Validation and Verification of Neural Network Systems,
2006.
Dataset, Segmentation.
HTML Version. Annotated data.
LHI Segmentation Dataset,
Subset of larger dataset.
Online2008
HTML Version.
Dataset, Segmentation.
See also Lotus Hill Institute.
BibRef
0800
Ranade, S., and
Prewitt, J.M.S.,
A Comparison of Some Segmentation Algorithms for Cytology,
ICPR80(561-564).
Survey, Segmentation.
Evaluation, Segmentation.
Segmentation, Histogram.
Segmentation, Survey.
Relaxation.
Segmentation, Evaluation. Compares 5 segmentation method for cell segmentation.
BibRef
8000
Hartley, R.L.,
Wang, C.Y.,
Kitchen, L., and
Rosenfeld, A.,
Segmentation of FLIR Images: A Comparative Study,
SMC(12), No. 4, July/August 1982, pp. 553-566.
BibRef
8207
Earlier:
DARPA82(323-341).
FLIR.
Evaluation, Segmentation.
Segmentation, Evaluation.
Relaxation. Compares super slice, pyramid spot detection, relaxation (2),
pyramid linking, and super spike. Superspike won. 88% of the
targets and 1.6 false alarms per true target (the second number
seems excessive?) using segmentation alone.
BibRef
Weszka, J.S.,
A Survey of Threshold Selection Techniques,
CGIP(7), No. 2, April 1978, pp. 259-265.
WWW Version.
Threshold Selection, Survey.
Survey, Segmentation.
Survey, Threshold Selection.
Segmentation, Thresholds.
Segmentation, Survey.
BibRef
7804
Gurari, E.M., and
Wechsler, H.,
On the Difficulties Involved in the Segmentation of Pictures,
PAMI(4), No. 3, May 1982, pp. 304-306.
BibRef
8205
Sahoo, P.K.,
Soltani, S.,
Wong, A.K.C., and
Chen, Y.C.,
A Survey of Thresholding Techniques,
CVGIP(41), No. 2, February 1988, pp. 233-260.
WWW Version.
Survey, Segmentation.
Survey, Threshold Selection.
Segmentation, Thresholds.
Threshold Selection, Survey. An update of
See also Survey of Threshold Selection Techniques, A. Analytic analysis of thresholding results.
BibRef
8802
Pal, N.R., and
Pal, S.K.,
A Review on Image Segmentation Techniques,
PR(26), No. 9, September 1993, pp. 1277-1294.
WWW Version.
PDF Version.
Survey, Segmentation.
Evaluation, Segmentation.
BibRef
9309
Nazif, A.M., and
Levine, M.D.,
Dynamic Measurement of Computer Generated Image Segmentations,
PAMI(7), No. 2, March 1985, pp. 155-164.
(McGill)
Evaluation, Segmentation.
The measurement of parameters that indicate how good the
segmentation is currently, are fed back to change the segmentation
so that it is optimized for the criterion being applied. This is a
companion paper with the following paper on a rule-based
segmentation method.
BibRef
8503
Nazif, A.M., and
Levine, M.D., (McGill)
Low Level Image Segmentation: An Expert System,
PAMI(6), No. 5, September 1984, pp. 555-577.
BibRef
8409
And:
Authors reply:
PAMI(8), No. 5, September 1986, pp. 676.
Segmentation, Expert system.
Segmentation, Evaluation. An expert system for segmentation. There are comparisons with
other methods (split and merge [With errors, see the comments
paper.] and histogram). The results are not overwhelming, they
look more like what would be expected with low tolerances for a
histogram based approach. Low level implies data driven rather
than model driven.
Analysis in:
See also Comment on Using the Uniformity Measure for Performance-Measure in Image Segmentation.
BibRef
Pavlidis, T.,
Comments on 'Low Level Image Segmentation: An Expert System',
PAMI(8), No. 5, September 1986, pp. 675-676.
BibRef
8609
Levine, M.D., and
Nazif, A.M.,
Rule-Based Image Segmentation: A Dynamic Control Strategy Approach,
CVGIP(32), No. 1, October 1985, pp. 104-126.
WWW Version.
Segmentation, Expert system. Control aspects of rule based segmentation
See also Low Level Image Segmentation: An Expert System.
BibRef
8510
Levine, M.D., and
Nazif, A.M.,
An Optimal Set of Image Segmentation Rules,
PRL(2), 1984, pp. 243-248.
BibRef
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Yasnoff, W.A.[William A.],
Mui, J.K.[Jack K.],
Bacus, J.W.[James W.],
Error Measures for Scene Segmentation,
PR(9), No. 4, 1977, pp. 217-231.
Evaluation, Segmentation.
Segmentation, Evaluation.
