Shaheen, S.I.[Samir I.],
Levine, M.D.[Martin D.],
Some Experiments with the Interpretation Strategy of a Modular Computer
Vision System,
PR(14), No. 1-6, 1981, pp. 87-90.
Elsevier DOI
0309
BibRef
Levine, M.D., and
Shaheen, S.I.,
A Modular Computer Vision System for Picture Segmentation
and Interpretation,
PAMI(3), No. 5, September 1981, pp. 540-556.
BibRef
8109
Earlier:
A Modular Computer Vision System for Picture Segmentation,
PRIP79(523-539).
BibRef
Kropatsch, W.G.,
Segmentation of Digital Images Using a Priori Information about
the Expected Image Contents,
PDA83(107-132).
BibRef
8300
Zamperoni, P.[Piero],
Model-Based Segmentation of Grey-Tone Images,
IVC(2), No. 3, August 1984, pp. 123-133.
Elsevier DOI
BibRef
8408
Zamperoni, P.[Piero],
Feature Extraction by Rank-Order Filtering for Image Segmentation,
PRAI(2), 1988, pp. 301-319.
BibRef
8800
Zamperoni, P.[Piero],
Some Adaptive Rank Order Filters for Image Enhancement,
PRL(11), 1990, pp. 81-86.
BibRef
9000
Zamperoni, P.[Piero],
Mode Estimation and Non-Linear Image Smoothing with
Adaptive Rank-Order Filters,
Draft1989.
BibRef
8900
Hyde, J.,
Fullwood, J.A.,
Corrall, D.R.,
An Approach to Knowledge-Driven Segmentation,
IVC(3), No. 4, November 1985, pp. 198-205.
Elsevier DOI
BibRef
8511
Matsuyama, T.[Takashi],
Expert Systems for Image Processing: Knowledge-Based
Composition of Image Analysis Processes,
CVGIP(48), No. 1, October 1989, pp. 22-49.
Elsevier DOI
BibRef
8910
Earlier:
ICPR88(I: 125-133).
IEEE DOI
8811
Rule Based Systems.
System: SIGMA. This builds on the general systems such as SIGMA and is directed
toward segmentation.
BibRef
Nagao, M.[Makoto],
Matsuyama, T.[Takashi],
Ikeda, Y.[Yoshio],
Region Extraction and Shape Analysis in Aerial Photographs,
CGIP(10), No. 3, July 1979, pp. 195-223.
Elsevier DOI
BibRef
7907
Earlier:
Region Extraction and Shape Analysis of Aerial Photographs,
ICPR78(620-628).
This uses a global to detailed analysis technique.
BibRef
Tenenbaum, J.M., and
Barrow, H.G.,
Experiments in Interpretation Guided Segmentation,
AI(8), No. 3, June 1977, pp. 241-274.
Elsevier DOI
BibRef
7706
And:
SRI AICenter-TN 123, March 1976.
BibRef
And:
IGS: A Paradigm for Integrating Image Segmentation and
Interpretation,
PRAI-76(472-507).
BibRef
And:
ICPR76(504-513).
BibRef
And:
CMetImAly77(435-444).
Segmentation, Knowledge.
System: IGS. The key idea is that image elements can be reliably clustered into
regions if semantic interpretations are used in addition to the raw
image values. This builds on the interpretation ideas of MSYS
(
See also MSYS: A System for Reasoning about Scenes. ).
Unlike the work in Yakimovsky and Feldman, the relations between
different types of regions are either possible or impossible.
Initial interpretations are based on the image data, but extra
interpretations at this point are not harmful. An iterative
procedure is used to eliminate interpretations that are not valid
given all the possible interpretations of the neighbors. When
adjacent regions have the same interpretation they can be merged.
This method requires a very specific model of the possible scene to
provide any benefit.
BibRef
Chassery, J.M., and
Garbay, C.,
An Iterative Segmentation Method Based on a Contextual Color and
Shape Criterion,
PAMI(6), No. 6, November 1984, pp. 794-799.
BibRef
8411
Earlier:
ICPR84(642-644).
Segmentation, Color.
BibRef
Garbay, C.,
Image Structure Representation and Processing:
A Discussion of Some Segmentation Methods in Cytology,
PAMI(8), No. 2, March 1986, pp. 140-146.
BibRef
8603
Earlier:
Knowledge and Strategies for Image Segmentation,
ICPR86(669-671).
BibRef
Vasselle, B.,
Giraudon, G.,
A Multiscale Regularity Measure as a Geometric Criterion for
Image Segmentation,
MVA(7), No. 4, 1994, pp. 229-236.
BibRef
9400
Houzelle, S., and
Giraudon, G.,
Model Based Region Segmentation Using Cooccurrence Matrices,
CVPR92(636-639).
IEEE DOI Base segmentation on second order statistics.
BibRef
9200
Tucker, L.W., (Cornell and Polytechnic I of NY)
Model-Guided Segmentation Using Quadtrees,
ICPR84(216-219).
BibRef
8400
And:
Control Strategy for an Expert Vision System Using Quadtree Refinement,
CVWS84(214-218).
Innovative use of quadtrees in segmentation.
BibRef
Kestner, W.,
Segmentation and Abstract Interpretation in an Image
Understanding System,
ICPR82(1011-1013).
BibRef
8200
Earlier:
Considerations About Knowledge-Based Image Interpretation,
ICPR80(330-332).
High level discussion of a high level system for image analysis
using different descriptions and the different levels of
abstraction and different programs to link between the
different levels.
BibRef
Kestner, W.,
Bohner, M.,
Scharf, R., and
Sties, M.,
Object Guided Segmentation of Aerial Images,
ICPR80(529-531).
(Karlsruhe). Starting from (interactively selected)
elements (line segments, rough contours, centers of regions) expand
to complete lines or regions. Interactive. Basically grow the
object as long as the feature values remain constant or change
gradually. Boundaries between regions handled by pixel level
analysis - initial expansion is based on features over larger
areas.
BibRef
8000
Selfridge, P.G.,
Sloan, Jr., K.R.,
Reasoning About Images:
Using Meta-Knowledge in Aerial Image Understanding,
IJCAI81(755-757).
BibRef
8100
Earlier:
Reasoning About Images: Application to Aerial Image Understanding,
DARPA81(1-6).
BibRef
And:
Locating Objects under Different Conditions:
An Example in Aerial Image Understanding,
PRIP81(470-472).
(Rochester). Segmentation and analysis system to use partial
results to perform matching then update parameters for
segmentation. Given an appearance model, an initial threshold
based segmentation is applied, e.g., building and shadow.
Candidate regions are matched to select most likely, and the
threshold is varied to find the region which produces the best
match. Extensive use of partial matching results.
BibRef
Selfridge, P.G.,
Reasoning About Success and Failure in Aerial Image Understanding,
Ph.D.Thesis (CS), May 1983,
BibRef
8305
Univ. of Rochester-TR-103.
BibRef
And:
with
Sloan, Jr., K.R.,
PRIP82(44-49).
Segmentation problems: what to look for, which technique to use,
and the parameters for the procedure. Goal directed processing at
every stage. Given a model of the objects, pick a segmentation
method (thresholding), set arbitrary thresholds (almost), change
them to get candidate regions, evaluate based on other features.
Then in conjunction with higher level model descriptions composed
into groups or structures and look at where other pieces can
occur.
BibRef
Sloan, Jr., K.R.,
Representation and Communication of Image-Related Information,
DARPAN79(136-139).
BibRef
7900
Mota, F.A., and
Velasco, F.R.D.,
A Method for the Analysis of Ambiguous Segmentations of Images,
PAMI(8), No. 6, November 1986, pp. 755-760.
Seems to be a discussion of using the model (interpretation)
information to eliminate segmentation "errors."
BibRef
8611
Mason, P.,
Buggy, T.W.,
Knowledge-Based Segmentation of Sonar Data,
IVC(5), No. 2, May 1987, pp. 127-131.
Elsevier DOI
BibRef
8705
Sunil Kumar, K.,
Desai, U.B.,
Joint segmentation and image interpretation,
PR(32), No. 4, April 1999, pp. 577-589.
Elsevier DOI
BibRef
9904
Earlier:
ICIP96(I: 853-856).
IEEE DOI
9610
See also Image Interpretation Using Bayesian Networks.
BibRef
Kopparapu, S.I.K.[Sun-Il K.],
Desai, U.B.[Uday B.],
Bayesian Approach to Image Interpretation,
KluwerBoston, July 2001.
ISBN 0-7923-7372-3.
WWW Link. Combine segmentation and interpretation modules.
BibRef
0107
Figov, Z.,
Tal, Y., and
Koppel, M.[Moshe],
Detecting and Removing Shadows,
ICCGI04(xx-yy).
PDF File.
BibRef
0400
Kamath, N.[Nidish],
Kopparapu, S.I.K.[Sun-Il K.],
Desai, U.B.,
Dugud, R.[Rakesh],
Joint Segmentation and Image Interpretation Using Hidden Markov Models,
ICPR98(Vol II: 1840-1842).
IEEE DOI
9808
BibRef
Molander, S.,
Broman, H.,
Knowledge-based segmentation and state-based control in image analysis:
Two examples from the biomedical domain,
SP(32), No. 1-2, 1993, pp. 201-215.
BibRef
9300
Ezquerra, N.[Norberto],
Mullick, R.[Rakesh],
Knowledge-Guided Segmentation of 3D Imagery,
GMIP(58), No. 6, November 1996, pp. 510-523.
9701
BibRef
Ezquerra, N.[Norberto],
Mullick, R.[Rakesh],
An Approach to 3D Pose Estimation,
TOG(15), No. 2, April 1996, pp. 99-120.
