8.2 Comparison and Evaluation of Different Techniques, Segmentation Evaluation, Benchmarks

Chapter Contents (Back)
Evaluation, Segmentation. Segmentation, Comparison. Segmentation, Evaluation.

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

The PASCAL Visual Object Classes Challenge 2012,
Online2012 Dataset, Segmentation.
HTML Version. See also Pascal: Pattern Analysis, Statistical Modelling and Computational Learning. BibRef 1200

Everingham, M.[Mark], Van Gool, L.J.[Luc J.], Williams, C.K.I.[Christopher K. I.], Winn, J.[John], Zisserman, A.[Andrew],
The Pascal Visual Object Classes (VOC) Challenge,
IJCV(88), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,
Online2007.
HTML Version. BibRef

Everingham, M.[Mark], Eslami, S.M.A.[S. M. Ali], Van Gool, L.J.[Luc J.], Williams, C.K.I.[Christopher K. I.], Winn, J.[John], Zisserman, A.[Andrew],
The Pascal Visual Object Classes Challenge: A Retrospective,
IJCV(111), No. 1, January 2015, pp. 98-136.
Springer DOI 1502
BibRef

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: Histogram based threshold selection. Adequate and best overall, computationally simple. Probably: See also Object Enhancement and Extraction. Mode sharpening before 1. Applied to the histogram not an image mode filter. Problems: it is a global transformation. Region/edge: See also Region Extraction Using Convergent Evidence. spurious edges in texture cause problems. Quad-tree (edge-region): local, sensitive to choice of split criterion and threshold selections. General papers: See also Segmentation by Split and Merge Techniques, Hierarchical. Relaxation ( See also Scene Labeling by Relaxation Operations. ): sensitive to compatibility measure. 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 Link. 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 Link. 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 Link.
PDF File. 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 Link. 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 8400

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 Link. 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. BibRef 9300

Zhang, Y.J.,
A Survey on Evaluation Methods for Image Segmentation,
PR(29), No. 8, August 1996, pp. 1335-1346.
WWW Link. 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 Link. BibRef 9211

Zhang, Y.J.,
Evaluation and Comparison of Different Segmentation Algorithms,
PRL(18), No. 10, October 1997, pp. 963-974. 9802
Evaluation, Segmentation. BibRef

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
Evaluation, Segmentation. BibRef

Zhang, Y.J., Gerbrands, J.J.,
Objective and quantitative segmentation evaluation and comparison,
SP(39), No. 1-2, 1994, pp. 43-54. Evaluation, Segmentation. BibRef 9400
Earlier:
Comparison of thresholding techniques using synthetic images and ultimate measurement accuracy,
ICPR92(III:209-213).
IEEE DOI 9208
BibRef

Correia, P.L.[Paulo Lobato], Pereira, F.[Fernando],
Stand-Alone Objective Segmentation Quality Evaluation,
JASP(2002), No. 4, 2002, pp. 389-400.
WWW Link. 0204
Evaluation, Segmentation. BibRef
Earlier:
Objective Evaluation of Relative Segmentation Quality,
ICIP00(Vol I: 308-311).
IEEE DOI 0008
BibRef

Correia, P.L.[Paulo Lobato], Pereira, F.[Fernando],
Objective evaluation of video segmentation quality,
IP(12), No. 2, February 2003, pp. 186-200.
IEEE DOI 0304
Evaluation, Video Segmentation. BibRef

Lucieer, A., Stein, A.,
Existential uncertainty of spatial objects segmented from satellite sensor imagery,
GeoRS(40), No. 11, November 2002, pp. 2518-2521.
IEEE Top Reference. 0301
BibRef

Lei, T.[Tianhu], Udupa, J.K.,
Performance evaluation of finite normal mixture model-based image segmentation techniques,
IP(12), No. 10, October 2003, pp. 1153-1169.
IEEE DOI 0310
BibRef

Warfield, S.K., Zou, K.H., Wells, III, W.M.[William M.],
Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation,
MedImg(23), No. 7, July 2004, pp. 903-921.
IEEE Abstract. 0407
BibRef

Gorthi, S.[Subrahmanyam], Akhondi-Asl, A.[Alireza], Thiran, J.P.[Jean-Philippe], Warfield, S.K.[Simon K.],
Optimal MAP Parameters Estimation in STAPLE: Learning from Performance Parameters versus Image Similarity Information,
MLMI14(174-181).
Springer DOI 1410
BibRef

Paglieroni, D.W.[David W.],
Design considerations for image segmentation quality assessment measures,
PR(37), No. 8, August 2004, pp. 1607-1617.
WWW Link. 0407
Assessment requires 1 manual segmentation for reference. BibRef

Zouagui, T., Benoit-Cattin, H., Odet, C.,
Image segmentation functional model,
PR(37), No. 9, September 2004, pp. 1785-1795.
WWW Link. 0407
Describe segmentation in general with 5 different operations. Compose these into particular algorithms. BibRef

Villegas, P., Marichal, X.,
Perceptually-Weighted Evaluation Criteria for Segmentation Masks in Video Sequences,
IP(13), No. 8, August 2004, pp. 1092-1103.
IEEE DOI 0409
Evaluation, Video Segmentation. Automatic evaluation of segmentations. BibRef

