ADE20K Dataset,
2017.
Dataset, Segmentation.
WWW Link. Annotated data,
LHI Segmentation Dataset,
Subset of larger dataset.
Online2008
HTML Version.
Dataset, Segmentation.
See also Lotus Hill Institute.
BibRef
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The PASCAL Visual Object Classes Challenge 2012,
Online2012
Dataset, Segmentation.
WWW Link.
Various PASCAL datasets for different years
See also Pascal: Pattern Analysis, Statistical Modelling and Computational Learning.
BibRef
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COCO: Common Objects in Context,
Online
Dataset, Segmentation.
WWW Link.
Large-scale object detection, segmentation, and captioning dataset.
Used for ECCV 2018 challange:
HTML Version.
BibRef
DIS5K,
2022
Dataset, Segmentation.
WWW Link.
5,470 high-resolution (e.g., 2K, 4K or larger) images covering
camouflaged, salient, or meticulous objects in various backgrounds.
See also Highly Accurate Dichotomous Image Segmentation.
Caves, R.,
Quegan, S.,
White, R.,
Quantitative Comparison of the Performance of
SAR Segmentation Algorithms,
IP(7), No. 11, November 1998, pp. 1534-1546.
IEEE DOI
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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],
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IJCV(111), No. 1, January 2015, pp. 98-136.
Springer DOI
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Ranade, S., and
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A Comparison of Some Segmentation Algorithms for Cytology,
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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.[Joan S.],
A Survey of Threshold Selection Techniques,
CGIP(7), No. 2, April 1978, pp. 259-265.
Elsevier DOI
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
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Sahoo, P.K.,
Soltani, S.,
Wong, A.K.C.,
A Survey of Thresholding Techniques,
CVGIP(41), No. 2, February 1988, pp. 233-260.
Elsevier DOI
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,
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Elsevier DOI
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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.
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Levine, M.D.[Martin D.],
Nazif, A.M.[Ahmed M.],
Rule-Based Image Segmentation: A Dynamic Control Strategy Approach,
CVGIP(32), No. 1, October 1985, pp. 104-126.
Elsevier DOI
Segmentation, Expert system. Control aspects of rule based segmentation
See also Low Level Image Segmentation: An Expert System.
BibRef
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Levine, M.D.[Martin D.],
Nazif, A.M.[Ahmed M.],
An Optimal Set of Image Segmentation Rules,
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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.
Elsevier DOI 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
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BibRef
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Jiang, H.,
Toriwaki, J.,
Suzuki, H.,
Comparative Performance Evaluation of Segmentation Methods Based
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SCJ(24), No. 13, 1993, pp. 28-42.
Evaluation, Segmentation.
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Evaluation, Segmentation.
Survey, Segmentation.
Segmentation, Evaluation.
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Towards Visually Convincing Image Segmentation,
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Elsevier DOI
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Evaluation, Segmentation.
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9400
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Comparison of thresholding techniques using synthetic images and
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ICPR92(III:209-213).
IEEE DOI
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BibRef
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0204
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Objective Evaluation of Relative Segmentation Quality,
ICIP00(Vol I: 308-311).
IEEE DOI
0008
BibRef
Correia, P.L.[Paulo Lobato],
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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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Simultaneous Truth and Performance Level Estimation (STAPLE):
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0407
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Optimal MAP Parameters Estimation in STAPLE: Learning from Performance
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Benoit-Cattin, H.,
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PR(37), No. 9, September 2004, pp. 1785-1795.
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0407
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IP(13), No. 8, August 2004, pp. 1092-1103.
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Evaluation, Video Segmentation. Automatic evaluation of segmentations.
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Image segmentation; Video segmentation; Performance evaluation;
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Evaluation, Classifiers. Image classification; Image segmentation; Uncertain environment; Expert fusion
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Evaluation, Segmentation.
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Earlier:
A Novel Segmentation Strategy Based on Colour Channels Coupling,
CIAP05(328-335).
Springer DOI
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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
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IEEE DOI
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Evaluation, Segmentation.
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See also Phenomenological Model of Diffuse Global and Regional Atrophy Using Finite-Element Methods.
BibRef
Chang, C.I.,
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Wang, J.,
Guo, S.M.,
Thouin, P.D.,
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Survey, Segmentation.
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Evaluation, Segmentation.
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Toward Objective Evaluation of Image Segmentation Algorithms,
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IEEE DOI
0704
Evaluation, Segmentation.
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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.
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CMU-RI-TR-08-23, May, 2008.