BibRef
9604
Haker, S.,
Sapiro, G.,
Tannenbaum, A.,
Knowledge-Based Segmentation of SAR Data with Learned Priors,
IP(9), No. 2, February 2000, pp. 299-301.
IEEE DOI
0003
BibRef
Baujard, O.,
Garbay, C.,
KISS: A Multiagent Segmentation System,
OptEng(32), No. 6, June 1993, pp. 1235-1249.
BibRef
9306
Manon, G.,
Pesty, S.,
Garbay, C.,
KIDS (knowledge-based diagnosis system)-a specialized architecture,
ICPR88(II: 995-997).
IEEE DOI
8811
BibRef
Rushing, J.A.,
Ranganath, H.,
Hinke, T.H.,
Graves, S.J.,
Image segmentation using association rule features,
IP(11), No. 5, May 2002, pp. 558-567.
IEEE DOI
0206
BibRef
Evans, C.,
Jones, R.,
Svalbe, I.,
Berman, M.,
Segmenting Multispectral Landsat TM Images into Field Units,
GeoRS(40), No. 5, May 2002, pp. 1054-1064.
IEEE Top Reference.
0206
BibRef
Goldberger, J.[Jacob],
Greenspan, H.[Hayit],
Context-Based Segmentation of Image Sequences,
PAMI(28), No. 3, March 2006, pp. 463-468.
IEEE DOI
0602
New frames segmented based on the segmentation of prior frames.
BibRef
Eriksson, A.P.[Anders P.],
Olsson, C.[Carl],
Kahl, F.[Fredrik],
Normalized Cuts Revisited:
A Reformulation for Segmentation with Linear Grouping Constraints,
JMIV(39), No. 1, January 2011, pp. 45-61.
WWW Link.
1101
BibRef
Earlier:
ICCV07(1-8).
IEEE DOI
0710
BibRef
And:
Image Segmentation with Context,
SCIA07(283-292).
Springer DOI
0706
BibRef
Toyoda, T.[Takahiro],
Hasegawa, O.[Osamu],
Random Field Model for Integration of Local Information and Global
Information,
PAMI(30), No. 8, August 2008, pp. 1483-1489.
IEEE DOI
0806
BibRef
Toyoda, T.[Takahiro],
Tagami, K.[Keisuke],
Hasegawa, O.[Osamu],
Integration of Top-down and Bottom-up Information for Image Labeling,
CVPR06(I: 1106-1113).
IEEE DOI
0606
BibRef
Rahmani, R.[Rouhollah],
Goldman, S.A.[Sally A.],
Zhang, H.[Hui],
Cholleti, S.R.[Sharath R.],
Fritts, J.E.[Jason E.],
Localized Content-Based Image Retrieval,
PAMI(30), No. 11, November 2008, pp. 1902-1912.
IEEE DOI
0809
System, ACCIO. Interested only in part of the iamge.
Extend traditional segmentation-based and salient point-based techniques
to capture content.
Salient points using
SPARSE (filtered Haar-wavelet points)
Wavelet (Variably Split Window with Neighbor)
SIFT (
See also Distinctive Image Features from Scale-Invariant Keypoints. )
BibRef
Zhang, H.[Hui],
Goldman, S.A.[Sally A.],
Image Segmentation using Salient Points-Based Object Templates,
ICIP06(765-768).
IEEE DOI
0610
BibRef
Gould, S.[Stephen],
Rodgers, J.[Jim],
Cohen, D.[David],
Elidan, G.[Gal],
Koller, D.[Daphne],
Multi-Class Segmentation with Relative Location Prior,
IJCV(80), No. 3, December 2008, pp. xx-yy.
Springer DOI
0810
Using spatial relations in segmentation.
BibRef
Gould, S.[Stephen],
Fulton, R.[Richard],
Koller, D.[Daphne],
Decomposing a scene into geometric and semantically consistent regions,
ICCV09(1-8).
IEEE DOI
0909
BibRef
Gould, S.[Stephen],
Multiclass pixel labeling with non-local matching constraints,
CVPR12(2783-2790).
IEEE DOI
1208
BibRef
Crevier, D.[Daniel],
Image segmentation algorithm development using ground truth image data
sets,
CVIU(112), No. 2, November 2008, pp. 143-159.
Elsevier DOI
0811
BibRef
Earlier:
Extracting Salient Objects from Operator-Framed Images,
CRV07(36-43).
IEEE DOI
0705
Image segmentation; Performance measures; Ground
truth images; Image data sets; Parameter learning; Texture; Edge
detection; Region growing; Data engineering tools and techniques;
Stochastic optimization
BibRef
Borenstein, E.[Eran],
Ullman, S.[Shimon],
Combined Top-Down/Bottom-Up Segmentation,
PAMI(30), No. 12, December 2008, pp. 2109-2125.
IEEE DOI
0811
BibRef
Earlier:
Learning to Segment,
ECCV04(Vol III: 315-328).
Springer DOI
0405
BibRef
Earlier:
Class-Specific, Top-Down Segmentation,
ECCV02(II: 109 ff.).
Springer DOI
0205
Guided by a stored representation of the shape.
Find the foreground objects, they generally are more fragmented (textured?).
BibRef
Borenstein, E.[Eran],
Sharon, E.[Eitan],
Ullman, S.[Shimon],
Combining Top-Down and Bottom-Up Segmentation,
PercOrg04(46).
IEEE DOI
0502
BibRef
Ross, M.G.[Michael G.],
Kaelbling, L.P.[Leslie Pack],
Segmentation According to Natural Examples:
Learning Static Segmentation from Motion Segmentation,
PAMI(31), No. 4, April 2009, pp. 661-676.
IEEE DOI
0903
BibRef
Earlier:
Learning object segmentation from video data,
MIT AIM-2003-022, September 8, 2003.
WWW Link.
0501
Trained on video that uses background subtraction to find objects in video.
Then segment static scene using learned values.
BibRef
Burrus, N.[Nicolas],
Bernard, T.M.[Thierry M.],
Jolion, J.M.[Jean-Michel],
Image segmentation by a contrario simulation,
PR(42), No. 7, July 2009, pp. 1520-1532.
Elsevier DOI
0903
BibRef
Earlier:
Bottom-Up and Top-Down Object Matching Using Asynchronous Agents and a
Contrario Principles,
CVS08(xx-yy).
Springer DOI
0805
Segmentation; A contrario reasoning; Statistical image processing;
Monte-Carlo simulation.
hierarchy of independent agents.
Strength based on relevance of visual data.
BibRef
Burrus, N.[Nicolas],
Bernard, T.M.[Thierry M.],
Adaptive Vision Leveraging Digital Retinas:
Extracting Meaningful Segments,
ACIVS06(220-231).
Springer DOI
0609
Extract segments that depart from the norm.
BibRef
Sabuncu, M.R.,
Yeo, B.T.T.,
van Leemput, K.,
Fischl, B.,
Golland, P.,
A Generative Model for Image Segmentation Based on Label Fusion,
MedImg(29), No. 10, October 2010, pp. 1714-1729.
IEEE DOI
1011
Segmentation given a training set. Pairwise registration between
image and label maps.
BibRef
Wachinger, C.,
Fritscher, K.,
Sharp, G.,
Golland, P.,
Contour-Driven Atlas-Based Segmentation,
MedImg(34), No. 12, December 2015, pp. 2492-2505.
IEEE DOI
1601
Bayes methods
BibRef
Iglesias, J.E.[Juan Eugenio],
Sabuncu, M.R.[Mert Rory],
van Leemput, K.[Koen],
A Generative Model for Probabilistic Label Fusion of Multimodal Data,
MBIA12(115-133).
Springer DOI
1210
BibRef
Sabuncu, M.R.,
van Leemput, K.,
The Relevance Voxel Machine (RVoxM):
A Self-Tuning Bayesian Model for Informative Image-Based Prediction,
MedImg(31), No. 12, December 2012, pp. 2290-2306.
IEEE DOI
1212
BibRef
Li, T.[Teng],
Yan, S.C.[Shui-Cheng],
Mei, T.[Tao],
Hua, X.S.[Xian-Sheng],
Kweon, I.S.[In-So],
Image Decomposition With Multilabel Context:
Algorithms and Applications,
IP(20), No. 8, August 2011, pp. 2301-2314.
IEEE DOI
1108
Segmentation. Context from multiple images.
BibRef
Chen, X.Y.[Xiang-Yu],
Yuan, X.T.[Xiao-Tong],
Chen, Q.A.[Qi-Ang],
Yan, S.C.[Shui-Cheng],
Chua, T.S.[Tat-Seng],
Multi-label visual classification with label exclusive context,
ICCV11(834-841).
IEEE DOI
1201
BibRef
Li, T.[Teng],
Mei, T.[Tao],
Yan, S.C.[Shui-Cheng],
Kweon, I.S.[In-So],
Lee, C.W.[Chil-Woo],
Contextual decomposition of multi-label images,
CVPR09(2270-2277).
IEEE DOI
0906
Context for segmentation.
BibRef
Scheffler, C.[Carl],
Odobez, J.M.[Jean-Marc],
Joint Adaptive Colour Modelling and Skin, Hair and Clothes Segmentation
using Coherent Probabilistic Index Maps,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Zheng, W.S.[Wei-Shi],
Gong, S.G.[Shao-Gang],
Xiang, T.[Tao],
Quantifying and Transferring Contextual Information in Object Detection,
PAMI(34), No. 4, April 2012, pp. 762-777.
IEEE DOI
1203
BibRef
Earlier:
Unsupervised Selective Transfer Learning for Object Recognition,
ACCV10(II: 527-541).
Springer DOI
1011
BibRef
Earlier:
Quantifying contextual information for object detection,
ICCV09(932-939).