Yong, X.[Xia], Feng, D.[Dagan], Zhao, R.C.[Rong-Chun], Petrou, M.[Maria],
Learning-based algorithm selection for image segmentation,
PRL(26), No. 8, June 2005, pp. 1059-1068.
WWW Link. 0506
BibRef

Cardoso, J.S.[Jaime S.], Corte-Real, L.[Luis],
Toward a Generic Evaluation of Image Segmentation,
IP(14), No. 11, November 2005, pp. 1773-1782.
IEEE DOI 0510
Evaluation, Segmentation. BibRef

Cardoso, J.S.[Jaime S.], Corte-Real, L.[Luis],
A Measure for Mutual Refinements of Image Segmentations,
IP(15), No. 8, August 2006, pp. 2358-2363.
IEEE DOI 0606
BibRef

Cardoso, J.S.[Jaime S.], Carvalho, P.[Pedro], Teixeira, L.F.[Luis F.], Corte-Real, L.[Luis],
Partition-distance methods for assessing spatial segmentations of images and videos,
CVIU(113), No. 7, July 2009, pp. 811-823.
Elsevier DOI 0905
Image segmentation; Video segmentation; Performance evaluation; Partition-distance; Intersection-graph; Mutual refinement BibRef

Carvalho, P.[Pedro], Pinheiro, M.[Miguel], Cardoso, J.S.[Jaime S.], Corte-Real, L.[Luís],
A Shortest Path Approach for Vibrating Line Detection and Tracking,
IbPRIA11(9-16).
Springer DOI 1106
BibRef

Carleer, A.P., Debeir, O., Wolff, E.,
Assessment of Very High Spatial Resolution Satellite Image Segmentations,
PhEngRS(71), No. 11, November 2005, pp. 1285-1294.
WWW Link. 0602
Evaluation, Segmentation. An evaluation of several segmentation algorithms carried out with empirical discrepancy methods. BibRef

Martin, A.[Arnaud], Laanaya, H.[Hicham], Arnold-Bos, A.[Andreas],
Evaluation for uncertain image classification and segmentation,
PR(39), No. 11, November 2006, pp. 1987-1995.
WWW Link. 0608
Evaluation, Classifiers. Image classification; Image segmentation; Uncertain environment; Expert fusion BibRef

Wolf, C.[Christian], Jolion, J.M.[Jean-Michel],
Object count/area graphs for the evaluation of object detection and segmentation algorithms,
IJDAR(8), No. 4, September 2006, pp. 280-296.
Springer DOI 0609
BibRef

Ortiz, A.[Alberto], Oliver, G.[Gabriel],
On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures,
PRL(27), No. 16, December 2006, pp. 1916-1926.
WWW Link. 0611
Evaluation, Segmentation. BibRef
Earlier:
A Novel Segmentation Strategy Based on Colour Channels Coupling,
CIAP05(328-335).
Springer DOI 0509
Performance evaluation; Image segmentation; Overlapping area matrix BibRef

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 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.,
Survey and comparative analysis of entropy and relative entropy thresholding techniques,
VISP(153), No. 6, December 2006, pp. 837-850.
DOI Link 0702
Survey, Segmentation. BibRef

Chen, H.C., Wang, S.J.,
Visible colour difference-based quantitative evaluation of colour segmentation,
VISP(153), No. 5, October 2006, pp. 598-609.
DOI Link 0702
Evaluation, Segmentation. BibRef

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 0704
Evaluation, Segmentation. BibRef
Earlier:
A Measure for Objective Evaluation of Image Segmentation Algorithms,
EEMCV05(III: 34-34).
IEEE DOI 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

Pantofaru, C.[Caroline],
Studies in Using Image Segmentation to Improve Object Recognition,
CMU-RI-TR-08-23, May, 2008. BibRef 0805 Ph.D.Thesis, May, 2008.
WWW Link. BibRef

Pantofaru, C.[Caroline], Hebert, M.[Martial],
A Comparison of Image Segmentation Algorithms,
CMU-RI-TR-05-40, September, 2005.
WWW Link. BibRef 0509

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 0803
Dataset, Segmentation. Dataset with hand segmentations.
WWW Link. BibRef

Zhang, H.[Hui], 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 Link. 0804
Evaluation, Segmentation. Survey, Segmentation. Image segmentation; Objective evaluation; Unsupervised evaluation; Empirical goodness measure BibRef

Zhang, H.[Hui], 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 0606
See also Localized Content-Based Image Retrieval. BibRef

Letscher, D.[David], Fritts, J.E.[Jason E.],
Image Segmentation Using Topological Persistence,
CAIP07(587-595).
Springer DOI 0708
BibRef

Zhang, H.[Hui], Goldman, S.A.[Sally A.],
Perceptual Information of Images and the Bias in Homogeneity-based Segmentation,
PercOrg06(181).
IEEE DOI 0609
BibRef

Polak, M.[Mark], Zhang, H.[Hong], Pi, M.H.[Ming-Hong],
An evaluation metric for image segmentation of multiple objects,
IVC(27), No. 8, 2 July 2009, pp. 1223-1227.
Elsevier DOI 0906
Image segmentation; Evaluation; Error measure BibRef

Estrada, F.J.[Francisco J.], Jepson, A.D.[Allan D.],
Benchmarking Image Segmentation Algorithms,
IJCV(85), No. 2, November 2009, pp. xx-yy.
Springer DOI 0909
BibRef
Earlier:
Quantitative Evaluation of a Novel Image Segmentation Algorithm,
CVPR05(II: 1132-1139).
IEEE DOI 0507
Evaluation, Segmentation. BibRef