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Barnard, K.[Kobus],
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Dataset, Segmentation. Dataset with hand segmentations.
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Image segmentation evaluation: A survey of unsupervised methods,
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0804
Evaluation, Segmentation.
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Empirical goodness measure
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Goldman, S.A.[Sally A.],
Fritts, J.E.[Jason E.],
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See also Localized Content-Based Image Retrieval.
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Image Segmentation Using Topological Persistence,
CAIP07(587-595).
Springer DOI
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Perceptual Information of Images and the Bias in Homogeneity-based
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PercOrg06(181).
IEEE DOI
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Polak, M.[Mark],
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An evaluation metric for image segmentation of multiple objects,
IVC(27), No. 8, 2 July 2009, pp. 1223-1227.
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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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Quantitative Evaluation of a Novel Image Segmentation Algorithm,
CVPR05(II: 1132-1139).
IEEE DOI
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Evaluation, Segmentation.
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1107
Model segmenttion results based on data quality.
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Dogra, D.P.,
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Sural, S.,
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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
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Gavet, Y.[Yann],
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Color; Image segmentation; Tensor voting; Clustering; Feature space
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IEEE DOI
1102
For segmentation. Also k-means and mean shift. For constant and varying
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Xue, J.H.[Jing-Hao],
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Image thresholding; Iterative selection; Discriminant analysis;
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See also Picture Thresholding Using an Iterative Selection Method.
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Object-based image analysis; Image segmentation; Discrepancy measures;
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Image understanding; Subjective evaluation; Object localization; Object
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IEEE DOI Ground truth; image segmentation evaluation; segmentation database
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Image segmentation
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Image processing
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Approximation methods
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Lucchi, A.[Aurelien],
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See also Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation.
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Databases, Feature extraction, Image segmentation, Imaging,
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Benchmark testing, Image segmentation,
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BibRef
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Multi-image Segmentation:
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Springer DOI
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Binary Partition Tree, Morphological hierarchies,
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Binary partition tree, Object segmentation,
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Buildings, Image segmentation, Indexes, Measurement, Remote sensing,
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Image segmentation, Quality control, Object segmentation,
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BibRef
Earlier: A1, A3, A4, A5, Only:
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IEEE DOI
1612
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Semantics, Measurement, Image segmentation, Databases,
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Meta-learning, Image segmentation, Gradient-based techniques,
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Geographic object-based image analysis (GOBIA), OBIA,
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Image segmentation, Benchmark, Low-level vision
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Semantics, Cameras, Image segmentation, Sensors, Navigation,
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Detectors, Distortion, Neural networks, Measurement,
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Location awareness, Visualization, Codes, Measurement uncertainty,
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2212
BibRef
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CVPR20(2876-2885)
IEEE DOI
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Training, Semantics, Computational modeling, Annotations, Taxonomy,
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Feature extraction, Visualization, Object detection, Training,
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A Novel Segmentation Error Minimization-Based Method for Multilevel
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Segment anything model, Model robustness, Prompting techniques
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Elsevier DOI
2410
Image segmentation, Segmentation evaluation, Evaluation metric
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Bernhard, M.[Maximilian],
Amoroso, R.[Roberto],
Kindermann, Y.[Yannic],
Baraldi, L.[Lorenzo],
Cucchiara, R.[Rita],
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WACV24(957-966)
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2404
Analytical models, Systematics, Error analysis,
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WWW Link.
2403
Measurement, Codes, Machine vision, Filter banks, Object detection,
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Li, Z.J.[Ze-Jian],
Sun, L.Y.[Ling-Yun],
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SAM-Adapter: Adapting Segment Anything in Underperformed Scenes,
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2401
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FemtoDet: An Object Detection Baseline for Energy Versus Performance
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ICCV23(13272-13281)
IEEE DOI
2401
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Mintun, E.[Eric],
Ravi, N.[Nikhila],
Mao, H.Z.[Han-Zi],
Rolland, C.[Chloe],
Gustafson, L.[Laura],
Xiao, T.[Tete],
Whitehead, S.[Spencer],
Berg, A.C.[Alexander C.],
Lo, W.Y.[Wan-Yen],
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Segment Anything,
ICCV23(3992-4003)
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2401
Dataset, Segmentation.
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Shen, T.C.[Tian-Cheng],
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Guo, W.D.[Wei-Dong],
Jia, J.Y.[Jia-Ya],
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High Quality Entity Segmentation,
ICCV23(4024-4033)
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Dataset, Segmentation.