IEEE DOI
0909
Context model without prior segmentation of context annotation.
See also Learning Behavioural Context.
BibRef
Liu, S.,
Yan, S.,
Zhang, T.,
Xu, C.,
Liu, J.,
Lu, H.,
Weakly Supervised Graph Propagation Towards Collective Image Parsing,
MultMed(14), No. 2, 2012, pp. 361-373.
IEEE DOI
1204
Assign annotated labels to contextually derived semantic regions.
BibRef
Alush, A.[Amir],
Goldberger, J.[Jacob],
Ensemble Segmentation Using Efficient Integer Linear Programming,
PAMI(34), No. 10, October 2012, pp. 1966-1977.
IEEE DOI
1208
Combine several segmentations into one.
BibRef
Reinbacher, C.,
Rüther, M.,
Bischof, H.,
Fast variational multi-view segmentation through backprojection of
spatial constraints,
IVC(30), No. 11, November 2012, pp. 797-807.
Elsevier DOI
1211
Multi-view; Variational segmentation; GPU; Catadioptric
BibRef
Kim, S.,
Nowozin, S.,
Kohli, P.,
Yoo, C.D.,
Task-Specific Image Partitioning,
IP(22), No. 2, February 2013, pp. 488-500.
IEEE DOI
1302
BibRef
Kim, S.,
Yoo, C.D.,
Nowozin, S.,
Kohli, P.,
Image Segmentation Using Higher-Order Correlation Clustering,
PAMI(36), No. 9, September 2014, pp. 1761-1774.
IEEE DOI
1408
Clustering algorithms
BibRef
Liu, Y.C.[Yen-Cheng],
Chen, H.T.[Hwann-Tzong],
Unsupervised scene segmentation using sparse coding context,
MVA(24), No. 2, February 2013, pp. 243-254.
WWW Link.
1302
Semantically meaningful regions without user annotation.
BibRef
Chung, F.[Francois],
Delingette, H.[Herve],
Regional appearance modeling based on the clustering of intensity
profiles,
CVIU(117), No. 6, June 2013, pp. 705-717.
Elsevier DOI
1304
Appearance modeling; Unsupervised clustering; Model-based image
segmentation; Medical imaging
BibRef
Chen, Q.A.[Qi-Ang],
He, C.J.[Chuan-Jiang],
Variational segmentation model for images with intensity inhomogeneity
and Poisson noise,
JIVP(2013), No. 1, 2013, pp. xx-yy.
DOI Link
1306
BibRef
Chen, Q.A.[Qi-Ang],
He, C.J.[Chuan-Jiang],
Integrating clustering with level set method for piecewise constant
Mumford-Shah model,
JIVP(2014), No. 1, 2014, pp. 1.
DOI Link
1402
BibRef
Zhou, Q.[Quan],
Zhu, J.[Jun],
Liu, W.Y.[Wen-Yu],
Learning Dynamic Hybrid Markov Random Field for Image Labeling,
IP(22), No. 6, 2013, pp. 2219-2232.
IEEE DOI
1307
Markov processes; tree searching; Deformable models; Histograms
BibRef
Zhou, Q.[Quan],
Yan, C.X.[Can-Xiang],
Zhu, Y.Y.[Ying-Ying],
Bai, X.[Xiang],
Liu, W.Y.[Wen-Yu],
Image labeling by multiple segmentation,
ICIP11(3129-3132).
IEEE DOI
1201
BibRef
Zhou, Q.[Quan],
Liu, W.Y.[Wen-Yu],
Inference Scene Labeling by Incorporating Object Detection with
Explicit Shape Model,
ACCV10(III: 382-395).
Springer DOI
1011
Shape, texture and context. Segment and label the regions.
BibRef
Khan, A.A.[Asmar Azar],
Xydeas, C.[Costas],
Ahmed, H.[Hassan],
Using macroscopic information in image segmentation,
IET-IPR(7), No. 3, 2013, pp. 219-228.
DOI Link
1307
BibRef
Zhou, Y.B.[Ying-Bo],
Nwogu, I.[Ifeoma],
Govindaraju, V.[Venu],
Labeling Spain With Stanford,
IP(22), No. 12, 2013, pp. 5362-5371.
IEEE DOI
1312
computer vision
Outdoor scene decomposition, use the Stanford background data set.
BibRef
Nwogu, I.[Ifeoma],
Govindaraju, V.[Venu],
Brown, C.[Chris],
Syntactic image parsing using ontology and semantic descriptions,
POCV10(41-48).
IEEE DOI
1006
Semantic descriptions, real-world spatial relationships, colors, textures,
shape.
BibRef
Waggoner, J.[Jarrell],
Zhou, Y.J.[You-Jie],
Simmons, J.[Jeff],
de Graef, M.[Marc],
Wang, S.[Song],
3D Materials Image Segmentation by 2D Propagation:
A Graph-Cut Approach Considering Homomorphism,
IP(22), No. 12, 2013, pp. 5282-5293.
IEEE DOI
1312
graph theory
BibRef
Waggoner, J.[Jarrell],
Zhou, Y.J.[You-Jie],
Simmons, J.[Jeff],
de Graef, M.[Marc],
Wang, S.[Song],
Topology-Preserving Multi-label Image Segmentation,
WACV15(1084-1091)
IEEE DOI
1503
Cost function
BibRef
Luo, B.[Bing],
Huang, C.[Chao],
Ma, L.[Lei],
Li, W.[Wei],
Wu, Q.B.[Qing-Bo],
Foreground Segmentation via Dynamic Programming,
IEICE(E97-D), No. 10, October 2014, pp. 2818-2822.
WWW Link.
1411
Segment object of specific class based on deformable part model.
BibRef
Luo, B.[Bing],
Li, H.,
Meng, F.,
Wu, Q.B.[Qing-Bo],
Huang, C.,
Video Object Segmentation via Global Consistency Aware Query Strategy,
MultMed(19), No. 7, July 2017, pp. 1482-1493.
IEEE DOI
1706
History, Manuals, Motion segmentation, Object segmentation,
Proposals, Tracking, Video sequences, Energy minimization,
global consistency, video, object, segmentation
BibRef
Luo, B.[Bing],
Li, H.L.[Hong-Liang],
Meng, F.M.[Fan-Man],
Wu, Q.B.[Qing-Bo],
Ngan, K.N.[King N.],
An Unsupervised Method to Extract Video Object via Complexity
Awareness and Object Local Parts,
CirSysVideo(28), No. 7, July 2018, pp. 1580-1594.
IEEE DOI
1807
Complexity theory, Image segmentation, Motion segmentation,
Object segmentation, Proposals, Streaming media, Video sequences,
video segmentation
BibRef
Rezvanifar, A.,
Khosravifard, M.,
Including the Size of Regions in Image Segmentation by Region-Based
Graph,
IP(23), No. 2, February 2014, pp. 635-644.
IEEE DOI
1402
computational complexity
BibRef
He, H.[Hu],
Upcroft, B.,
Automatic object segmentation of unstructured scenes using colour and
depth maps,
IET-CV(8), No. 1, February 2014, pp. 45-53.
DOI Link
1404
Markov processes
BibRef
You, X.G.[Xin-Ge],
Li, Q.A.[Qi-Ang],
Tao, D.C.[Da-Cheng],
Ou, W.H.[Wei-Hua],
Gong, M.M.[Ming-Ming],
Local Metric Learning for Exemplar-Based Object Detection,
CirSysVideo(24), No. 8, August 2014, pp. 1265-1276.
IEEE DOI
1410
computer vision
BibRef
Liu, Y.M.[Yong-Mei],
Wongwitit, T.[Tanakrit],
Yu, L.[Linsen],
Automatic Image Annotation Based on Scene Analysis,
IJIG(14), No. 03, 2014, pp. 1450012.
DOI Link
1410
BibRef
Sanroma, G.[Gerard],
Wu, G.R.[Guo-Rong],
Gao, Y.Z.[Yao-Zong],
Shen, D.G.[Ding-Gang],
Learning to Rank Atlases for Multiple-Atlas Segmentation,
MedImg(33), No. 10, October 2014, pp. 1939-1953.
IEEE DOI
1411
BibRef
And:
Correction:
MedImg(33), No. 11, November 2014, pp. 2210-2210.
IEEE DOI
1411
BibRef
Earlier:
Learning-Based Atlas Selection for Multiple-Atlas Segmentation,
CVPR14(3111-3117)
IEEE DOI
1409
Atlas selection. image segmentation. Biomedical imaging.
BibRef
Nie, D.[Dong],
Shen, D.G.[Ding-Gang],
Adversarial Confidence Learning for Medical Image Segmentation and
Synthesis,
IJCV(128), No. 10-11, November 2020, pp. 2494-2513.
Springer DOI
2009
BibRef
Guillaumin, M.[Matthieu],
Küttel, D.[Daniel],
Ferrari, V.[Vittorio],
ImageNet Auto-Annotation with Segmentation Propagation,
IJCV(110), No. 1, December 2014, pp. 328-348.
Springer DOI
1411
BibRef
Earlier: A2, A1, A3:
Segmentation Propagation in ImageNet,
ECCV12(VII: 459-473).
Springer DOI
1210
Award, ECCV.
BibRef
Kuettel, D.[Daniel],
Ferrari, V.[Vittorio],
Learning to approximate global shape priors for figure-ground
segmentation,
BMVC13(xx-yy).
DOI Link
1402
BibRef
Seyedhosseini, M.[Mojtaba],
Tasdizen, T.[Tolga],
Disjunctive normal random forests,
PR(48), No. 3, 2015, pp. 976-983.