Chung, F.[François], Schmid, J.[Jérôme], Magnenat-Thalmann, N.[Nadia], Delingette, H.[Hervé],
Comparison of statistical models performance in case of segmentation using a small amount of training datasets,
VC(27), No. 2, February 2011, pp. 141-151.
WWW Link. 1103
BibRef

Zhao, X., Stein, A., Chen, X., Zhang, X.,
Quantification of Extensional Uncertainty of Segmented Image Objects by Random Sets,
GeoRS(49), No. 7, July 2011, pp. 2548-2557.
IEEE DOI 1107
Model segmenttion results based on data quality. BibRef

Dogra, D.P., Majumdar, A.K., Sural, S.,
Evaluation of segmentation techniques using region area and boundary matching information,
JVCIR(23), No. 1, January 2012, pp. 150-160.
Elsevier DOI 1112
BibRef
Earlier:
Evaluation of Segmentation Techniques Using Region Size and Boundary Information,
PReMI09(285-290).
Springer DOI 0912
Segmentation evaluation; Region area; Region boundary; Matching index BibRef

Gavet, Y.[Yann], Pinoli, J.C.[Jean-Charles],
A Geometric Dissimilarity Criterion Between Jordan Spatial Mosaics. Theoretical Aspects and Application to Segmentation Evaluation,
JMIV(42), No. 1, January 2012, pp. 25-49.
WWW Link. 1201
BibRef

Nguyen, T.D.[Toan Dinh], Lee, G.S.[Guee-Sang],
Color image segmentation using tensor voting based color clustering,
PRL(33), No. 5, 1 April 2012, pp. 605-614.
Elsevier DOI 1202
Color; Image segmentation; Tensor voting; Clustering; Feature space BibRef

Dinh, T.N.[Toan Nguyen], Park, J.H.[Jong-Hyun], Lee, C.W.[Chil-Woo], Lee, G.S.[Guee-Sang],
Tensor Voting Based Color Clustering,
ICPR10(597-600).
IEEE DOI 1008
BibRef

Madhubalan, K.[Kavitha], Lee, G.S.[Guee-Sang],
Comparison of Tensor Voting based clustering and EM based clustering,
FCV11(1-5).
IEEE DOI 1102
For segmentation. Also k-means and mean shift. For constant and varying numbers of clusters. Tensor voting came out best. BibRef

Xue, J.H.[Jing-Hao], Zhang, Y.J.[Yu-Jin],
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding,
PRL(33), No. 6, 15 April 2012, pp. 793-797.
Elsevier DOI 1203
Image thresholding; Iterative selection; Discriminant analysis; Minimum error thresholding; Mixture of Gaussian distributions; Otsu's method See also Picture Thresholding Using an Iterative Selection Method. See also Minimum Error Thresholding. See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Liu, Y.[Yong], Bian, L.[Ling], Meng, Y.H.[Yu-Hong], Wang, H.P.[Huan-Ping], Zhang, S.[Shifu], Yang, Y.N.[Yi-Ning], Shao, X.M.[Xiao-Min], Wang, B.[Bo],
Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis,
PandRS(68), No. 1, March 2012, pp. 144-156.
Elsevier DOI 1204
Object-based image analysis; Image segmentation; Discrepancy measures; Optimal parameter value combinations; Under-segmentation; Over-segmentation BibRef

Correa-Tome, F.E., Sanchez-Yanez, R.E.[Raul E.], Ayala-Ramirez, V.,
Measuring empirical discrepancy in image segmentation results,
IET-CV(6), No. 3, 2012, pp. 224-230.
DOI Link 1205
BibRef

Sidiropoulos, P., Mezaris, V., Kompatsiaris, I., Kittler, J.V.,
Differential Edit Distance: A Metric for Scene Segmentation Evaluation,
CirSysVideo(22), No. 6, June 2012, pp. 904-914.
IEEE DOI 1206
BibRef

Hemery, B., Laurent, H., Emile, B., Rosenberger, C.,
Parametrization of an image understanding quality metric with a subjective evaluation,
PRL(34), No. 5, 1 April 2013, pp. 511-518.
Elsevier DOI 1303
Image understanding; Subjective evaluation; Object localization; Object recognition; Evaluation metrics BibRef

Peng, B., Li, T.,
A Probabilistic Measure for Quantitative Evaluation of Image Segmentation,
SPLetters(20), No. 7, 2013, pp. 689-692.
IEEE DOI Ground truth; image segmentation evaluation; segmentation database 1307
BibRef

Varga, B.[Balázs], Karacs, K.[Kristóf],
Towards a balanced trade-off between speed and accuracy in unsupervised data-driven image segmentation,
MVA(24), No. 6, August 2013, pp. 1267-1294.
WWW Link. 1307
BibRef

Li-Baboud, Y.S.[Ya-Shian], Cardone, A.[Antonio], Chalfoun, J.[Joe], Bajcsy, P.[Peter], Elliott, J.[John],
Understanding the impact of image quality on segmentation accuracy,
SPIE(Newsroom), August 6, 2013
DOI Link 1310
A method of detecting suboptimal microscopy settings explores the relationship between instrument settings, image quality descriptors, and the accuracy of image post-processing. BibRef