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Jiao, L.C.[Li-Cheng],
Peng, R.[Rui],
Wang, X.[Xinyi],
Zhang, J.[Junpei],
Zhang, K.[Kexin],
Liu, F.[Fang],
Alcover-Couso, R.[Roberto],
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The Robust Semantic Segmentation UNCV2023 Challenge Results,
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IEEE DOI
2401
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Jia, Z.J.[Zhi-Jie],
Chen, L.[Lin],
Hu, K.W.[Kai-Wen],
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Qiao, R.Z.[Rui-Zhi],
Shu, X.J.[Xiu-Jun],
Gan, B.[Bei],
Xu, L.S.[Liang-Sheng],
Ren, B.[Bo],
Xu, M.M.[Meng-Meng],
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Ramachandra, R.[Raghavendra],
Lin, C.W.[Chia-Wen],
Ghanem, B.[Bernard],
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation,
CVPR23(10669-10680)
IEEE DOI
2309
BibRef
Xu, R.K.[Rui-Kang],
Chen, C.[Chang],
Peng, J.Y.[Jing-Yang],
Li, C.[Cheng],
Huang, Y.[Yibin],
Song, F.L.[Feng-Long],
Yan, Y.L.[You-Liang],
Xiong, Z.W.[Zhi-Wei],
Toward RAW Object Detection: A New Benchmark and A New Model,
CVPR23(13384-13393)
IEEE DOI
2309
BibRef
Ji, D.[Deyi],
Zhao, F.[Feng],
Lu, H.T.[Hong-Tao],
Tao, M.Y.[Ming-Yuan],
Ye, J.P.[Jie-Ping],
Ultra-High Resolution Segmentation with Ultra-Rich Context:
A Novel Benchmark,
CVPR23(23621-23630)
IEEE DOI
2309
BibRef
Dreyer, M.[Maximilian],
Achtibat, R.[Reduan],
Wiegand, T.[Thomas],
Samek, W.[Wojciech],
Lapuschkin, S.[Sebastian],
Revealing Hidden Context Bias in Segmentation and Object Detection
through Concept-specific Explanations,
SAIAD23(3829-3839)
IEEE DOI
2309
BibRef
Long, C.[Cheng],
Barbu, A.[Adrian],
A Study of Shape Modeling Against Noise,
ICIP22(611-615)
IEEE DOI
2211
Image segmentation, Shape, Computational modeling,
Shape measurement, Noise reduction, Object segmentation.
BibRef
Upchurch, P.[Paul],
Niu, R.[Ransen],
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene
Parsing,
ECCV22(VIII:450-466).
Springer DOI
2211
Dataset, Segmentation.
BibRef
Qin, X.B.[Xue-Bin],
Dai, H.[Hang],
Hu, X.B.[Xia-Bin],
Fan, D.P.[Deng-Ping],
Shao, L.[Ling],
Van Gool, L.J.[Luc J.],
Highly Accurate Dichotomous Image Segmentation,
ECCV22(XVIII:38-56).
Springer DOI
2211
HTML Version.
See also DIS5K.
BibRef
Gu, J.D.[Jin-Dong],
Zhao, H.S.[Heng-Shuang],
Tresp, V.[Volker],
Torr, P.H.S.[Philip H. S.],
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating
and Boosting Segmentation Robustness,
ECCV22(XXIX:308-325).
Springer DOI
2211
BibRef
Alvarez-Gila, A.[Aitor],
van de Weijer, J.[Joost],
Wang, Y.X.[Ya-Xing],
Garrote, E.[Estibaliz],
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic
Segmentation,
ICIP22(1166-1170)
IEEE DOI
2211
Annotations, Semantics, Propulsion, Cameras, multi-view, cross-view,
semantic segmentation, synthetic dataset
BibRef
Cheng, B.[Bowen],
Girshick, R.[Ross],
Dollár, P.[Piotr],
Berg, A.C.[Alexander C.],
Kirillov, A.[Alexander],
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation,
CVPR21(15329-15337)
IEEE DOI
2111
Image segmentation, Protocols, Measurement uncertainty, Tools,
Size measurement, Performance analysis, Pattern recognition
BibRef
Ma, N.N.[Ning-Ning],
Zhang, X.Y.[Xiang-Yu],
Liu, M.[Ming],
Sun, J.[Jian],
Activate or Not: Learning Customized Activation,
CVPR21(8028-8038)
IEEE DOI
2111
WWW Link.