WWW Version. Subjective methods are inadequate for evaluations. Objective
measures created by correlation with human observation of multiple
segmentations of some scenes and multiple scenes with same
segmentation technique. Two measures are: percentage of area
misclassified (P); pixel error distance (sum of D^2 of each error
pixel (distance to the nearest correct pixel, SQRT of it divided by
image area)). Both can be helpful, but P is not always good since
there is no spatial information.
BibRef
7700
Jiang, H.,
Toriwaki, J.,
Suzuki, H.,
Comparative Performance Evaluation of Segmentation Methods Based
on Region Growing and Division,
SCJ(24), No. 13, 1993, pp. 28-42.
Evaluation, Segmentation.
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Zhang, Y.J.,
A Survey on Evaluation Methods for Image Segmentation,
PR(29), No. 8, August 1996, pp. 1335-1346.
WWW Version.
9608
Evaluation, Segmentation.
Survey, Segmentation.
Segmentation, Evaluation.
BibRef
Kaushal, T.P.,
Towards Visually Convincing Image Segmentation,
IVC(10), No. 9, November 1992, pp. 617-624.
WWW Version.
BibRef
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Zhang, Y.J.,
Evaluation and Comparison of Different Segmentation Algorithms,
PRL(18), No. 10, October 1997, pp. 963-974.
9802
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Beauchemin, M.,
Thomson, K.P.B.,
The Evaluation of Segmentation Results and the Overlapping Area Matrix,
JRS(18), No. 18, December 1997, pp. 3895-3899.
9801
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Zhang, Y.J.,
Gerbrands, J.J.,
Objective and quantitative segmentation evaluation and comparison,
SP(39), No. 1-2, 1994, pp. 43-54.
Evaluation, Segmentation.
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9400
Earlier:
Comparison of thresholding techniques using synthetic images and
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ICPR92(III:209-213).
IEEE DOI Link
9208
BibRef
Correia, P.L.[Paulo Lobato],
Pereira, F.[Fernando],
Stand-Alone Objective Segmentation Quality Evaluation,
JASP(2002), No. 4, 2002, pp. 389-400.
HTML Version.
0204
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Earlier:
Objective Evaluation of Relative Segmentation Quality,
ICIP00(Vol I: 308-311).
IEEE Abstract. IEEE Top Reference.
0008
BibRef
Correia, P.L.[Paulo Lobato],
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Objective evaluation of video segmentation quality,
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IEEE DOI Link
0304
Evaluation, Video Segmentation.
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Existential uncertainty of spatial objects segmented from satellite
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IEEE Top Reference.
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Udupa, J.K.,
Performance evaluation of finite normal mixture model-based image
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IP(12), No. 10, October 2003, pp. 1153-1169.
IEEE DOI Link
0310
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Simultaneous Truth and Performance Level Estimation (STAPLE):
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0407
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PR(37), No. 8, August 2004, pp. 1607-1617.
WWW Version.
0407
Assessment requires 1 manual segmentation for reference.
BibRef
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Benoit-Cattin, H.,
Odet, C.,
Image segmentation functional model,
PR(37), No. 9, September 2004, pp. 1785-1795.
WWW Version.
0407
Describe segmentation in general with 5 different operations. Compose these
into particular algorithms.
BibRef
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Marichal, X.,
Perceptually-Weighted Evaluation Criteria for Segmentation Masks in
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IP(13), No. 8, August 2004, pp. 1092-1103.
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0409
Evaluation, Video Segmentation. Automatic evaluation of segmentations.
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Petrou, M.[Maria],
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0506
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IP(14), No. 11, November 2005, pp. 1773-1782.
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0510
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A Measure for Mutual Refinements of Image Segmentations,
IP(15), No. 8, August 2006, pp. 2358-2363.
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0606
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Cardoso, J.S.[Jaime S.],
Carvalho, P.[Pedro],
Teixeira, L.F.[Luis F.],
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images and videos,
CVIU(113), No. 7, July 2009, pp. 811-823.
Elsevier DOI Link
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0905
Image segmentation; Video segmentation; Performance evaluation;
Partition-distance; Intersection-graph; Mutual refinement
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Carleer, A.P.,
Debeir, O.,
Wolff, E.,
Assessment of Very High Spatial Resolution Satellite Image
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PhEngRS(71), No. 11, November 2005, pp. 1285-1294.
WWW Version.
0602
Evaluation, Segmentation. An evaluation of several segmentation algorithms carried out with
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Laanaya, H.[Hicham],
Arnold-Bos, A.[Andreas],
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PR(39), No. 11, November 2006, pp. 1987-1995.
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0608
Evaluation, Classifiers. Image classification; Image segmentation; Uncertain environment; Expert fusion
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Jolion, J.M.[Jean-Michel],
Object count/area graphs for the evaluation of object detection and
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IJDAR(8), No. 4, September 2006, pp. 280-296.
