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.
WWW Version.
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.,
Model-Based Segmentation of Grey-Tone Images,
IVC(2), No. 3, August 1984, pp. 123-133.
WWW Version.
BibRef
8408
Zamperoni, P.,
Feature Extraction by Rank-Order Filtering for Image Segmentation,
PRAI(2), 1988, pp. 301-319.
BibRef
8800
Zamperoni, P.,
Some Adaptive Rank Order Filters for Image Enhancement,
PRL(11), 1990, pp. 81-86.
BibRef
9000
Zamperoni, P.,
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.
WWW Version.
BibRef
8511
Matsuyama, T.,
Expert Systems for Image Processing: Knowledge-Based
Composition of Image Analysis Processes,
CVGIP(48), No. 1, October 1989, pp. 22-49.
WWW Version.
BibRef
8910
Earlier:
ICPR88(I: 125-133).
IEEE DOI Link
8811
Rule Based Systems.
System: SIGMA. This builds on the general systems such as SIGMA and is directed
toward segmentation.
BibRef
Nagao, M.,
Matsuyama, T., and
Ikeda, Y.,
Region Extraction and Shape Analysis in Aerial Photographs,
CGIP(10), No. 3, July 1979, pp. 195-223.
WWW Version.
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.
WWW Version.
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 Abstract. 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
Harwood, D.A.,
Chang, S., and
Davis, L.S.,
Interpreting Aerial Photographs by Segmentation and Search,
DARPA87(507-520).
(
See also Sigma Image Understanding System, The. )
Find segments (homogeneous regions), then find instances which
satisfy definitions of object types, then search for support, then
improve instances, then iterate with new estimates of parameters.
See also Fua and Leclerc Guided Segmentation Papers.
BibRef
8700
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.
WWW Version.
BibRef
8705
Sunil Kumar, K.,
Desai, U.B.,
Joint segmentation and image interpretation,
PR(32), No. 4, April 1999, pp. 577-589.
WWW Version.
BibRef
9904
Earlier:
ICIP96(I: 853-856).
IEEE DOI Link
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 Version. Combine segmentation and interpretation modules.
BibRef
0107
Figov, Z.,
Tal, Y., and
Koppel, M.[Moshe],
Detecting and Removing Shadows,
ICCGI04(xx-yy).
PDF Version.
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 Link
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 Link
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 Link
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 Link
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 Link
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 Version.
1101
BibRef
Earlier:
ICCV07(1-8).
IEEE DOI Link
0710
BibRef
And:
Image Segmentation with Context,
SCIA07(283-292).
Springer DOI Link
0706
BibRef
Mu, Y.D.[Ya-Dong],
Zhou, B.F.[Bing-Feng],
Co-segmentation of Image Pairs with Quadratic Global Constraint in MRFs,
ACCV07(II: 837-846).
Springer DOI Link
0711
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 Link
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 Link
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 Link
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.,
Image Segmentation using Salient Points-Based Object Templates,
ICIP06(765-768).
IEEE DOI Link
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 Link
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 Link
0909
BibRef
Crevier, D.[Daniel],
Image segmentation algorithm development using ground truth image data
sets,
CVIU(112), No. 2, November 2008, pp. 143-159.
WWW Version.
0811
BibRef
Earlier:
Extracting Salient Objects from Operator-Framed Images,
CRV07(36-43).
IEEE DOI Link
0705
Computer vision; 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 Link
0811
BibRef
Earlier:
Learning to Segment,
ECCV04(Vol III: 315-328).
WWW Version.
0405
BibRef
Earlier:
Class-Specific, Top-Down Segmentation,
ECCV02(II: 109 ff.).
HTML Version.
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 Link
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 Link
0903
BibRef
Earlier:
Learning object segmentation from video data,
MIT AIM-2003-022, September 8, 2003.
WWW Version.
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 Link
WWW Version.
0903
BibRef
Earlier:
Bottom-Up and Top-Down Object Matching Using Asynchronous Agents and a
Contrario Principles,
CVS08(xx-yy).
Springer DOI Link
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 Link
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 Link
1011
Segmentation given a training set. Pairwise registration between
image and label maps.
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 Link
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 Link
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 Link
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
Kim, G.H.[Gun-Hee],
Xing, E.P.[Eric P.],
Fei-Fei, L.[Li],
Kanade, T.[Takeo],
Distributed cosegmentation via submodular optimization on anisotropic
diffusion,
ICCV11(169-176).
IEEE DOI Link
1201
Saliency greater if region in many images. Segment common regions
in multiple image.
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 Link
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 Link
1201
Medical analysis. Select similar shape in database.
