto, A.M.[Abdourrahmane M.], Pastor, D.[Dominique], Mercier, G.[Grégoire],
Wavelet shrinkage: Unification of basic thresholding functions and thresholds,
SIViP(5), No. 1, March 2011, pp. 11-28.
WWW Link. 1103
BibRef

Xu, X.Y.[Xiang-Yang], Xu, S.Z.[Sheng-Zhou], Jin, L.H.[Liang-Hai], Song, E.[Enmin],
Characteristic analysis of Otsu threshold and its applications,
PRL(32), No. 7, 1 May 2011, pp. 956-961.
Elsevier DOI 1101
Image segmentation; Threshold selection; Otsu criterion
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Cuevas, E.[Erik], Zaldivar, D.[Daniel], Pérez-Cisneros, M.[Marco],
Seeking multi-thresholds for image segmentation with Learning Automata,
MVA(22), No. 5, September 2011, pp. 805-818.
WWW Link. 1108
BibRef

Xue, J.H.[Jing-Hao], Titterington, D.M.,
t-Tests, F-Tests and Otsu's Methods for Image Thresholding,
IP(20), No. 8, August 2011, pp. 2392-2396.
IEEE DOI 1108

See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Liu, J.[Jun], Ku, Y.B.[Yin-Bon], Leung, S.Y.[Shing-Yu],
Expectation-maximization algorithm with total variation regularization for vector-valued image segmentation,
JVCIR(23), No. 8, November 2012, pp. 1234-1244.
Elsevier DOI 1211
Gaussian mixture model; Expectation-maximization; Total variation; Unified cost functional; Image segmentation; Vector-valued images; Fast algorithm; Alternative minimization BibRef

Yazid, H.[Haniza], Arof, H.[Hamzah],
Gradient based adaptive thresholding,
JVCIR(24), No. 7, 2013, pp. 926-936.
Elsevier DOI 1309
Image segmentation BibRef

Koosha, M., Hajsadeghi, K., Koosha, M.,
Fine logarithmic adaptive soft morphological algorithm for synthetic aperture radar image segmentation,
IET-IPR(8), No. 2, February 2014, pp. 90-102.
DOI Link 1403
filtering theory BibRef

Cai, H.M.[Hong-Min], Yang, Z.[Zhong], Cao, X.H.[Xin-Hua], Xia, W.M.[Wei-Ming], Xu, X.Y.[Xiao-Yin],
A New Iterative Triclass Thresholding Technique in Image Segmentation,
IP(23), No. 3, March 2014, pp. 1038-1046.
IEEE DOI 1403
image segmentation BibRef

Sarkar, S.[Soham], Das, S.[Swagatam], Chaudhuri, S.S.[Sheli Sinha],
A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution,
PRL(54), No. 1, 2015, pp. 27-35.
Elsevier DOI 1502
Multi-Level image segmentation BibRef

Sadek, S.[Samy], Al-Hamadi, A.[Ayoub],
Entropic Image Segmentation: A Fuzzy Approach Based on Tsallis Entropy,
IJCVSP(5), No. 1, 2015, pp. xx-yy.
WWW Link. 1504
BibRef

Kurisu, K.[Kosei], Suematsu, N.[Nobuo], Iwata, K.[Kazunori], Hayashi, A.[Akira],
A Spatially Correlated Mixture Model for Image Segmentation,
IEICE(E98-D), No. 4, April 2015, pp. 930-937.
WWW Link. 1505
BibRef
Earlier:
Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors,
ACPR13(59-63)
IEEE DOI 1408
Gaussian processes BibRef

Bhardwaj, N.[Neelam], Agarwal, S.[Suneeta], Bhardwaj, V.[Vikash],
An imaging approach for the automatic thresholding of photo defects,
PRL(60-61), No. 1, 2015, pp. 32-40.
Elsevier DOI 1506
Thresholding BibRef

Zhou, J.X.[Jia-Xiang], Li, Z.W.[Zhi-Wei], Fan, C.[Chong],
Improved fast mean shift algorithm for remote sensing image segmentation,
IET-IPR(9), No. 5, 2015, pp. 389-394.
DOI Link 1506
geophysical image processing BibRef

Nguyen, T.V., Lu, C.Y.[Can-Yi], Sepulveda, J., Yan, S.C.[Shui-Cheng],
Adaptive Nonparametric Image Parsing,
CirSysVideo(25), No. 10, October 2015, pp. 1565-1575.
IEEE DOI 1511
feature extraction BibRef