Springer DOI Link
0609
BibRef
Ortiz, A.[Alberto],
Oliver, G.[Gabriel],
On the use of the overlapping area matrix for image segmentation
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PRL(27), No. 16, December 2006, pp. 1916-1926.
WWW Version.
0611
Evaluation, Segmentation.
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Earlier:
A Novel Segmentation Strategy Based on Colour Channels Coupling,
CIAP05(328-335).
Springer DOI Link
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Performance evaluation; Image segmentation; Overlapping area matrix
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Crum, W.R.[William R.],
Camara, O.,
Hill, D.L.G.,
Generalized Overlap Measures for Evaluation and Validation in Medical
Image Analysis,
MedImg(25), No. 11, November 2006, pp. 1451-1461.
IEEE DOI Link
0611
Evaluation, Segmentation.
overlap for registration or segmentation evaluation.
See also Phenomenological Model of Diffuse Global and Regional Atrophy Using Finite-Element Methods.
BibRef
Chang, C.I.,
Du, Y.,
Wang, J.,
Guo, S.M.,
Thouin, P.D.,
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VISP(153), No. 6, December 2006, pp. 837-850.
WWW Version.
0702
Survey, Segmentation.
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Wang, S.J.,
Visible colour difference-based quantitative evaluation of colour
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VISP(153), No. 5, October 2006, pp. 598-609.
WWW Version.
0702
Evaluation, Segmentation.
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Unnikrishnan, R.[Ranjith],
Pantofaru, C.[Caroline],
Hebert, M.[Martial],
Toward Objective Evaluation of Image Segmentation Algorithms,
PAMI(29), No. 6, June 2007, pp. 929-944.
IEEE DOI Link
0704
Evaluation, Segmentation.
BibRef
Earlier:
A Measure for Objective Evaluation of Image Segmentation Algorithms,
EEMCV05(III: 34-34).
IEEE DOI Link
0507
Segmentation is usually subjective, no one ground truth.
Use Normalized Probabilistic Rand index to compare segmentations.
See also Objective Criteria for the Evaluation of Clustering Methods.
BibRef
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Studies in Using Image Segmentation to Improve Object Recognition,
CMU-RI-TR-08-23, May, 2008.
BibRef
0805
Ph.D.Thesis, May, 2008.
WWW Version.
BibRef
Pantofaru, C.[Caroline],
Hebert, M.[Martial],
A Comparison of Image Segmentation Algorithms,
CMU-RI-TR-05-40, September, 2005.
WWW Version.
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Barnard, K.[Kobus],
Fan, Q.F.[Quan-Fu],
Swaminathan, R.[Ranjini],
Hoogs, A.[Anthony],
Collins, R.[Roderic],
Rondot, P.[Pascale],
Kaufhold, J.[John],
Evaluation of Localized Semantics: Data, Methodology, and Experiments,
IJCV(77), No. 1-3, May 2008, pp. 199-217.
Springer DOI Link
0803
Dataset, Segmentation. Dataset with hand segmentations.
WWW Version.
BibRef
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Fritts, J.E.[Jason E.],
Goldman, S.A.[Sally A.],
Image segmentation evaluation: A survey of unsupervised methods,
CVIU(110), No. 2, May 2008, pp. 260-280.
WWW Version.
0804
Evaluation, Segmentation.
Survey, Segmentation. Image segmentation; Objective evaluation; Unsupervised evaluation;
Empirical goodness measure
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Cholleti, S.R.[Sharath R.],
Goldman, S.A.[Sally A.],
Fritts, J.E.[Jason E.],
Meta-Evaluation of Image Segmentation Using Machine Learning,
CVPR06(I: 1138-1145).
IEEE DOI Link
0606
See also Localized Content-Based Image Retrieval.
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Letscher, D.[David],
Fritts, J.E.[Jason E.],
Image Segmentation Using Topological Persistence,
CAIP07(587-595).
Springer DOI Link
0708
BibRef
Zhang, H.[Hui],
Goldman, S.A.[Sally A.],
Perceptual Information of Images and the Bias in Homogeneity-based
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PercOrg06(181).
IEEE DOI Link
0609
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Polak, M.[Mark],
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Pi, M.H.[Ming-Hong],
An evaluation metric for image segmentation of multiple objects,
IVC(27), No. 8, 2 July 2009, pp. 1223-1227.
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0906
Image segmentation; Evaluation; Error measure
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Estrada, F.J.[Francisco J.],
Jepson, A.D.[Allan D.],
Benchmarking Image Segmentation Algorithms,
IJCV(85), No. 2, November 2009, pp. xx-yy.
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0909
BibRef
Earlier:
Quantitative Evaluation of a Novel Image Segmentation Algorithm,
CVPR05(II: 1132-1139).