Elsevier DOI
1412
Random forest
BibRef
Sajjadi, M.[Mehdi],
Seyedhosseini, M.[Mojtaba],
Tasdizen, T.[Tolga],
Nonlinear Regression with Logistic Product Basis Networks,
SPLetters(22), No. 8, August 2015, pp. 1011-1015.
IEEE DOI
1502
BibRef
Earlier: A2, A1, A3:
Image Segmentation with Cascaded Hierarchical Models and Logistic
Disjunctive Normal Networks,
ICCV13(2168-2175)
IEEE DOI
1403
Contextual information.
regression analysis.
BibRef
Seyedhosseini, M.[Mojtaba],
Tasdizen, T.[Tolga],
Semantic Image Segmentation with Contextual Hierarchical Models,
PAMI(38), No. 5, May 2016, pp. 951-964.
IEEE DOI
1604
edge detection
BibRef
Liu, T.,
Seyedhosseini, M.[Mojtaba],
Tasdizen, T.[Tolga],
Image Segmentation Using Hierarchical Merge Tree,
IP(25), No. 10, October 2016, pp. 4596-4607.
IEEE DOI
1610
image classification
BibRef
Kim, J.[Jaechul],
Grauman, K.[Kristen],
Boundary Preserving Dense Local Regions,
PAMI(37), No. 5, May 2015, pp. 931-943.
IEEE DOI
1504
BibRef
Earlier:
CVPR11(1553-1560).
IEEE DOI
1106
Detectors
BibRef
Lee, Y.J.[Yong Jae],
Grauman, K.[Kristen],
Collect-cut:
Segmentation with top-down cues discovered in multi-object images,
CVPR10(3185-3192).
IEEE DOI
1006
BibRef
Lee, Y.J.[Yong Jae],
Kim, J.[Jaechul],
Grauman, K.[Kristen],
Key-segments for video object segmentation,
ICCV11(1995-2002).
IEEE DOI
1201
Find possible objects, then find ones with persistent appearance
and motion.
See also Foreground Focus: Unsupervised Learning from Partially Matching Images.
BibRef
Vijayanarasimhan, S.[Sudheendra],
Grauman, K.[Kristen],
Top-down pairwise potentials for piecing together multi-class
segmentation puzzles,
POCV10(25-32).
IEEE DOI
1006
BibRef
Gao, Y.[Yi],
Zhu, L.J.[Liang-Jia],
Cates, J.[Joshua],
MacLeod, R.S.[Rob S.],
Bouix, S.[Sylvain],
Tannenbaum, A.[Allen],
A Kalman Filtering Perspective for Multiatlas Segmentation,
SIIMS(8), No. 2, 2015, pp. 1007-1029.
DOI Link
1507
BibRef
Djelouah, A.[Abdelaziz],
Franco, J.S.[Jean-Sébastien],
Boyer, E.[Edmond],
Le Clerc, F.[François],
Pérez, P.[Patrick],
Sparse Multi-View Consistency for Object Segmentation,
PAMI(37), No. 9, September 2015, pp. 1890-1903.
IEEE DOI
1508
BibRef
Earlier:
Multi-view Object Segmentation in Space and Time,
ICCV13(2640-2647)
IEEE DOI
1403
BibRef
Earlier:
N-tuple Color Segmentation for Multi-View Silhouette Extraction,
ECCV12(V: 818-831).
Springer DOI
1210
Cameras.
multi-view segmentation; segmentation
BibRef
Djelouah, A.[Abdelaziz],
Franco, J.S.[Jean-Sébastien],
Boyer, E.[Edmond],
Pérez, P.[Patrick],
Drettakis, G.,
Cotemporal Multi-View Video Segmentation,
3DV16(360-369)
IEEE DOI
1701
Calibration
BibRef
Ye, L.,
Liu, Z.,
Zhou, X.,
Shen, L.,
Zhang, J.,
Saliency Detection Via Similar Image Retrieval,
SPLetters(23), No. 6, June 2016, pp. 838-842.
IEEE DOI
1606
Boosting. Propogate salience from similar images.
BibRef
Zhou, Q.[Quan],
Zheng, B.Y.[Bao-Yu],
Zhu, W.P.[Wei-Ping],
Latecki, L.J.[Longin Jan],
Multi-scale context for scene labeling via flexible segmentation
graph,
PR(59), No. 1, 2016, pp. 312-324.
Elsevier DOI
1609
Scene labeling
BibRef
Zhang, W.[Wei],
Ngo, C.W.[Chong-Wah],
Cao, X.C.[Xiao-Chun],
Hyperlink-Aware Object Retrieval,
IP(25), No. 9, September 2016, pp. 4186-4198.
IEEE DOI
1609
data mining
BibRef
Liu, F.Y.[Fa-Yao],
Lin, G.S.[Guo-Sheng],
Shen, C.H.[Chun-Hua],
Discriminative Training of Deep Fully Connected Continuous CRFs With
Task-Specific Loss,
IP(26), No. 5, May 2017, pp. 2127-2136.
IEEE DOI
1704
Labeling
Conditional Random Fields.
BibRef
Cordts, M.,
Rehfeld, T.,
Enzweiler, M.[Markus],
Franke, U.[Uwe],
Roth, S.[Stefan],
Tree-Structured Models for Efficient Multi-Cue Scene Labeling,
PAMI(39), No. 7, July 2017, pp. 1444-1454.
IEEE DOI
1706
Detectors, Feature extraction, Histograms, Labeling, Proposals,
Semantics, Vegetation, Scene labeling, automotive, decision forests,
depth cues, segmentation tree, stixels, superpixels
BibRef
Scharwächter, T.[Timo],
Enzweiler, M.[Markus],
Franke, U.[Uwe],
Roth, S.[Stefan],
Efficient Multi-cue Scene Segmentation,
GCPR13(435-445).
Springer DOI
1311
Award, GCPR.
BibRef
Scharwächter, T.[Timo],
Stixel-Based Target Existence Estimation under Adverse Conditions,
GCPR13(225-230).
Springer DOI
1311
BibRef
Dann, C.[Christoph],
Gehler, P.V.[Peter V.],
Roth, S.[Stefan],
Nowozin, S.[Sebastian],
Pottics: The Potts Topic Model for Semantic Image Segmentation,
DAGM12(397-407).
Springer DOI
1209
BibRef
Liu, C.X.[Chun-Xiao],
Ng, M.K.P.[Michael Kwok-Po],
Zeng, T.Y.[Tie-Yong],
Weighted variational model for selective image segmentation with
application to medical images,
PR(76), No. 1, 2018, pp. 367-379.
Elsevier DOI
1801
Selective segmentation
BibRef
Wang, Z.,
Yuan, J.,
Simultaneously Discovering and Localizing Common Objects in Wild
Images,
IP(27), No. 9, September 2018, pp. 4503-4515.
IEEE DOI
1807
feature extraction, graph theory, image classification,
image representation, image retrieval, image segmentation,
unsupervised learning
BibRef
Zhang, Y.Q.[Yong-Qiang],
Bai, Y.C.[Yai-Cheng],
Ding, M.L.[Ming-Li],
Li, Y.Q.[Yong-Qiang],
Ghanem, B.[Bernard],
Weakly-supervised object detection via mining pseudo ground truth
bounding-boxes,
PR(84), 2018, pp. 68-81.
Elsevier DOI
1809
BibRef
And:
Corrigendum:
PR(90), 2019, pp. 483.
Elsevier DOI
1903
Weakly-supervised learning, Object detection,
Pseudo ground truth, Iterative learning, Deep learning
BibRef
Zhang, Y.Q.[Yong-Qiang],
Ding, M.L.[Ming-Li],
Bai, Y.C.[Yan-Cheng],
Xu, M.M.[Meng-Meng],
Ghanem1, B.[Bernard],
Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing
Bounding-Boxes Object Detection,
CirSysVideo(30), No. 4, April 2020, pp. 983-997.
IEEE DOI
2004
Object detection, Detectors, Training data, Benchmark testing,
Task analysis, Visualization, Semantics, Pseudo ground truths,
missing bounding-boxes
BibRef
Aliniya, P.[Parvaneh],
Razzaghi, P.[Parvin],
Parametric and nonparametric context models:
A unified approach to scene parsing,
PR(84), 2018, pp. 165-181.
Elsevier DOI
1809
Scene parsing, Parametric, Nonparametric, Context model,
Co-occurrence graph, Unified framework
BibRef
Zhou, B.L.[Bo-Lei],
Zhao, H.[Hang],
Puig, X.[Xavier],
Xiao, T.[Tete],
Fidler, S.[Sanja],
Barriuso, A.[Adela],
Torralba, A.B.[Antonio B.],
Semantic Understanding of Scenes Through the ADE20K Dataset,
IJCV(127), No. 3, March 2019, pp. 302-321.
Springer DOI
1903
BibRef
Earlier: A1, A2, A3, A5, A6, A7, Only:
Scene Parsing through ADE20K Dataset,
CVPR17(5122-5130)
IEEE DOI
PDF File.
1711
Image segmentation, Labeling, Neural networks,
Semantics, Sun, Visualization
See also ADE20K Dataset.
BibRef
Zhao, H.[Hang],
Puig, X.[Xavier],
Zhou, B.L.[Bo-Lei],
Fidler, S.[Sanja],
Torralba, A.B.[Antonio B.],
Open Vocabulary Scene Parsing,
ICCV17(2021-2029)
IEEE DOI
1802
document image processing, grammars, vocabulary, ADE20K dataset,
Open Vocabulary Scene Parsing, image pixel,
Vocabulary
BibRef
Jiang, S.,
Liang, S.,
Chen, C.,
Zhu, Y.,
Li, X.,
Class Agnostic Image Common Object Detection,
IP(28), No. 6, June 2019, pp. 2836-2846.