Xu, C.Y.[Chun-Yan], Lu, C.Y.[Can-Yi], Gao, J.B.[Jun-Bin], Zheng, W.[Wei], Wang, T.J.[Tian-Jiang], Yan, S.C.[Shui-Cheng],
Discriminative Analysis for Symmetric Positive Definite Matrices on Lie Groups,
CirSysVideo(25), No. 10, October 2015, pp. 1576-1585.
IEEE DOI 1511
Lie groups BibRef

Sang, Q.[Qian], Lin, Z.L.[Zong-Li], Acton, S.T.[Scott T.],
Learning automata for image segmentation,
PRL(74), No. 1, 2016, pp. 46-52.
Elsevier DOI 1604
Image analysis. Threshold selection. BibRef

Sha, C.S.[Chun-Shi], Hou, J.[Jian], Cui, H.X.[Hong-Xia],
A robust 2D Otsu's thresholding method in image segmentation,
JVCIR(41), No. 1, 2016, pp. 339-351.
Elsevier DOI 1612
Otsu's method
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Gu, Y.[Ying], Xiong, W.[Wei], Wang, L.L.[Li-Lian], Cheng, J.R.[Jie-Rong],
Generalizing Mumford-Shah Model for Multiphase Piecewise Smooth Image Segmentation,
IP(26), No. 2, February 2017, pp. 942-952.
IEEE DOI 1702
Gaussian processes BibRef

Gu, Y.[Ying], Xiong, W.[Wei], Wang, L.L.[Li-Lian], Cheng, J.R.[Jie-Rong], Huang, W.M.[Wei-Min], Zhou, J.Y.[Jia-Yin],
A new approach for multiphase piecewise smooth image segmentation,
ICIP14(4417-4421)
IEEE DOI 1502
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Efficient and robust image segmentation with a new piecewise-smooth decomposition model,
ICIP13(2718-2722)
IEEE DOI 1402
Active contours. Image segmentation BibRef

Singla, A.[Anshu], Patra, S.[Swarnajyoti],
A fast automatic optimal threshold selection technique for image segmentation,
SIViP(11), No. 2, February 2017, pp. 243-250.
WWW Link. 1702
BibRef

Zhang, C., Xie, Y., Liu, D., Wang, L.,
Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO,
IP(26), No. 3, March 2017, pp. 1355-1362.
IEEE DOI 1703
fuzzy set theory BibRef

Zosso, D.[Dominique], Dragomiretskiy, K.[Konstantin], Bertozzi, A.L.[Andrea L.], Weiss, P.S.[Paul S.],
Two-Dimensional Compact Variational Mode Decomposition,
JMIV(58), No. 2, June 2017, pp. 294-320.
Springer DOI 1704
BibRef
Earlier: A2, A1, Only:
Two-Dimensional Variational Mode Decomposition,
EMMCVPR15(197-208).
Springer DOI 1504
BibRef

Pham, D.H., Meignen, S.,
An Adaptive Computation of Contour Representations for Mode Decomposition,
SPLetters(24), No. 11, November 2017, pp. 1596-1600.
IEEE DOI 1710
signal detection, signal reconstruction, time-frequency analysis, AM-FM mode reconstruction, Dirac impulses, adaptive computation, BibRef

Cho, H., Kang, S.J., Kim, Y.H.,
Image Segmentation Using Linked Mean-Shift Vectors and Global/Local Attributes,
CirSysVideo(27), No. 10, October 2017, pp. 2132-2140.
IEEE DOI 1710
Filtering, Histograms, Image color analysis, Image edge detection, Kernel, Merging, image analysis, mean-shift, algorithm BibRef

Forsthoefel Fitzgerald, D.[Dana], Wills, D.S.[D. Scott], Wills, L.M.[Linda M.],
Real-time, parallel segmentation of high-resolution images on multi-core platforms,
RealTimeIP(13), No. 4, December 2017, pp. 685-702.
Springer DOI 1712
BibRef

Forsthoefel, D.[Dana], Wills, D.S.[D. Scott], Wills, L.M.[Linda M.],
Leap segmentation for recovering image surface layout,
Southwest12(153-156).
IEEE DOI 1205
Replace with a map of similar regions, allows gaps BibRef

Pare, S., Bhandari, A.K., Kumar, A., Bajaj, V.,
Backtracking search algorithm for color image multilevel thresholding,
SIViP(12), No. 2, February 2018, pp. 385-392.
Springer DOI 1802
BibRef

Craciun, S.[Stefan], Kirchgessner, R.[Robert], George, A.D.[Alan D.], Lam, H.[Herman], Principe, J.C.[Jose C.],
A real-time, power-efficient architecture for mean-shift image segmentation,
RealTimeIP(14), No. 2, February 2018, pp. 379-394.
Springer DOI 1804
BibRef