BibRef
Xu, W.[Wentao],
Kanawong, R.[Ratchadaporn],
Duan, Y.[Ye],
Zhang, G.X.[Gui-Xu],
A new information fusion approach for image segmentation,
ICIP11(2873-2876).
IEEE DOI Link
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],
Soergel, U.[Uwe],
Implicit Scene Context for Object Segmentation and Classification,
DAGM11(31-40).
Springer DOI Link
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 Link
1109
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 Link
1106
BibRef
Chang, K.Y.[Kai-Yueh],
Liu, T.L.[Tyng-Luh],
Lai, S.H.[Shang-Hong],
From co-saliency to co-segmentation:
An efficient and fully unsupervised energy minimization model,
CVPR11(2129-2136).
IEEE DOI Link
1106
BibRef
Donoser, M.[Michael],
Urschler, M.[Martin],
Riemenschneider, H.[Hayko],
Bischof, H.[Horst],
Highly Consistent Sequential Segmentation,
SCIA11(48-58).
Springer DOI Link
1105
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 Link
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 Link
1104
Oversegment, classify into 7 catetories, discard low confidence.
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 Link
1011
Shape, texture and context. Segment and label the regions.
BibRef
Chu, W.S.[Wen-Sheng],
Chen, C.P.[Chia-Ping],
Chen, C.S.[Chu-Song],
MOMI-Cosegmentation: Simultaneous Segmentation of Multiple Objects
among Multiple Images,
ACCV10(I: 355-368).
Springer DOI Link
1011
BibRef
Aue, A.[Alexander],
Lee, T.C.M.[Thomas C. M.],
Statistically consistent image segmentation,
ICIP10(2229-2232).
IEEE DOI Link
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 Link
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 Link
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 Link
1006
Interactively give task information, use this for other viewpoints.
BibRef
Nwogu, I.[Ifeoma],
Govindaraju, V.[Venu],
Brown, C.[Chris],
Syntactic image parsing using ontology and semantic descriptions,
POCV10(41-48).
IEEE DOI Link
1006
Semantic descriptions, real-world spatial relationships, colors, textures,
shape.
BibRef
Vezhnevets, A.[Alexander],
Ferrari, V.[Vittorio],
Buhmann, J.M.[Joachim M.],
Weakly supervised semantic segmentation with a multi-image model,
ICCV11(643-650).
IEEE DOI Link
1201
BibRef
Vezhnevets, A.[Alexander],
Buhmann, J.M.[Joachim M.],
Towards weakly supervised semantic segmentation by means of multiple
instance and multitask learning,
CVPR10(3249-3256).
IEEE DOI Link
1006
See also Agnostic Domain Adaptation.
BibRef
Lee, Y.J.[Yong Jae],
Kim, J.[Jaechul],
Grauman, K.[Kristen],
Key-segments for video object segmentation,
ICCV11(1995-2002).
IEEE DOI Link
1201
Find possible objects, then find ones with persistent appearance
and motion.
See also Foreground Focus: Unsupervised Learning from Partially Matching Images.
BibRef
Kim, J.[Jaechul],
Grauman, K.[Kristen],
Boundary preserving dense local regions,
CVPR11(1553-1560).
IEEE DOI Link
1106
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 Link
1006
BibRef
Vijayanarasimhan, S.[Sudheendra],
Grauman, K.[Kristen],
Top-down pairwise potentials for piecing together multi-class
segmentation puzzles,
POCV10(25-32).
IEEE DOI Link
1006
BibRef
Joulin, A.[Armand],
Bach, F.[Francis],
Ponce, J.[Jean],
Discriminative clustering for image co-segmentation,
CVPR10(1943-1950).
IEEE DOI Link
1006
simultaneously divide multiple images. Combine bottom up segmentation with
object recognition tools.
BibRef
Zheng, W.S.[Wei-Shi],
Gong, S.G.[Shao-Gang],
Xiang, T.[Tao],
Unsupervised Selective Transfer Learning for Object Recognition,
ACCV10(II: 527-541).
Springer DOI Link
1011
BibRef
Zheng, W.S.[Wei-Shi],
Gong, S.G.[Shao-Gang],
Xiang, T.[Tao],
Quantifying contextual information for object detection,
ICCV09(932-939).
IEEE DOI Link
0909
Context model without prior segmentation of context annotation.
BibRef
Micusik, B.[Branislav],
Kosecka, J.[Jana],
Semantic segmentation of street scenes by superpixel co-occurrence and
3D geometry,
ObjectEvent09(625-632).
IEEE DOI Link
0910
identify as one of a few common object/background classes.
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 Link
0912
BibRef
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Simultaneous segmentation of the same features in multiple images.
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Fua and Leclerc Guided Segmentation Papers .