IEEE DOI Link
0507
Evaluation, Segmentation.
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Wong, A.[Alexander],
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Fieguth, P.W.[Paul W.],
SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice
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CRV09(299-305).
IEEE DOI Link
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Wong, A.[Alexander],
Zhang, W.[Wen],
Clausi, D.A.[David A.],
IceSynth: An Image Synthesis System for Sea-Ice Segmentation Evaluation,
CRV09(178-183).
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Sturm, U.,
Weidner, U.,
Further Investigations on Segmentation Quality Assessment for Remote
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HighRes09(xx-yy).
PDF Version.
0906
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Andres, B.[Björn],
Köthe, U.[Ullrich],
Bonea, A.[Andreea],
Nadler, B.[Boaz],
Hamprecht, F.A.[Fred A.],
Quantitative Assessment of Image Segmentation Quality by Random Walk
Relaxation Times,
DAGM09(502-511).
Springer DOI Link
0909
BibRef
Hanbury, A.[Allan],
Stottinger, J.[Julian],
On segmentation evaluation metrics and region counts,
ICPR08(1-4).
IEEE DOI Link
0812
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Kubassova, O.[Olga],
Boesen, M.[Mikael],
Bliddal, H.[Henning],
General framework for unsupervised evaluation of quality of
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ICIP08(3036-3039).
IEEE DOI Link
0810
BibRef
Hanbury, A.[Allan],
How Do Superpixels Affect Image Segmentation?,
CIARP08(178-186).
Springer DOI Link
0809
BibRef
Bagon, S.[Shai],
Boiman, O.[Oren],
Irani, M.[Michal],
What Is a Good Image Segment? A Unified Approach to Segment Extraction,
ECCV08(IV: 30-44).
Springer DOI Link
0810
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Pantofaru, C.[Caroline],
Schmid, C.[Cordelia],
Hebert, M.[Martial],
Object Recognition by Integrating Multiple Image Segmentations,
ECCV08(III: 481-494).
Springer DOI Link
0810
Combination of segmentations.
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Adamek, T.[Tomasz],
Vajda, P.[Peter],
Karaman, M.[Mustafa],
Mörzinger, R.[Roland],
Galmar, E.[Eric],
Sikora, T.[Thomas],
O'Connor, N.E.[Noel E.],
Thien, H.M.[Ha-Minh],
Ebrahimi, T.[Touradj],
Schallauer, P.[Peter],
Huet, B.[Benoit],
Towards Fully Automatic Image Segmentation Evaluation,
ACIVS08(xx-yy).
Springer DOI Link
0810
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Haja, A.[Andreas],
Abraham, S.[Steffen],
Jähne, B.[Bernd],
A Comparison of Region Detectors for Tracking,
DAGM08(xx-yy).
Springer DOI Link
0806
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Wattuya, P.[Pakaket],
Jiang, X.Y.[Xiao-Yi],
Ensemble Combination for Solving the Parameter Selection Problem in
Image Segmentation,
SSPR08(392-401).
Springer DOI Link
0812
BibRef
Wattuya, P.[Pakaket],
Jiang, X.Y.[Xiao-Yi],
Rothaus, K.[Kai],
Combination of Multiple Segmentations by a Random Walker Approach,
DAGM08(xx-yy).
Springer DOI Link
0806
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Wattuya, P.[Pakaket],
Rothaus, K.[Kai],
Prassni, J.S.,
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ICPR08(1-4).
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A Fast Level Set-Like Algorithm with Topology Preserving Constraint,
CAIP09(930-938).
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Svoboda, D.[David],
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A Comparison of Fast Level Set-Like Algorithms for Image Segmentation
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ISVC07(II: 571-581).
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Campilho, A.C.[Aurélio C.],
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ICIAR06(I: 248-259).
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Monteiro, F.C.[Fernando C.],
Campilho, A.C.[Aurélio C.],
Spectral Methods in Image Segmentation:
A Combined Approach,
IbPRIA05(II:191).
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Ge, F.[Feng],
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IEEE DOI Link
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Compare normalized-cut:
See also Normalized Cuts and Image Segmentation. Efficient Graph:
See also Efficient Graph-Based Image Segmentation. Mean Shift:
See also Mean Shift: A Robust Approach Toward Feature Space Analysis. Level Set:
See also Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Science. (and
See also Level-set image segmenation software. )
Ratio-Coutour:
See also Salient Closed Boundary Extraction with Ratio Contour. (and
See also Salient Closed Boundary Extraction with Ratio Contour. ).
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Huart, J.,
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Van Droogenbroeck, M.[Marc],
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Gelasca, E.D.,
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WACV05(I: 394).
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Global - Threshold Based Segmentation Techniques .