Vojodi, H.[Hakime], Fakhari, A.[Ali], Moghadam, A.M.E.[Amir Masoud Eftekhari],
A new evaluation measure for color image segmentation based on genetic programming approach,
IVC(31), No. 11, 2013, pp. 877-886.
Elsevier DOI 1311
Image segmentation BibRef

Lin, J.[Jian], Peng, B.[Bo], Li, T.[Tianrui],
A Learning-Based Framework for Supervised and Unsupervised Image Segmentation Evaluation,
IJIG(14), No. 03, 2014, pp. 1450014.
DOI Link 1410
BibRef

Michel, J., Youssefi, D., Grizonnet, M.,
Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images,
GeoRS(53), No. 2, February 2015, pp. 952-964.
IEEE DOI 1411
geophysical image processing BibRef

Peng, R.B.[Ren-Bin], Varshney, P.K.[Pramod K.],
On performance limits of image segmentation algorithms,
CVIU(132), No. 1, 2015, pp. 24-38.
Elsevier DOI 1502
Image segmentation BibRef

Peng, R.B.[Ren-Bin], Varshney, P.K.[Pramod K.],
A human visual system-driven image segmentation algorithm,
JVCIR(26), No. 1, 2015, pp. 66-79.
Elsevier DOI 1502
Image processing BibRef

Zhang, X., Xiao, P., Feng, X., Feng, L., Ye, N.,
Toward Evaluating Multiscale Segmentations of High Spatial Resolution Remote Sensing Images,
GeoRS(53), No. 7, July 2015, pp. 3694-3706.
IEEE DOI 1503
Accuracy BibRef

Yang, J.[Jian], He, Y.H.[Yu-Hong], Caspersen, J.[John], Jones, T.[Trevor],
A discrepancy measure for segmentation evaluation from the perspective of object recognition,
PandRS(101), No. 1, 2015, pp. 186-192.
Elsevier DOI 1503
GEOBIA BibRef

Lucchi, A.[Aurelien], Márquez-Neila, P.[Pablo], Becker, C., Li, Y.P.[Yun-Peng], Smith, K.[Kevin], Knott, G., Fua, P.[Pascal],
Learning Structured Models for Segmentation of 2-D and 3-D Imagery,
MedImg(34), No. 5, May 2015, pp. 1096-1110.
IEEE DOI 1505
Approximation methods BibRef

Lucchi, A.[Aurelien], Li, Y.P.[Yun-Peng], Boix, X.[Xavier], Smith, K.[Kevin], Fua, P.[Pascal],
Are spatial and global constraints really necessary for segmentation?,
ICCV11(9-16).
IEEE DOI 1201
for MRF segmentations. MAP doesn't add anything. See also Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation. BibRef

Lucchi, A.[Aurelien], Li, Y.P.[Yun-Peng], Fua, P.[Pascal],
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets,
CVPR13(1987-1994)
IEEE DOI 1309
computer vision BibRef

Lucchi, A.[Aurélien], Li, Y.P.[Yun-Peng], Smith, K.[Kevin], Fua, P.[Pascal],
Structured Image Segmentation Using Kernelized Features,
ECCV12(II: 400-413).
Springer DOI 1210
BibRef

Shi, R.[Ran], Ngan, K.N.[King Ngi], Li, S.[Songnan], Paramesran, R., Li, H.L.[Hong-Liang],
Visual Quality Evaluation of Image Object Segmentation: Subjective Assessment and Objective Measure,
IP(24), No. 12, December 2015, pp. 5033-5045.
IEEE DOI 1512
image segmentation BibRef

Borji, A., Cheng, M.M.[Ming-Ming], Jiang, H.[Huaizu], Li, J.[Jia],
Salient Object Detection: A Benchmark,
IP(24), No. 12, December 2015, pp. 5706-5722.
IEEE DOI 1512
image segmentation BibRef

Peng, B., Wang, X., Yang, Y.,
Region Based Exemplar References for Image Segmentation Evaluation,
SPLetters(23), No. 4, April 2016, pp. 459-462.
IEEE DOI 1604
Entropy BibRef

Lampert, T.A., Stumpf, A., Gançarski, P.,
An Empirical Study Into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation,
IP(25), No. 6, June 2016, pp. 2557-2572.
IEEE DOI 1605
estimation theory. How does the variation affect the segmentation evaluation. BibRef

Pont-Tuset, J.[Jordi], Marques, F.[Ferran],
Supervised Evaluation of Image Segmentation and Object Proposal Techniques,
PAMI(38), No. 7, July 2016, pp. 1465-1478.
IEEE DOI 1606
BibRef
Earlier:
Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation,
CVPR13(2131-2138)
IEEE DOI 1309
BibRef
Earlier:
Supervised Assessment of Segmentation Hierarchies,
ECCV12(IV: 814-827).
Springer DOI 1210
Context BibRef


Huang, C., Wu, Q., Meng, F.,
QualityNet: Segmentation quality evaluation with deep convolutional networks,
VCIP16(1-4)
IEEE DOI 1701
Computer vision BibRef

Ge, Q.[Qian], Lobaton, E.[Edgar],
Consensus-Based Image Segmentation via Topological Persistence,
DIFF-CV16(1050-1057)
IEEE DOI 1612
Consensus segmentation, not a single method. BibRef