IEEE DOI
1905
feature extraction, image classification,
image matching, learning (artificial intelligence),
relation network
BibRef
Lei, T.,
Jia, X.,
Liu, T.,
Liu, S.,
Meng, H.,
Nandi, A.K.,
Adaptive Morphological Reconstruction for Seeded Image Segmentation,
IP(28), No. 11, November 2019, pp. 5510-5523.
IEEE DOI
1909
Image segmentation, Image reconstruction, Image edge detection,
Transforms, Convergence, Morphology, Computer science,
spectral segmentation
BibRef
Lee, M.C.H.,
Petersen, K.,
Pawlowski, N.,
Glocker, B.,
Schaap, M.,
TeTrIS: Template Transformer Networks for Image Segmentation With
Shape Priors,
MedImg(38), No. 11, November 2019, pp. 2596-2606.
IEEE DOI
1911
Shape, Neural networks, Image segmentation, Strain,
Deformable models, Training, Task analysis, Image segmentation,
image registration
BibRef
Sharma, A.[Ashu],
Ghosh, J.K.[Jayanta Kumar],
Kolay, S.[Saptarshi],
Reference data preparation for complex satellite image segmentation,
IET-IPR(14), No. 4, 27 March 2020, pp. 628-637.
DOI Link
2003
Training data for segmentation.
BibRef
Qiu, H.Q.[He-Qian],
Li, H.L.[Hong-Liang],
Wu, Q.B.[Qing-Bo],
Meng, F.M.[Fan-Man],
Xu, L.F.[Lin-Feng],
Ngan, K.N.[King Ngi],
Shi, H.C.[Heng-Can],
Hierarchical Context Features Embedding for Object Detection,
MultMed(22), No. 12, December 2020, pp. 3039-3050.
IEEE DOI
2011
Feature extraction, Object detection, Logic gates,
Image segmentation, Convolution, Semantics, Decoding,
gated encoder-decoder network
BibRef
Zhou, F.[Feng],
Hang, R.L.[Ren-Long],
Liu, Q.S.[Qing-Shan],
Class-Guided Feature Decoupling Network for Airborne Image
Segmentation,
GeoRS(59), No. 3, March 2021, pp. 2245-2255.
IEEE DOI
2103
Feature extraction, Image segmentation, Automobiles, Semantics,
Context modeling, Task analysis, Computer architecture,
semantic segmentation
BibRef
Chen, Z.M.[Zhao-Min],
Jin, X.[Xin],
Zhao, B.R.[Bo-Rui],
Zhang, X.Q.[Xiao-Qin],
Guo, Y.[Yanwen],
HCE: Hierarchical Context Embedding for Region-Based Object Detection,
IP(30), 2021, pp. 6917-6929.
IEEE DOI
2108
Detectors, Feature extraction, Proposals, Object detection, Head,
Training, Noise measurement, Object detection, context embedding,
region-based CNNs
BibRef
Chen, Z.M.[Zhao-Min],
Jin, X.[Xin],
Zhao, B.R.[Bo-Rui],
Wei, X.S.[Xiu-Shen],
Guo, Y.[Yanwen],
Hierarchical Context Embedding for Region-based Object Detection,
ECCV20(XXI:633-648).
Springer DOI
2011
BibRef
Tomar, D.[Devavrat],
Bozorgtabar, B.[Behzad],
Vray, M.L.G.[Manana Lortkipanidze Guillaume],
Rad, M.S.[Mohammad Saeed],
Thiran, J.P.[Jean-Philippe],
Self-Supervised Generative Style Transfer for One-Shot Medical Image
Segmentation,
WACV22(1737-1747)
IEEE DOI
2202
Training, Image segmentation, Computational modeling,
Magnetic resonance imaging, Transfer learning, Brain modeling,
Semi- and Un- supervised Learning
BibRef
Xu, Y.Z.[Yu-Zheng],
Wu, Y.[Yang],
Zuraimi, N.S.B.[Nur Sabrina Binti],
Nobuhara, S.[Shohei],
Nishino, K.[Ko],
Video Region Annotation with Sparse Bounding Boxes,
IJCV(131), No. 3, March 2023, pp. 717-731.
Springer DOI
2302
automatically generate region boundaries for all frames of a video
from sparsely annotated bounding boxes of target regions
BibRef
Qu, H.C.[Hai-Cheng],
Wang, X.N.[Xiao-Na],
Wang, Y.[Ying],
Chen, Y.[Yao],
Multi-branch residual image semantic segmentation combined with
inverse weight gated-control,
IVC(143), 2024, pp. 104932.
Elsevier DOI
2403
Image semantic segmentation, Deep multi-branch residuals,
Attention, Inverse weight gated-control, Contextual information
BibRef
Xing, P.[Peng],
Sun, Y.P.[Yan-Peng],
Zeng, D.[Dan],
Li, Z.C.[Ze-Chao],
Normal Image Guided Segmentation Framework for Unsupervised Anomaly
Detection,
CirSysVideo(34), No. 6, June 2024, pp. 4639-4652.
IEEE DOI
2406
Feature extraction, Image segmentation, Anomaly detection,
Training, Image reconstruction, Task analysis, feature guidance
BibRef
Wang, X.L.[Xin-Long],
Zhang, X.S.[Xiao-Song],
Cao, Y.[Yue],
Wang, W.[Wen],
Shen, C.H.[Chun-Hua],
Huang, T.J.[Tie-Jun],
SegGPT: Towards Segmenting Everything In Context,
ICCV23(1130-1140)
IEEE DOI
2401
BibRef
Okazawa, A.[Atsuro],
Interclass Prototype Relation for Few-Shot Segmentation,
ECCV22(XXIX:362-378).
Springer DOI
2211
BibRef
Yu, J.[Jinan],
Ma, L.Y.[Li-Yan],
Li, Z.L.[Zheng-Lin],
Peng, Y.[Yan],
Xie, S.R.[Shao-Rong],
Open-World Object Detection via Discriminative Class Prototype
Learning,
ICIP22(626-630)
IEEE DOI
2211
Training, Measurement, Image coding, Prototypes, Object detection,
Benchmark testing, Object recognition,
Embedding Space Compressor
BibRef
Saha, O.[Oindrila],
Cheng, Z.[Zezhou],
Maji, S.[Subhransu],
GANORCON: Are Generative Models Useful for Few-shot Segmentation?,
CVPR22(9981-9990)
IEEE DOI
2210
Training, Representation learning, Image segmentation, Shape,
Training data, Self-supervised learning, grouping and shape analysis
BibRef
Liu, S.[Sheng],
Liu, K.N.[Kang-Ning],
Zhu, W.C.[Wei-Cheng],
Shen, Y.Q.[Yi-Qiu],
Fernandez-Granda, C.[Carlos],
Adaptive Early-Learning Correction for Segmentation from Noisy
Annotations,
CVPR22(2596-2606)
IEEE DOI
2210
Code, Segmentation.
WWW Link. Training, Deep learning, Annotations, Shape, Semantics, Robustness,
Segmentation, grouping and shape analysis
BibRef
Workman, S.[Scott],
Rafique, M.U.[M. Usman],
Blanton, H.[Hunter],
Jacobs, N.[Nathan],
Revisiting Near/Remote Sensing with Geospatial Attention,
CVPR22(1768-1777)
IEEE DOI
2210
Overhead image segmentation with ground-level images.
Image segmentation, Social networking (online),
Computational modeling, Computer architecture, Vision + X
BibRef
Chen, C.S.[Chen-Shu],
Liu, T.[Tao],
Tan, W.M.[Wen-Ming],
Pu, S.L.[Shi-Liang],
Mixed-Dual-Head Meets Box Priors:
A Robust Framework for Semi-supervised Segmentation,
WACV22(2592-2602)
IEEE DOI
2202
Training, Image segmentation, Annotations,
Semantics, Interference, Segmentation, Semi- and Un- supervised Learning
BibRef
Chowdhury, S.[Sanjoy],
Dasgupta, S.[Subhrajyoti],
Das, S.[Sudip],
Bhattacharya, U.[Ujjwal],
Listen To the Pixels,
ICIP21(2568-2572)
IEEE DOI
2201
Sound source separation and visual object segmentation.
Location awareness, Concurrent computing, Visualization,
Source separation, Semantics, Object segmentation, partial occlusion
BibRef
Niloy, F.F.[Fahim Faisal],
Amin, M.A.[M. Ashraful],
Ali, A.A.[Amin Ahsan],
Rahman, A.K.M.M.[A.K.M. Mahbubur],
Attention Toward Neighbors:
A Context Aware Framework for High Resolution Image Segmentation,
ICIP21(2279-2283)
IEEE DOI
2201
Context-aware services, Image segmentation, Image resolution,
Fuses, Task analysis, High-resolution image segmentation,
Contextual information
BibRef
Azam, B.,
Mandal, R.,
Zhang, L.,
Verma, B.,
Class Probability-based Visual and Contextual Feature Integration for
Image Parsing,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Deep learning, Visualization, Image segmentation,
Computer architecture, Benchmark testing, Task analysis,
deep learning
BibRef
Wu, T.Y.[Tian-Yi],
Lu, Y.[Yu],
Zhu, Y.[Yu],
Zhang, C.[Chuang],
Wu, M.[Ming],
Ma, Z.Y.[Zhan-Yu],
Guo, G.D.[Guo-Dong],
GiNet: Graph Interaction Network for Scene Parsing,
ECCV20(XVII:34-51).
Springer DOI
2011
Incorperate the linguistic knowledge to promote context reasoning.
BibRef
Zhong, Y.J.[Yu-Jie],
Deng, Z.[Zelu],
Guo, S.[Sheng],
Scott, M.R.[Matthew R.],
Huang, W.L.[Wei-Lin],
Representation Sharing for Fast Object Detector Search and Beyond,
ECCV20(XIX:471-487).