Zhang, S.P.[Si-Peng], Jiang, W.[Wei], Satoh, S.[Shin'ichi],
Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm,
IEICE(E101-D), No. 8, August 2018, pp. 2064-2071.
WWW Link. 1808
BibRef

Chung, Y.M.[Yu-Min], Day, S.[Sarah],
Topological Fidelity and Image Thresholding: A Persistent Homology Approach,
JMIV(60), No. 7, September 2018, pp. 1167-1179.
WWW Link. 1808
BibRef

Wang, Z., Xiong, J., Yang, Y., Li, H.,
A Flexible and Robust Threshold Selection Method,
CirSysVideo(28), No. 9, September 2018, pp. 2220-2232.
IEEE DOI 1809
Histograms, Image segmentation, Entropy, Computational modeling, Discrete Fourier transforms, Frequency-domain analysis, slope difference distribution BibRef

Song, J., Cho, H., Yoon, J., Yoon, S.M.,
Structure Adaptive Total Variation Minimization-Based Image Decomposition,
CirSysVideo(28), No. 9, September 2018, pp. 2164-2176.
IEEE DOI 1809
TV, Image decomposition, Image edge detection, Minimization, Smoothing methods, Robustness, Transforms, Image decomposition, total variation minimization BibRef

Jia, H.M.[He-Ming], Peng, X.X.[Xiao-Xu], Song, W.L.[Wen-Long], Oliva, D.[Diego], Lang, C.[Chunbo], Li, Y.[Yao],
Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Jia, H.M.[He-Ming], Lang, C.[Chunbo], Oliva, D.[Diego], Song, W.L.[Wen-Long], Peng, X.X.[Xiao-Xu],
Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Jia, H.M.[He-Ming], Lang, C.B.[Chun-Bo], Oliva, D.[Diego], Song, W.L.[Wen-Long], Peng, X.X.[Xiao-Xu],
Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Stosic, D.[Dusan], Stosic, D.[Darko], Ludermir, T.B.[Teresa Bernarda], Ren, T.I.[Tsang Ing],
Natural image segmentation with non-extensive mixture models,
JVCIR(63), 2019, pp. 102598.
Elsevier DOI 1909
Non-extensive statistics, Image segmentation, q-Gaussians, Finite mixture models BibRef

Kiefer, L., Storath, M., Weinmann, A.,
An Efficient Algorithm for the Piecewise Affine-Linear Mumford-Shah Model Based on a Taylor Jet Splitting,
IP(29), No. 1, 2020, pp. 921-933.
IEEE DOI 1910
Computational modeling, Partitioning algorithms, Approximation algorithms, Estimation, Image segmentation, image edge detection
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Kim, B., Ye, J.C.,
Mumford-Shah Loss Functional for Image Segmentation With Deep Learning,
IP(29), No. 1, 2020, pp. 1856-1866.
IEEE DOI 1912
Image segmentation, Semantics, Neural networks, Minimization, Deep learning, Training data, Unsupervised learning, Mumford-Shah functional
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Raj, A.[Aditya], Gautam, G.[Gunjan], Abdullah, S.N.H.S.[Siti Norul Huda Sheikh], Zaini, A.S.[Abbas Salimi], Mukhopadhyay, S.[Susanta],
Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy,
IVC(91), 2019, pp. 103792.
Elsevier DOI 1912
Multilevel thresholding, Tsallis-Fuzzy entropy, Differential evolution, Otsu entropy, Kapur entropy BibRef

Luo, L.K.[Ling-Kun], Wang, X.F.[Xiao-Fang], Hu, S.Q.[Shi-Qiang], Hu, X.[Xing], Zhang, H.L.[Huan-Long], Liu, Y.H.[Yao-Hua], Zhang, J.[James],
A unified framework for interactive image segmentation via Fisher rules,
VC(35), No. 12, December 2018, pp. 1869-1882.
Springer DOI 1912
BibRef

Zhu, X.Y.[Xian-Yi], Xiao, Y.[Yi], Tan, G.H.[Guang-Hua], Zhou, S.Z.[Shi-Zhe], Leung, C.S.[Chi-Sing], Zheng, Y.[Yan],
GPU-accelerated 2D OTSU and 2D entropy-based thresholding,
RealTimeIP(17), No. 4, August 2020, pp. 993-1005.
Springer DOI 2007

See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Ameer, S.[Salah],
Eigenstructure involving the histogram for image thresholding,
IET-IPR(14), No. 13, November 2020, pp. 3084-3088.
DOI Link 2012
BibRef