Gurari, D., Jain, S.D., Betke, M., Grauman, K.,
Pull the Plug? Predicting If Computers or Humans Should Segment Images,
CVPR16(382-391)
IEEE DOI 1612
BibRef

Sanroma, G.[Gerard], Benkarim, O.M.[Oualid M.], Piella, G.[Gemma], Ballester, M.Á.G.[Miguel Ángel González],
Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates,
MLMI16(27-35).
Springer DOI 1611
BibRef

Sun, H., Ding, Y., Huang, Y., Wang, G.,
Critical Assessment Of Object Segmentation In Aerial Image Using Geo-Hausdorff Distance,
ISPRS16(B4: 187-194).
DOI Link 1610
BibRef

Sakamoto, M., Honda, Y., Kondo, A.,
Improvement And Extension Of Shape Evaluation Criteria In Multi-scale Image Segmentation,
ISPRS16(B3: 909-915).
DOI Link 1610
BibRef

Krenn, M.[Markus], Dorfer, M.[Matthias], del Toro, O.A.J.[Oscar Alfonso Jiménez], Müller, H.[Henning], Menze, B.[Bjoern], Weber, M.A.[Marc-André], Hanbury, A.[Allan], Langs, G.[Georg],
Creating a Large-Scale Silver Corpus from Multiple Algorithmic Segmentations,
MCV15(103-115).
Springer DOI 1608
To evaluate medical segmentation. BibRef

Jiang, B.J.[Bing-Jie], Ren, T.[Tongwei], Bei, J.[Jia],
Automatic Scribble Simulation for Interactive Image Segmentation Evaluation,
MMMod16(I: 596-608).
Springer DOI 1601
BibRef

Cabezas, F.[Ferran], Carlier, A.[Axel], Charvillat, V.[Vincent], Salvador, A.[Amaia], Giro-i-Nieto, X.[Xavier],
Quality control in crowdsourced object segmentation,
ICIP15(4243-4247)
IEEE DOI 1512
Crowdsourcing BibRef

Sithole, G., Majola, L.,
Framework for Comparing Segmentation Algorithms,
Seamless15(131-136).
DOI Link 1508
BibRef

Perret, B.[Benjamin], Cousty, J.[Jean], Ura, J.C.R.[Jean Carlo Rivera], Guimarães, S.J.F.[Silvio Jamil F.],
Evaluation of Morphological Hierarchies for Supervised Segmentation,
ISMM15(39-50).
Springer DOI 1506
BibRef

Wang, P.[Peng], Yuille, A.L.[Alan L.],
Error Factor Analysis for Wild Scene Image-Labelling,
WACV15(781-788)
IEEE DOI 1503
Accuracy. Segmentation analysis. PASCAL VOC challenge. BibRef

Jean, F.[Frederic], Albu, A.B.[Alexandra Branzan], Capson, D.[David], Higgs, E.[Eric], Fisher, J.T.[Jason T.], Starzomski, B.M.[Brian M.],
The Mountain Habitats Segmentation and Change Detection Dataset,
WACV15(603-609)
IEEE DOI 1503
Feature extraction. High resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains. BibRef

Gurari, D.[Danna], Theriault, D.[Diane], Sameki, M.[Mehrnoosh], Isenberg, B.[Brett], Pham, T.A.[Tuan A.], Purwada, A.[Alberto], Solski, P.[Patricia], Walker, M.[Matthew], Zhang, C.[Chentian], Wong, J.Y.[Joyce Y.], Betke, M.[Margrit],
How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-experts, and Algorithms,
WACV15(1169-1176)
IEEE DOI 1503
Algorithm design and analysis BibRef

Shi, R.[Ran], Ngan, K.N.[King Ngi], Li, S.[Songnan],
Jaccard index compensation for object segmentation evaluation,
ICIP14(4457-4461)
IEEE DOI 1502
Image segmentation BibRef

Taha, A.A.[Abdel Aziz], Hanbury, A.[Allan], del Toro, O.A.J.[Oscar A. Jimenez],
A formal method for selecting evaluation metrics for image segmentation,
ICIP14(932-936)
IEEE DOI 1502
Correlation BibRef

Haindl, M.[Michal], Mike, S.[Stanislav],
Unsupervised Image Segmentation Contest,
ICPR14(1484-1489)
IEEE DOI 1412
Benchmark testing BibRef

Gueguen, L.[Lionel], Hamid, R.[Raffay],
Large-scale damage detection using satellite imagery,
CVPR15(1321-1328)
IEEE DOI 1510
BibRef

Hamid, R., O'Hara, S., Tabb, M.,
Global-scale object detection using satellite imagery,
PCV14(107-113).
DOI Link 1404
BibRef

Ledig, C.[Christian], Shi, W.Z.[Wen-Zhe], Bai, W.[Wenjia], Rueckert, D.[Daniel],
Patch-Based Evaluation of Image Segmentation,
CVPR14(3065-3072)
IEEE DOI 1409
evaluation; image segmentation; medical; patch-based; similarity measure BibRef

Mai, L.[Long], Liu, F.[Feng],
Comparing Salient Object Detection Results without Ground Truth,
ECCV14(III: 76-91).
Springer DOI 1408
BibRef

Srubar, S.[Stepan],
Speed Comparison of Segmentation Evaluation Methods,
IWCIA14(113-122).
Springer DOI 1405
BibRef