Springer DOI
2011
BibRef
Mendel, R.[Robert],
de Souza, Jr., L.A.[Luis Antonio],
Rauber, D.[David],
Papa, J.P.[Joăo Paulo],
Palm, C.[Christoph],
Semi-supervised Segmentation Based on Error-correcting Supervision,
ECCV20(XXIX: 141-157).
Springer DOI
2010
BibRef
Yu, C.,
Wang, J.,
Gao, C.,
Yu, G.,
Shen, C.,
Sang, N.,
Context Prior for Scene Segmentation,
CVPR20(12413-12422)
IEEE DOI
2008
Aggregates, Semantics, Context modeling, Image segmentation,
Feature extraction, Task analysis, Encoding
BibRef
Ortiz, A.,
Robinson, C.,
Morris, D.,
Fuentes, O.,
Kiekintveld, C.,
Hassan, M.M.,
Jojic, N.,
Local Context Normalization: Revisiting Local Normalization,
CVPR20(11273-11282)
IEEE DOI
2008
Neural networks, Convergence, Image segmentation, Training
BibRef
McIntosh, B.[Bruce],
Duarte, K.[Kevin],
Rawat, Y.S.[Yogesh S.],
Shah, M.[Mubarak],
Visual-Textual Capsule Routing for Text-Based Video Segmentation,
CVPR20(9939-9948)
IEEE DOI
2008
Actor and action video segmentation from a sentence.
Routing, Visualization, Task analysis, Natural languages,
Feature extraction, Convolution, Merging
BibRef
Yang, K.,
Zhang, P.,
Qiao, P.,
Wang, Z.,
Dai, H.,
Shen, T.,
Li, D.,
Dou, Y.,
Rethinking Segmentation Guidance for Weakly Supervised Object
Detection,
VL3W20(4069-4073)
IEEE DOI
2008
Proposals, Image segmentation,
Optical character recognition software, Detectors, Semantics,
Object detection
BibRef
Yang, Z.H.[Zhen-Heng],
Mahajan, D.[Dhruv],
Ghadiyaram, D.[Deepti],
Nevatia, R.[Ram],
Ramanathan, V.[Vignesh],
Activity Driven Weakly Supervised Object Detection,
CVPR19(2912-2921).
IEEE DOI
2002
BibRef
Dmitriev, K.[Konstantin],
Kaufman, A.E.[Arie E.],
Learning Multi-Class Segmentations From Single-Class Datasets,
CVPR19(9493-9503).
IEEE DOI
2002
BibRef
Ding, H.[Henghui],
Jiang, X.D.[Xu-Dong],
Shuai, B.[Bing],
Liu, A.Q.[Ai Qun],
Wang, G.[Gang],
Semantic Correlation Promoted Shape-Variant Context for Segmentation,
CVPR19(8877-8886).
IEEE DOI
2002
BibRef
Kim, Y.[Yonghyun],
Kim, T.[Taewook],
Kang, B.N.[Bong-Nam],
Kim, J.[Jieun],
Kim, D.J.[Dai-Jin],
BAN: Focusing on Boundary Context for Object Detection,
ACCV18(VI:555-570).
Springer DOI
1906
BibRef
Zhao, Q.J.[Qi-Jie],
Wang, Y.T.[Yong-Tao],
Sheng, T.[Tao],
Tang, Z.[Zhi],
Comprehensive Feature Enhancement Module for Single-Shot Object
Detector,
ACCV18(V:325-340).
Springer DOI
1906
BibRef
Rao, C.[Cong],
Fan, Y.[Yi],
Su, K.L.[Kai-Le],
Latecki, L.J.[Longin Jan],
Common Object Discovery as Local Search for Maximum Weight Cliques in a
Global Object Similarity Graph,
DGCI19(219-233).
Springer DOI
1905
BibRef
Pino, O.[Omar],
Nascimento, E.[Erickson],
Campos, M.[Mario],
Prototypicality Effects in Global Semantic Description of Objects,
WACV19(1233-1242)
IEEE DOI
1904
convolutional neural nets, feature extraction,
image classification, learning (artificial intelligence),
Organizations
BibRef
Lu, H.F.[Hsueh-Fu],
Du, X.F.[Xiao-Fei],
Chang, P.L.[Ping-Lin],
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks,
ECCV18(VIII: 175-190).
Springer DOI
1810
BibRef
Singh, K.K.[Krishna Kumar],
Divvala, S.[Santosh],
Farhadi, A.[Ali],
Lee, Y.J.[Yong Jae],
DOCK: Detecting Objects by Transferring Common-Sense Knowledge,
ECCV18(XIII: 506-522).
Springer DOI
1810
BibRef
Liu, S.F.[Si-Fei],
Zhong, G.Y.[Guang-Yu],
de Mello, S.[Shalini],
Gu, J.W.[Jin-Wei],
Jampani, V.[Varun],
Yang, M.H.[Ming-Hsuan],
Kautz, J.[Jan],
Switchable Temporal Propagation Network,
ECCV18(VII: 89-104).
Springer DOI
1810
Apply to colorizing a video based on a few key-frames, generating an HDR
video, and propagating a segmentation mask from
the first frame in videos.
BibRef
Wang, Y.F.[Yu-Fei],
Lin, Z.[Zhe],
Shen, X.H.[Xiao-Hui],
Zhang, J.M.[Jian-Ming],
Cohen, S.[Scott],
Concept Mask: Large-Scale Segmentation from Semantic Concepts,
ECCV18(XII: 542-557).
Springer DOI
1810
BibRef
Holua, M.[Michael],
Sojka, E.[Eduard],
On the Stability of the K-Max Distance to the Position of Seeds,
ICIP18(261-265)
IEEE DOI
1809
Image segmentation, Image edge detection, Transforms,
Level measurement, Standards, Q measurement, Distance transform,
Image segmentation
BibRef
Jin, B.[Bin],
Ortiz Segovia, M.V.[Maria V.],
Süsstrunk, S.[Sabine],
Webly Supervised Semantic Segmentation,
CVPR17(1705-1714)
IEEE DOI
1711
Weakly supervised from web tags. Benchmark testing, Image segmentation,
Neural networks, Pipelines, Semantics, Training, Visualization
BibRef
Xu, Y.[Yang],
Li, J.[Jun],
Chen, J.B.[Jian-Bin],
Shen, G.T.[Guang-Tian],
Gao, Y.J.[Yang-Jian],
A novel approach for visual Saliency detection and segmentation based
on objectness and top-down attention,
ICIVC17(361-365)
IEEE DOI
1708
Algorithm design and analysis, Benchmark testing,
Computational modeling, Detectors, Image recognition,
Image segmentation, object segmentation, objectness,
saliency detection, visual attention
BibRef
Hu, R.H.[Rong-Hang],
Rohrbach, M.[Marcus],
Darrell, T.J.[Trevor J.],
Segmentation from Natural Language Expressions,
ECCV16(I: 108-124).
Springer DOI
1611
BibRef
Lee, C.K.,
Liu, T.L.,
Guided co-training for multi-view spectral clustering,
ICIP16(4042-4046)
IEEE DOI
1610
Algorithm design and analysis
BibRef
Acuńa, R.G.G.[Rafael Guillermo Gonzalez],
Tao, J.L.[Jun-Li],
Klette, R.[Reinhard],
Generalization of Otsu's binarization into recursive colour image
segmentation,
ICVNZ15(1-6)
IEEE DOI
1701
image colour analysis
BibRef
Acuńa, R.G.G.[Rafael Guillermo Gonzalez],
Tao, J.L.[Jun-Li],
Breen, D.[Daniel],
Breen, B.[Barbara],
Pointing, S.[Steve],
Gillman, L.[Len],
Klette, R.[Reinhard],
Robust Segmentation of Aerial Image Data Recorded for Landscape Ecology
Studies,
GPID15(61-72).
Springer DOI
1603
BibRef
Xiao, F.[Fanyi],
Lee, Y.J.[Yong Jae],
Discovering the Spatial Extent of Relative Attributes,
ICCV15(1458-1466)
IEEE DOI
1602
Computational modeling
BibRef
Song, Y.H.[Yi-Hua],
Gong, Z.X.[Zhao-Xuan],
Zhao, D.[Dazhe],
Feng, C.[Chaolu],
Li, C.M.[Chun-Ming],
Impact of the Number of Atlases in a Level Set Formulation of
Multi-atlas Segmentation,
ISVC15(I: 531-537).
Springer DOI
1601
BibRef
Shimoda, W.[Wataru],
Yanai, K.[Keiji],
CNN-Based Food Image Segmentation Without Pixel-Wise Annotation,
MADiMa15(449-457).
Springer DOI
1511
BibRef
Dehais, J.[Joachim],
Anthimopoulos, M.[Marios],
Mougiakakou, S.[Stavroula],
Dish Detection and Segmentation for Dietary Assessment on Smartphones,
MADiMa15(433-440).
Springer DOI
1511
BibRef
Pinheiro, P.O.[Pedro O.],
Lin, T.Y.[Tsung-Yi],
Collobert, R.[Ronan],
Dollár, P.[Piotr],
Learning to Refine Object Segments,
ECCV16(I: 75-91).