Code, Training. Image segmentation, Codes, Semantics, Neurons,
Switches, Object detection
BibRef
Savarese, P.[Pedro],
Kim, S.S.Y.[Sunnie S. Y.],
Maire, M.[Michael],
Shakhnarovich, G.[Gregory],
McAllester, D.[David],
Information-Theoretic Segmentation by Inpainting Error Maximization,
CVPR21(4028-4038)
IEEE DOI
2111
Training, Deep learning, Image segmentation,
Computational modeling, Pattern recognition, Task analysis
BibRef
Voigtlaender, P.[Paul],
Luo, L.[Lishu],
Yuan, C.[Chun],
Jiang, Y.[Yong],
Leibe, B.[Bastian],
Reducing the Annotation Effort for Video Object Segmentation Datasets,
WACV21(3059-3068)
IEEE DOI
2106
Training, Annotations, Object segmentation, Manuals, Benchmark testing
BibRef
Zini, S.[Simone],
Buzzelli, M.[Marco],
On the Impact of Rain over Semantic Segmentation of Street Scenes,
MOI2QDN20(597-610).
Springer DOI
2103
First add rain and compare, then for rainy images, derain and test.
BibRef
Cappabianco, F.A.M.,
Ribeiro, P.F.O.,
de Miranda, P.A.V.,
Udupa, J.K.,
A General and Balanced Region-Based Metric for Evaluating Medical
Image Segmentation Algorithms,
ICIP19(1525-1529)
IEEE DOI
1910
Dice coefficient, Jaccard coefficient, image segmentation, image evaluation
BibRef
Follmann, P.[Patrick],
Böttger, T.[Tobias],
Härtinger, P.[Philipp],
König, R.[Rebecca],
Ulrich, M.[Markus],
MVTec D2S: Densely Segmented Supermarket Dataset,
ECCV18(X: 581-597).
Springer DOI
1810
Dataset, Segmentation.
BibRef
Malladi, S.R.S.P.,
Ram, S.,
Rodríguez, J.J.,
A Ground-Truth Fusion Method for Image Segmentation Evaluation,
Southwest18(137-140)
IEEE DOI
1809
Image segmentation, Manuals, Indexes, Signal processing algorithms,
Expectation-maximization algorithms, Q measurement, Ground-truth,
segmentation
BibRef
Filho, S.S.[Sérgio Sousa],
Flores, F.C.[Franklin César],
Attribute Operators for Color Images: Image Segmentation Improved by
the Use of Unsupervised Segmentation Evaluation Methods,
ISMM17(249-260).
Springer DOI
1706
BibRef
Shi, W.,
Meng, F.,
Wu, Q.,
Segmentation quality evaluation based on multi-scale convolutional
neural networks,
VCIP17(1-4)
IEEE DOI
1804
convolution, feedforward neural nets, image segmentation,
regression analysis, image segmentation,
Segmentation
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
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
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
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.J.[Wen-Jia],
Rueckert, D.[Daniel],
Patch-Based Evaluation of Image Segmentation,
CVPR14(3065-3072)
IEEE DOI
1409
evaluation; image segmentation; medical; patch-based; similarity measure
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
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.
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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
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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
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
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Akbas, C.E.[Cem Emre],
Ulman, V.[Vladimír],
Maka, M.[Martin],
Jug, F.[Florian],
Kozubek, M.[Michal],
Automatic Fusion of Segmentation and Tracking Labels,
BioIm18(VI:446-454).
Springer DOI
1905
BibRef
Maka, 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
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Maka, M.[Martin],
Matula, P.[Pavel],
A Fast Level Set-Like Algorithm with Topology Preserving Constraint,
CAIP09(930-938).
Springer DOI
0909
BibRef
Maka, 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
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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
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Hafiane, A.[Adel],
Chabrier, S.[Sébastien],
Rosenberger, C.[Christophe],
Laurent, H.[Hélčne],
A New Supervised Evaluation Criterion for Region Based Segmentation
Methods,
ACIVS07(439-448).
Springer DOI
0708
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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
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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
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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
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Vergés-Llahí, J.[Jaume],
Sanfeliu, A.[Alberto],
Evaluation of Distances Between Color Image Segmentations,
IbPRIA05(II:263).
Springer DOI
0509
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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
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Gelasca, E.D.[Elisa Drelie],
Ebrahimi, T.[Touradj],
Farias, M.C.Q.[Mylčne 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).
IEEE DOI
0411
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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.,
Muńoz, 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],
Lřnnestad, T.[Tor],
Grřttum, 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 .