Ozay, M.[Mete], Vural, F.T.Y.[Fatos T. Yarman], Kulkarni, S.R.[Sanjeev R.], Poor, H.V.[H. Vincent],
Fusion of image segmentation algorithms using consensus clustering,
ICIP13(4049-4053)
IEEE DOI 1402
Segmentation; clustering; consensus; fusion; stochastic optimization BibRef

Shi, R.[Ran], Ngan, K.N.[King Ngi], Li, S.N.[Song-Nan],
The Objective Evaluation of Image Object Segmentation Quality,
ACIVS13(470-479).
Springer DOI 1311
BibRef

Intawong, K.[Kannikar], Scuturici, M.[Mihaela],
A New Pixel-Based Quality Measure for Segmentation Algorithms Integrating Precision, Recall and Specificity,
CAIP13(188-195).
Springer DOI 1308
BibRef

Eramian, M.G.[Mark G.],
Worst-Case Local Boundary Precision in Global Measures of Segmentation Reproducibility,
CRV13(59-66)
IEEE DOI 1308
Accuracy BibRef

Gurari, D., Kim, S.K., Yang, E., Isenberg, B., Pham, T.A., Purwada, A., Solski, P., Walker, M., Wong, J.Y., Betke, M.,
SAGE: An approach and implementation empowering quick and reliable quantitative analysis of segmentation quality,
WACV13(475-481).
IEEE DOI 1303
BibRef

Peng, B.[Bo], Zhang, L.[Lei],
Evaluation of Image Segmentation Quality by Adaptive Ground Truth Composition,
ECCV12(III: 287-300).
Springer DOI 1210
BibRef

Peles, D.[David], Lindenbaum, M.[Michael],
A Segmentation Quality Measure Based on Rich Descriptors and Classification Methods,
SSVM11(398-410).
Springer DOI 1201
BibRef

Wang, Z.Y.[Zu-Yuan], Boesch, R.[Ruedi], Ginzler, C.[Christian],
Quantitative Comparison of Segmentation Results from ADS40 Images in Swiss NFI,
ICIG11(147-151).
IEEE DOI 1109
BibRef

Popescu, B.[Bogdan], Iancu, A.[Andreea], Burdescu, D.D.[Dumitru Dan], Brezovan, M.[Marius], Ganea, E.[Eugen],
Evaluation of Image Segmentation Algorithms from the Perspective of Salient Region Detection,
ACIVS11(183-194).
Springer DOI 1108
BibRef

Jaber, M.[Mustafa], Vantaram, S.R.[Sreenath Rao], Saber, E.[Eli],
A probabilistic framework for unsupervised evaluation and ranking of image segmentations,
AIPR10(1-6).
IEEE DOI 1010
BibRef

van Coillie, F.M.B., van Camp, N.A.F., de Wulf, R.R., Bral, L., Gautama, S.,
Segmentation Quality Evaluation for Large Scale Mapping Purposes in Flanders, Belgium,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Movahedi, V.[Vida], Elder, J.H.[James H.],
Design and perceptual validation of performance measures for salient object segmentation,
POCV10(49-56).
IEEE DOI 1006
BibRef

Wang, Q.[Qi], Wang, Z.F.[Zeng-Fu],
A Subjective Method for Image Segmentation Evaluation,
ACCV09(III: 53-64).
Springer DOI 0909
BibRef

Loeff, N.[Nicolas], Farhadi, A.[Ali], Endres, I.[Ian], Forsyth, D.A.[David A.],
Unlabeled data improves word prediction,
ICCV09(956-962).
IEEE DOI 0909
Semi-supervised labeling of data. To generate labeled datasets. See also Berkeley Segmentation Dataset and Benchmark, The. BibRef

Zhang, Z.J.[Zheng-Jun], Liu, L.W.[Li-Wei], Zhu, Y.Q.[Yao-Qin],
GS Model for the Image Segmentation Number via Weighted the Position Information and the Gray Values,
CISP09(1-4).
IEEE DOI 0910
Gap statistics. BibRef

Ming, D.P.[Dong-Ping], Wang, Q.[Qun], Luo, J.C.[Jian-Cheng], Shen, Z.F.[Zhan-Feng],
Evaluation of High Spatial Resolution Remote Sensing Image Segmentation Algorithms,
CISP09(1-5).
IEEE DOI 0910
BibRef

Sturm, U., Weidner, U.,
Further Investigations on Segmentation Quality Assessment for Remote Sensing Applications,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Beier, T.[Thorsten], Kroeger, T.[Thorben], Kappes, J.H.[Jorg H.], Kothe, U.[Ullrich], Hamprecht, F.A.[Fred A],
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning,
CVPR14(73-80)
IEEE DOI 1409
correlation clustering BibRef

Andres, B.[Björn], Kappes, J.H.[Jörg H.], Köthe, U.[Ullrich], Schnörr, C.[Christoph], Hamprecht, F.A.[Fred A.],
An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM,
DAGM10(353-362).
Springer DOI 1009
BibRef

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 0909
BibRef

Hanbury, A.[Allan], Stottinger, J.[Julian],
On segmentation evaluation metrics and region counts,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kubassova, O.[Olga], Boesen, M.[Mikael], Bliddal, H.[Henning],
General framework for unsupervised evaluation of quality of segmentation results,
ICIP08(3036-3039).
IEEE DOI 0810
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 0810
BibRef