Springer DOI
1611
BibRef
Pinheiro, P.O.[Pedro O.],
Collobert, R.[Ronan],
From image-level to pixel-level labeling with Convolutional Networks,
CVPR15(1713-1721)
IEEE DOI
1510
BibRef
George, M.[Marian],
Image parsing with a wide range of classes and scene-level context,
CVPR15(3622-3630)
IEEE DOI
1510
BibRef
Lee, S.H.,
Jang, W.D.,
Park, B.K.,
Kim, C.S.,
RGB-D image segmentation based on multiple random walkers,
ICIP16(2549-2553)
IEEE DOI
1610
Clustering algorithms
BibRef
Lee, C.[Chulwoo],
Jang, W.D.[Won-Dong],
Sim, J.Y.[Jae-Young],
Kim, C.S.[Chang-Su],
Multiple random walkers and their application to image cosegmentation,
CVPR15(3837-3845)
IEEE DOI
1510
BibRef
Kasiri, K.[Keyvan],
Fieguth, P.W.[Paul W.],
Clausi, D.A.[David A.],
Cross modality label fusion in multi-atlas segmentation,
ICIP14(16-20)
IEEE DOI
1502
Accuracy
BibRef
Zhang, F.Q.[Fei-Qian],
Sun, Z.X.[Zheng-Xing],
Song, M.[Mofei],
Lang, X.F.[Xu-Feng],
Online 3D Shape Segmentation by Blended Learning,
MMMod15(I: 559-570).
Springer DOI
1501
3D shapes.
BibRef
Tang, K.[Kevin],
Joulin, A.[Armand],
Li, L.J.[Li-Jia],
Fei-Fei, L.[Li],
Co-localization in Real-World Images,
CVPR14(1464-1471)
IEEE DOI
1409
Co-localization; Object Detection of same class across images.
BibRef
Hayder, Z.[Zeeshan],
He, X.M.[Xu-Ming],
Salzmann, M.[Mathieu],
Structural Kernel Learning for Large Scale Multiclass Object
Co-detection,
ICCV15(2632-2640)
IEEE DOI
1602
BibRef
Earlier: A1, A3, A2:
Object Co-detection via Efficient Inference in a Fully-Connected CRF,
ECCV14(III: 330-345).
Springer DOI
1408
BibRef
Mairon, R.[Rotem],
Ben-Shahar, O.[Ohad],
A Closer Look at Context:
From Coxels to the Contextual Emergence of Object Saliency,
ECCV14(V: 708-724).
Springer DOI
1408
BibRef
Oramas Mogrovejo, J.A.[Jose Antonio],
de Raedt, L.[Luc],
Tuytelaars, T.[Tinne],
Towards cautious collective inference for object verification,
WACV14(269-276)
IEEE DOI
1406
Accuracy; Kernel
relationships between objects to aid detection.
BibRef
Anantharajah, K.[Kaneswaran],
Ge, Z.Y.[Zong-Yuan],
McCool, C.[Chris],
Denman, S.[Simon],
Fookes, C.[Clinton],
Corke, P.[Peter],
Tjondronegoro, D.[Dian],
Sridharan, S.[Sridha],
Local inter-session variability modelling for object classification,
WACV14(309-316)
IEEE DOI
1406
Adaptation models
BibRef
Misra, I.[Ishan],
Shrivastava, A.[Abhinav],
Hebert, M.[Martial],
Data-driven exemplar model selection,
WACV14(339-346)
IEEE DOI
1406
exemplars suitable for object detection.
Detectors
BibRef
Chen, X.[Xi],
Jain, A.[Arpit],
Davis, L.S.[Larry S.],
Object co-labeling in multiple images,
WACV14(721-728)
IEEE DOI
1406
jointly annotate multiple images of the same scene.
Buildings
BibRef
Song, Q.[Qi],
Montillo, A.[Albert],
Bhagalia, R.[Roshni],
Srikrishnan, V.,
Organ Localization Using Joint AP/LAT View Landmark Consensus Detection
and Hierarchical Active Appearance Models,
MCV13(138-147).
Springer DOI
1405
BibRef
Jiménez del Toro, Ó.A.[Óscar Alfonso],
Müller, H.[Henning],
Hierarchic Multi-atlas Based Segmentation for Anatomical Structures:
Evaluation in the VISCERAL Anatomy Benchmarks,
MCV14(189-200).
Springer DOI
1501
BibRef
Earlier:
Multi-structure Atlas-Based Segmentation Using Anatomical Regions of
Interest,
MCV13(217-221).
Springer DOI
1405
BibRef
Joyseeree, R.[Ranveer],
Jiménez del Toro, Ó.A.[Óscar Alfonso],
Müller, H.[Henning],
Using Probability Maps for Multi-organ Automatic Segmentation,
MCV13(222-228).
Springer DOI
1405
BibRef
Xie, Y.R.[Yu-Rui],
Huang, C.[Chao],
Song, T.C.[Tie-Cheng],
Ma, J.X.[Jin-Xiu],
Jing, J.T.[Jie-Tao],
Object co-detection via low-rank and sparse representation dictionary
learning,
VCIP13(1-6)
IEEE DOI
1402
image reconstruction
BibRef
Guo, Q.J.[Qiao-Jin],
Li, N.[Ning],
Yang, Y.B.[Yu-Bin],
Wu, G.S.[Gang-Shan],
Integrating image segmentation and annotation using supervised PLSA,
ICIP13(3800-3804)
IEEE DOI
1402
Image Annotation; Image Segmentation; PLSA
BibRef
Cheng, J.Z.[Jie-Zhi],
Chang, F.J.[Feng-Ju],
Hsu, K.J.[Kuang-Jui],
Lin, Y.Y.[Yen-Yu],
Knowledge Leverage from Contours to Bounding Boxes:
A Concise Approach to Annotation,
ACCV12(I:730-744).
Springer DOI
1304
Class based segmentation
BibRef
Jiang, Y.[Yu],
Liu, J.[Jing],
Li, Z.C.[Ze-Chao],
Li, P.[Peng],
Lu, H.Q.[Han-Qing],
Co-regularized PLSA for Multi-view Clustering,
ACCV12(II:202-213).
Springer DOI
1304
BibRef
Jiang, Y.[Yu],
Liu, J.[Jing],
Li, Z.C.[Ze-Chao],
Lu, H.Q.[Han-Qing],
Collaborative PLSA for multi-view clustering,
ICPR12(2997-3000).
WWW Link.
1302
Clustering in one view should agree with other view
BibRef
Ma, K.[Kai],
Ben-Arie, J.[Jezekiel],
Multi-view multi-class object detection via exemplar compounding,
ICPR12(3256-3259).
WWW Link.
1302
BibRef
Yonetani, R.[Ryo],
Kimura, A.[Akisato],
Sakano, H.[Hitoshi],
Fukuchi, K.[Ken],
Single Image Segmentation with Estimated Depth,
BMVC12(28).
DOI Link
1301
image plus depth
BibRef
Mukherjee, L.[Lopamudra],
Singh, V.[Vikas],
Xu, J.[Jia],
Collins, M.D.[Maxwell D.],
Analyzing the Subspace Structure of Related Images:
Concurrent Segmentation of Image Sets,
ECCV12(IV: 128-142).
Springer DOI
1210
BibRef
Langmann, B.[Benjamin],
Hartmann, K.[Klaus],
Loffeld, O.[Otmar],
A Modular Framework for 2D/3D and Multi-modal Segmentation with Joint
Super-Resolution,
CDC4CV12(II: 12-21).
Springer DOI
1210
BibRef
Stefanczyk, M.[Maciej],
Kasprzak, W.[Wlodzimierz],
Multimodal Segmentation of Dense Depth Maps and Associated Color
Information,
ICCVG12(626-632).
Springer DOI
1210
BibRef
Kasprzak, W.[Wlodzimierz],
Stefanczyk, M.[Maciej],
3D Semantic Map Computation Based on Depth Map and Video Image,
ICCVG12(441-448).
Springer DOI
1210
BibRef
Akter, N.[Nasreen],
Gondra, I.[Iker],
Attributed Relational Graph-Based Learning of Object Models for Object
Segmentation,
ICIAR15(90-99).
Springer DOI
1507
BibRef
Gondra, I.[Iker],
Alam, F.I.[Fahim Irfan],
Learning-Based Object Segmentation Using Regional Spatial Templates and
Visual Features,
ICCVG12(397-406).
Springer DOI
1210
BibRef
Cushen, G.A.[George A.],
Nixon, M.S.[Mark S.],
Real-time Semantic Clothing Segmentation,
ISVC12(I: 272-281).
Springer DOI
1209
BibRef
Collins, M.D.[Maxwell D.],
Xu, J.[Jia],
Grady, L.[Leo],
Singh, V.[Vikas],
Random walks based multi-image segmentation:
Quasiconvexity results and GPU-based solutions,
CVPR12(1656-1663).
IEEE DOI
1208
BibRef
Wang, H.Z.[Hong-Zhi],
Yushkevich, P.A.[Paul A.],
Spatial bias in multi-atlas based segmentation,
CVPR12(909-916).
IEEE DOI
1208
BibRef
Rivera, P.,
Gould, S.,
Simultaneous Multi-class Pixel Labeling over Coherent Image Sets,
DICTA11(99-106).
IEEE DOI
1205
BibRef
Jayawardena, S.,
Yang, D.[Di],
Hutter, M.,
3D Model Assisted Image Segmentation,
DICTA11(51-58).
IEEE DOI
1205
BibRef
Yang, Y.[Yi],
Newsam, S.[Shawn],
Estimating the spatial extents of geospatial objects using hierarchical
models,
WACV12(305-312).
IEEE DOI
1203
E.g. schools, golf courses usually marked only by a point. Automatically
segment the extent.
BibRef
Ibrahim, M.S.[Mostafa S.],
El-Saban, M.[Motaz],
Higher order potentials with superpixel neighbourhood (HSN) for
semantic image segmentation,
ICIP11(2881-2884).
IEEE DOI
1201
BibRef
Cao, Y.H.[Yi-Hui],
Yuan, Y.[Yuan],
Li, X.L.[Xue-Long],
Yan, P.K.[Ping-Kun],
Putting images on a manifold for atlas-based image segmentation,
ICIP11(289-292).