Bhandari, A.K., Singh, A., Kumar, I.V.,
Spatial Context Energy Curve-Based Multilevel 3-D Otsu Algorithm for Image Segmentation,
SMCS(51), No. 5, May 2021, pp. 2760-2773.
IEEE DOI 2104
Image segmentation, Histograms, Color, Thresholding (Imaging), Time complexity, Indexes, Energy curve, two-dimensional (2-D) Otsu
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Han, N.N.[Ning-Ning], Li, S.D.[Shi-Dong], Lu, J.[Jian],
Orthogonal Subspace Based Fast Iterative Thresholding Algorithms for Joint Sparsity Recovery,
SPLetters(28), 2021, pp. 1320-1324.
IEEE DOI 2107
Signal processing algorithms, Matching pursuit algorithms, Iterative algorithms, Null space, Convergence, Tuning, orthogonal subspace BibRef

Ehsaeyan, E.[Ehsan], Zolghadrasli, A.[Alireza],
A Multilevel Image Thresholding Method Using the Darwinian Cuckoo Search Algorithm,
IJIG(21), No. 4, October 2021 2021, pp. 2150052.
DOI Link 2110
BibRef

Ehsaeyan, E.[Ehsan], Zolghadrasli, A.[Alireza],
A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding,
IJIG(22), No. 1 2022, pp. 2250012.
DOI Link 2202
BibRef

Liu, C.[Chun], Yang, J.[Jian], Zheng, J.B.[Jiang-Bin], Nie, X.[Xuan],
An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Wang, D.[Dong], Wang, X.P.[Xiao-Ping],
The iterative convolution-thresholding method (ICTM) for image segmentation,
PR(130), 2022, pp. 108794.
Elsevier DOI 2206
Convolution, Thresholding, Image segmentation, Heat kernel BibRef

Yang, X.B.[Xiao-Bao], Wu, J.S.[Jun-Sheng], He, L.[Lang], Ma, S.[Sugang], Hou, Z.Q.[Zhi-Qiang], Sun, W.[Wei],
CPSS-FAT: A consistent positive sample selection for object detection with full adaptive threshold,
PR(141), 2023, pp. 109627.
Elsevier DOI 2306
Object detection, Label assignment, Location quality estimation, Adaptive threshold BibRef

Wang, C.[Cong], Zhou, M.C.[Meng-Chu], Pedrycz, W.[Witold], Li, Z.W.[Zhi-Wu],
Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation,
Cyber(54), No. 1, January 2024, pp. 241-253.
IEEE DOI 2312
BibRef

Xiao, X.[Xu], Wen, Y.[Youwei], Chan, R.[Raymond], Zeng, T.Y.[Tie-Yong],
Image Segmentation Using Bayesian Inference for Convex Variant Mumford-Shah Variational Model,
SIIMS(17), No. 1, 2024, pp. 248-272.
DOI Link 2404
BibRef

Ge, B.[Bin], Li, Y.Y.[Yu-Yang], Liu, H.[Huanhuan], Xia, C.X.[Chen-Xing], Geng, S.[Shuaishuai],
IML-SSOD: Interconnected and multi-layer threshold learning for semi-supervised detection,
JVCIR(103), 2024, pp. 104220.
Elsevier DOI 2409
Semi-supervised, Object detection, Pseudo label, Interconnected learning, Multi-layer threshold BibRef

do Nascimento, M.G.[Marcelo Gennari], Prisacariu, V.A.[Victor Adrian], Fawcett, R.[Roger], Langhammer, M.[Martin],
HyperBlock Floating Point: Generalised Quantization Scheme for Gradient and Inference Computation,
WACV23(6353-6362)
IEEE DOI 2302
Training, Backpropagation, Quantization (signal), Tensors, Costs, Computational modeling, Vision + language and/or other modalities BibRef

Noyel, G.,
Speeding up the Kohler's method of contrast thresholding,
ICIP17(320-324)
IEEE DOI 1803
Acceleration, Complexity theory, Image segmentation, Instruction sets, Morphology, Real-time systems, pattern recognition BibRef

Gall, J.[Juergen], Sawatzky, J.,
Adaptive Binarization for Weakly Supervised Affordance Segmentation,
ACVR17(1383-1391)
IEEE DOI 1802
Image segmentation, Indexes, Semantics, Solid modeling, Supervised learning, Training BibRef

Li, Z.[Zefan], Ni, B.B.[Bing-Bing], Zhang, W.J.[Wen-Jun], Yang, X.K.[Xiao-Kang], Gao, W.[Wen],
Performance Guaranteed Network Acceleration via High-Order Residual Quantization,
ICCV17(2603-2611)
IEEE DOI 1802
approximation theory, filtering theory, image recognition, quantisation (signal), accurate approximation, BibRef