Pantofaru, C.[Caroline], Schmid, C.[Cordelia], Hebert, M.[Martial],
Object Recognition by Integrating Multiple Image Segmentations,
ECCV08(III: 481-494).
Springer DOI 0810
Combination of segmentations. BibRef

Goldmann, L.[Lutz], 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 0810
BibRef

Haja, A.[Andreas], Abraham, S.[Steffen], Jähne, B.[Bernd],
A Comparison of Region Detectors for Tracking,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Wattuya, P.[Pakaket], Jiang, X.Y.[Xiao-Yi],
Ensemble Combination for Solving the Parameter Selection Problem in Image Segmentation,
SSPR08(392-401).
Springer DOI 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 0806
BibRef

Wattuya, P.[Pakaket], Rothaus, K.[Kai], Prassni, J.S., Jiang, X.Y.[Xiao-Yi],
A random walker based approach to combining multiple segmentations,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Al-Muhairi, H., Fleury, M., Clark, A.F.,
A computationally efficient evaluation environment for image segmentation,
ICMV07(129-134).
IEEE DOI 0712
BibRef

Maška, M.[Martin], Matula, P.[Pavel], Danek, O.[Ondrej], Kozubek, M.[Michal],
A Fast Level Set-Like Algorithm for Region-Based Active Contours,
ISVC10(III: 387-396).
Springer DOI 1011
BibRef

Maška, M.[Martin], Matula, P.[Pavel],
A Fast Level Set-Like Algorithm with Topology Preserving Constraint,
CAIP09(930-938).
Springer DOI 0909
BibRef

Maška, M.[Martin], Hubený, J.[Jan], Svoboda, D.[David], Kozubek, M.[Michal],
A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy,
ISVC07(II: 571-581).
Springer DOI 0711
BibRef

Cárdenes, R.[Rubén], Bach, M.[Meritxell], Chi, Y.[Ying], Marras, I.[Ioannis], de Luis, R.[Rodrigo], Anderson, M.[Mats], Cashman, P.[Peter], Bultelle, M.[Matthieu],
Multimodal Evaluation for Medical Image Segmentation,
CAIP07(229-236).
Springer DOI 0708
BibRef

Hafiane, A.[Adel], Chabrier, S.[Sébastien], Rosenberger, C.[Christophe], Laurent, H.[Hélène],
A New Supervised Evaluation Criterion for Region Based Segmentation Methods,
ACIVS07(439-448).
Springer DOI 0708
BibRef

Rosenberger, C.[Christopher],
Adaptative evaluation of image segmentation results,
ICPR06(II: 399-402).
IEEE DOI 0609
BibRef

Mashtalir, V., Mikhnova, E., Shlyakhov, V., Yegorova, E.,
A Novel Metric on Partitions for Image Segmentation,
AVSBS06(18-18).
IEEE DOI 0611
BibRef

Neubert, M., Herold, H., Meinel, G.,
Evaluation of remote sensing image segmentation quality: Further results and concepts,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Charles, J.J., Kuncheva, L.I., Wells, B., Lim, I.S.,
An Evaluation Measure of Image Segmentation Based on Object Centres,
ICIAR06(I: 283-294).
Springer DOI 0610
BibRef

Monteiro, F.C.[Fernando C.], Campilho, A.C.[Aurélio C.],
Performance Evaluation of Image Segmentation,
ICIAR06(I: 248-259).
Springer DOI 0610
BibRef

Monteiro, F.C.[Fernando C.], Campilho, A.C.[Aurélio C.],
Spectral Methods in Image Segmentation: A Combined Approach,
IbPRIA05(II:191).
Springer DOI 0509
BibRef

MacDonald, D., Lang, J., McAllister, M.,
Evaluation of Colour Image Segmentation Hierarchies,
CRV06(27-27).
IEEE DOI 0607
BibRef

Hodge, V.J.[Victoria J.], Eakins, J.[John], Austin, J.[James],
Inducing a perceptual relevance shape classifier,
CIVR07(138-145).
DOI Link 0707
BibRef

Hodge, V.J.[Victoria J.], Hollier, G.[Garry], Eakins, J.[John], Austin, J.[Jim],
Eliciting Perceptual Ground Truth for Image Segmentation,
CIVR06(320-329).
Springer DOI 0607
BibRef

Ge, F.[Feng], Wang, S.[Song], Liu, T.C.[Tie-Cheng],
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction,
CVPR06(I: 1146-1153).
IEEE DOI 0606
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. ). BibRef

Jiang, X.Y.[Xiao-Yi], Marti, C.[Cyril], Irniger, C.[Christophe], Bunke, H.[Horst],
Image Segmentation Evaluation by Techniques of Comparing Clusterings,
CIAP05(344-351).
Springer DOI 0509
BibRef

Ndjiki-Nya, P.[Patrick], Simo, G.[Ghislain], Wiegand, T.[Thomas],
Evaluation of Color Image Segmentation Algorithms Based on Histogram Thresholding,
VLBV05(214-222).
Springer DOI 0509
BibRef

Huart, J., Bertolino, P.,
Similarity-Based and Perception-Based Image Segmentation,
ICIP05(III: 1148-1151).
IEEE DOI 0512
BibRef

Van Droogenbroeck, M.[Marc], Barnich, O.[Olivier],
Design of Statistical Measures for the Assessment of Image Segmentation Schemes,
CAIP05(280).
Springer DOI 0509
BibRef