IEEE DOI
1201
Medical analysis. Select similar shape in database.
BibRef
Xu, W.T.[Wen-Tao],
Kanawong, R.[Ratchadaporn],
Duan, Y.[Ye],
Zhang, G.X.[Gui-Xu],
A new information fusion approach for image segmentation,
ICIP11(2873-2876).
IEEE DOI
1201
BibRef
Eslami, S.M.A.[Seyed Mohammadali Ali],
Williams, C.K.I.[Christopher K.I.],
Factored Shapes and Appearances for Parts-based Object Understanding,
BMVC11(xx-yy).
HTML Version.
1110
Some idea of the desired shape.
BibRef
Wegner, J.D.[Jan D.],
Rosenhahn, B.[Bodo],
Sörgel, U.[Uwe],
Segmentation and Classification of Objects with Implicit Scene Context,
WTFCV11(264-284).
Springer DOI
1210
BibRef
And:
Implicit Scene Context for Object Segmentation and Classification,
DAGM11(31-40).
Springer DOI
1109
BibRef
Fu, H.[Hao],
Qiu, G.P.[Guo-Ping],
Integrating Low-level and Semantic Features for Object Consistent
Segmentation,
ICIG11(39-44).
IEEE DOI
1109
BibRef
Lu, C.Y.[Can-Yi],
Feng, J.S.[Jia-Shi],
Lin, Z.C.[Zhou-Chen],
Yan, S.C.[Shui-Cheng],
Correlation Adaptive Subspace Segmentation by Trace Lasso,
ICCV13(1345-1352)
IEEE DOI
1403
BibRef
Liu, X.B.[Xiao-Bai],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Lin, L.[Liang],
Jin, H.[Hai],
Segment an image by looking into an image corpus,
CVPR11(2249-2256).
IEEE DOI
1106
BibRef
Martins, C.I.O.[Charles Iury Oliveira],
Cesar, R.M.[Roberto Marcondes],
Jorge, L.R.[Leonardo Ré],
Freitas, A.V.L.[André Victor Lucci],
Segmentation of Similar Images Using Graph Matching and Community
Detection,
GbRPR11(265-274).
Springer DOI
1105
Interactive segmentation of one, leads to automatic segmentation of rest in
dataset. Biological samples (butterfly wings).
BibRef
Cusano, C.[Claudio],
Region-Based Annotation of Digital Photographs,
CCIW11(47-59).
Springer DOI
1104
Oversegment, classify into 7 catetories, discard low confidence.
BibRef
Aue, A.[Alexander],
Lee, T.C.M.[Thomas C. M.],
Statistically consistent image segmentation,
ICIP10(2229-2232).
IEEE DOI
1009
BibRef
Yang, W.[Wen],
Dai, D.X.[Deng-Xin],
Triggs, B.[Bill],
Xia, G.S.[Gui-Song],
He, C.[Chu],
Fast semantic scene segmentation with conditional random field,
ICIP10(229-232).
IEEE DOI
1009
BibRef
Wu, S.L.[Shi-Lin],
Geng, J.J.[Jia-Jia],
Zhu, F.[Feng],
Theme-Based Multi-class Object Recognition and Segmentation,
ICPR10(3013-3016).
IEEE DOI
1008
assign a theme
BibRef
Walter, M.R.[Matthew R.],
Friedman, Y.[Yuli],
Antone, M.[Matthew],
Teller, S.[Seth],
Appearance-based object reacquisition for mobile manipulation,
CVforHRI10(1-8).
IEEE DOI
1006
Interactively give task information, use this for other viewpoints.
BibRef
Ghosh, S.[Soumya],
Pfeiffer, J.J.[Joseph J.],
Mulligan, J.[Jane],
A general framework for reconciling multiple weak segmentations of an
image,
WACV09(1-8).
IEEE DOI
0912
BibRef
Moore, A.P.[Alastair P.],
Prince, S.J.D.[Simon J.D.],
Warrell, J.[Jonathan],
'Lattice Cut': Constructing superpixels using layer constraints,
CVPR10(2117-2124).
IEEE DOI
1006
BibRef
Warrell, J.[Jonathan],
Prince, S.J.D.[Simon J.D.],
Moore, A.P.[Alastair P.],
Epitomized priors for multi-labeling problems,
CVPR09(2812-2819).
IEEE DOI
0906
Combine local and context for segmentation.
BibRef
Hochbaum, D.S.[Dorit S.],
An Efficient and Effective Tool for Image Segmentation, Total
Variations and Regularization,
SSVM11(338-349).
Springer DOI
1201
BibRef
Driesen, J.,
Scheunders, P.,
A Multicomponent Image Segmentation Framework,
ACIVS08(xx-yy).
Springer DOI
0810
BibRef
Xiang, B.[Bo],
Komodakis, N.[Nikos],
Paragios, N.[Nikos],
Pose Invariant Deformable Shape Priors Using L_1 Higher Order Sparse
Graphs,
ISVC13(I:194-205).
Springer DOI
1310
Knowledge based segmentation.
BibRef
Besbes, A.[Ahmed],
Komodakis, N.[Nikos],
Langs, G.[Georg],
Paragios, N.[Nikos],
Shape priors and discrete MRFs for knowledge-based segmentation,
CVPR09(1295-1302).
IEEE DOI
0906
BibRef
Essafi, S.[Salma],
Langs, G.[Georg],
Paragios, N.[Nikos],
Hierarchical 3D diffusion wavelet shape priors,
ICCV09(1717-1724).
IEEE DOI
0909
Knowledge for segmentation. Snakes?
BibRef
Essafi, S.[Salma],
Langs, G.[Georg],
Paragios, N.[Nikos],
Sparsity, redundancy and optimal image support towards knowledge-based
segmentation,
CVPR08(1-7).
IEEE DOI
0806
BibRef
Ahmad, J.E.[Jawad Elsayed],
Takakura, Y.[Yoshitate],
Improving Segmentation Maps using Polarization Imaging,
ICIP07(I: 281-284).
IEEE DOI
0709
BibRef
Wallhoff, F.[Frank],
Rub, M.[Martin],
Rigoll, G.[Gerhard],
Gobel, J.[Johann],
Diehl, H.[Hermann],
Improved Image Segmentation using Photonic Mixer Devices,
ICIP07(VI: 53-56).
IEEE DOI
0709
BibRef
Gallagher, C.[Claire],
Kokaram, A.[Anil],
Bayesian Example Based Segmentation using a Hybrid Energy Model,
ICIP07(II: 41-44).
IEEE DOI
0709
BibRef
Hong, X.[Xin],
McClean, S.[Sally],
Scotney, B.W.[Bryan W.],
Morrow, P.J.[Philip J.],
Model-Based Segmentation of Multimodal Images,
CAIP07(604-611).
Springer DOI
0708
BibRef
Vasconcelos, M.[Manuela],
Vasconcelos, N.M.[Nuno M.],
Carneiro, G.[Gustavo],
Weakly Supervised Top-down Image Segmentation,
CVPR06(I: 1001-1006).
IEEE DOI
0606
BibRef
Borenstein, E.[Eran],
Malik, J.[Jitendra],
Shape Guided Object Segmentation,
CVPR06(I: 969-976).
IEEE DOI
0606
BibRef
Johnson, M.A.,
Cipolla, R.,
Improved Image Annotation and Labelling through Multi-Label Boosting,
BMVC05(xx-yy).
HTML Version.
0509
Learning based on multi-labelled data.
BibRef
Clark, A.A.,
Thomas, B.T.,
Evolving Image Segmentations for the Analysis of Video Sequences,
CVPR01(II:290-295).
IEEE DOI
0110
Use initial segmentation to guide later ones.
BibRef
Perner, P.[Petra],
Controlling the Segmentation Parameters by Case-based Reasoning,
ICPR00(Vol III: 963-966).
IEEE DOI
0009
BibRef
Earlier:
Case based reasoning for image interpretation,
CAIP95(532-537).
Springer DOI
9509
BibRef
Hug, J.,
Brechbuhler, C.,
Szekely, G.,
Model-Based Initialisation for Segmentation,
ECCV00(II: 290-306).
Springer DOI
0003
BibRef
Charroux, B.,
Philipp, S.,
Cocquerez, J.P.,
Image Analysis:
Segmentation Operator Cooperation Led by the Interpretation,
ICIP96(III: 939-942).
IEEE DOI
BibRef
9600
Paulus, D.,
Object oriented image segmentation,
ICIPA92(482-485).
PS File.
BibRef
9200
Baker, D.C.,
Aggarwal, J.K., and
Hwang, V.S.,
Geometry Guided Incremental Segmentation,
CVWS87(237-239).
BibRef
8700
Price, K.E.,
Medioni, G.,
Segmentation Using Scene Models,
USC_ISG-102, October 1982.
BibRef
8210
USC Computer VisionCombine region and edge models.
BibRef
Medioni, G.G.[Gerard G.],
Segmentation of Images Into Regions Using Edge Information,
AAAI-82(42-45).
BibRef
8200
USC Computer Vision
BibRef
Sze, T.W., and
Yang, Y.H.,
Goal Directed Segmentation,
PRIP82(504-511).
How to use some of the a priori information in segmentation (such
as shape). Compares this to gradient based boundary extraction
followed by labeling of inside or outside (unclean). Also to split
and merge. Method generates initial regions (either method)
polygonal approximation, shape matching using syntactic methods and
merge regions which improve the shape match.
BibRef
8200
Aggarwal, R.K.,
Adaptive Image Segmentation Using Prototype Similarity,
PRIP78(354-359)
BibRef
7800
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Instance Segmentation .