Vergés-Llahí, J.[Jaume], Sanfeliu, A.[Alberto],
Evaluation of Distances Between Color Image Segmentations,
IbPRIA05(II:263).
Springer DOI 0509
BibRef

Gelasca, E.D., Ebrahimi, T., Farias, M.C.Q., Carli, M., Mitra, S.K.,
Annoyance of spatio-temporal artifacts in segmentation quality assessment,
ICIP04(I: 345-348).
IEEE DOI 0505
BibRef

Gelasca, E.D.[Elisa Drelie], Ebrahimi, T.[Touradj], Farias, M.C.Q.[Mylène C. Q.], Carli, M.[Marco], Mitra, S.K.[Sanjit K.],
Towards Perceptually Driven Segmentation Evaluation Metrics,
PercOrg04(52).
IEEE DOI 0502
BibRef

Cavallaro, A., Gelasca, E.D.[Elisa Drelie], Ebrahimi, T.[Touradj],
Objective evaluation of segmentation quality using spatio-temporal context,
ICIP02(III: 301-304).
IEEE DOI 0210
BibRef

Stauffer, C.[Chris],
Learning a Probabilistic Similarity Function for Segmentation,
PercOrg04(50).
IEEE DOI 0502
BibRef

Unnikrishnan, R.[Ranjith], Hebert, M.[Martial],
Measures of Similarity,
WACV05(I: 394).
IEEE DOI 0502
Measures to compare one segmentation to another. BibRef

Zhou, Q.A.[Qi-Ang], Ma, L.M.[Li-Min], Zhou, M.[Min], Chelberg, D.,
Strong image segmentation from a data-driven perspective: impossible?,
Southwest04(56-60).
WWW Link. 0411
BibRef

Chabrier, S., Emile, B., Laurent, H., Rosenberger, C., Marche, P.,
Unsupervised evaluation of image segmentation application to multi-spectral images,
ICPR04(I: 576-579).
IEEE DOI 0409
BibRef

Goumeidane, A.B., Khamadja, M., Belaroussi, B., Benoit-Cattin, H., Odet, C.,
New discrepancy measures for segmentation evaluation,
ICIP03(II: 411-414).
IEEE DOI 0312
BibRef

Letournel, V.[Valérie], Sankur, B.[Bülent], Pradeilles, F.[Frédéric], Maître, H.[Henri],
Feature Extraction for Quality Assessment of Aerial Image Segmentation,
PCV02(A: 199). 0305
BibRef

Odet, C., Belaroussi, B., Cattin, H.B.,
Scalable discrepancy measures for segmentation evaluation,
ICIP02(I: 785-788).
IEEE DOI 0210
BibRef

Everingham, M., Muller, H., Thomas, B.,
Evaluating Image Segmentation Algorithms Using the Pareto Front,
ECCV02(IV: 34 ff.).
Springer DOI 0205
BibRef
Earlier:
Evaluating image segmentation algorithms using monotonic hulls in fitness/cost space,
BMVC01(Session 4: Segmentation).
HTML Version. University of Bristol 0110
Allows for multiple metrics. BibRef

Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.,
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration,
ECCV02(III: 408 ff.).
Springer DOI 0205
Survey, Segmentation. Edge and region integration approaches. BibRef

Wenyin, L., Zhai, J., Dori, D., Long, T.,
A system for performance evaluation of arc segmentation algorithms,
EEMCV01(xx-yy). 0110
BibRef

Southall, B., Buxton, B.F., Marchant, J.A., Hague, T.,
On the Performance Characterisation of Image Segmentation Algorithms: A Case Study,
ECCV00(II: 351-365).
Springer DOI 0003
BibRef

Anderson, H.L., Bajcsy, R., and Mintz, M.,
A Modular Feedback System for Image Segmentation,
UPennGRASP Lab 110 MS-CIS-87-56, 1987. Uses apriori knowledge about desired scale of segmentation as well as underlying geometric structure to drive feedback system. Human-generated segmentations used for comparison. BibRef 8700

Schachter, B.J.,
A Survey and Evaluation of FLIR Target Detection/Segmentation Algorithms,
DARPA82(49-57). Survey, Segmentation. ATR. FLIR. BibRef 8200

Ashjaei, B.[Bahram], and Soltanian-Zadeh, H.[Hamid],
A Comparative Study of Segmentation Methodologies for Magnetic Resonance Imaging,
SCIA97(xx-yy)
HTML Version. 9705
BibRef
Earlier:
A comparative analysis of neural network methodologies for segmentation of magnetic resonance images,
ICIP96(II: 257-260).
IEEE DOI 9610
BibRef

Yang, L.[Luren], Albregtsen, F.[Fritz], Lønnestad, T.[Tor], Grøttum, P.[Per],
A supervised approach to the evaluation of image segmentation methods,
CAIP95(759-765).
Springer DOI 9509
BibRef

Huang, Q.[Qian], Dom, B.,
Quantitative methods of evaluating image segmentation,
ICIP95(III: 53-56).
IEEE DOI 9510
BibRef

de Graaf, C.N., Koster, A.S.E., Vincken, K.L., Viergever, M.A.,
Task-directed evaluation of image segmentation methods,
ICPR92(III:219-222).
IEEE DOI 9208
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Global - Threshold Based Segmentation Techniques .


Last update:Jan 22, 2017 at 18:17:25