4.8.4 Grouping, Figure-Ground, Background, Foreground

Herault, L., and Horaud, R.,
Figure-Ground Discrimination: A Combinatorial Approach,
PAMI(15), No. 9, September 1993, pp. 899-914.
IEEE DOI BibRef 9309
Earlier:
Figure-Ground Discrimination by Mean Field Annealing,
ECCV92(58-66).
Springer DOI Finding lines and curves in noisy edge images. BibRef

Spann, M.[Michael],
Figure/Ground Separation Using Stochastic Pyramid Relinking,
PR(24), No. 10, 1991, pp. 993-1002.
Elsevier DOI BibRef 9100

Everson, R., Knight, B.W., Sirovich, L.,
Separating Spatially Distributed Response to Stimulation from Background I: Optical Imaging,
BioCyber(77), No. 6, December 1997, pp. 407-417. 9801
BibRef

Amir, A.[Arnon], Lindenbaum, M.[Michael],
Ground from Figure Discrimination,
CVIU(76), No. 1, October 1999, pp. 7-18. BibRef 9910
Earlier: CVPR98(521-527).
DOI Link
See also Comments on ground from figure discrimination. BibRef

Zhang, J., Gao, J., Liu, J.,
Figure-Ground Separation by a Dynamical System,
IP(8), No. 1, January 1999, pp. 115-122.
IEEE DOI BibRef 9901

Zhang, J.[Jun], Lin, J.H.[Jian-Hua],
Figure-ground separation by a neural dynamical system,
ICIP95(II: 615-618).
IEEE DOI 9510
BibRef

Beyerer, J., Leon, F.P.,
Adaptive Separation of Random Lines and Background,
OptEng(37), No. 10, October 1998, pp. 2733-2741. 9810
BibRef

Caselles, V.[Vicent], Coll, B.[Bartomeu], Morel, J.M.[Jean-Michel],
Topographic Maps and Local Contrast Changes in Natural Images,
IJCV(33), No. 1, September 1999, pp. 5-27.
DOI Link Topographic map: contrast invariant representation of the image. Occlusion/Transparency analysis of the image formation and analysis. BibRef 9909

Coll, B.[Bartomeu], Froment, J.[Jacques],
Topographic Maps of Color Images,
ICPR00(Vol III: 609-612).
IEEE DOI 0009
Way to represent an image -- segmentation. BibRef

Carvalho, B.M.[Bruno M.], Gau, C.J.[C. Jose], Herman, G.T.[Gabor T.], Kong, T.Y.[T. Yung],
Algorithms for Fuzzy Segmentation,
PAA(2), No. 1, 1999, pp. 73-81. BibRef 9900

Herman, G.T.[Gabor T.], Carvalho, B.M.[Bruno M.],
Multiseeded Segmentation Using Fuzzy Connectedness,
PAMI(23), No. 5, May 2001, pp. 460-474.
IEEE DOI 0105
Segment an object from the background with noise. BibRef

Kellner, C.R.[Charles R.],
Method and apparatus for unencumbered capture of an object,
US_Patent6,101,289, Aug 8, 2000
WWW Link. controlled environment to do background BibRef 0008

Naoi, S.[Satoshi], Egawa, H.[Hiroichi], Shiohara, M.[Morito],
Image processing apparatus,
US_Patent6,141,435, Oct 31, 2000
WWW Link. BibRef 0010
And: US_Patent6,430,303, Aug 6, 2002
WWW Link. BibRef

Henderson, T.R.[Todd R.], Spaulding, K.E.[Kevin E.], Couwenhoven, D.W.[Douglas W.],
Method for segmenting a digital image into a foreground region and a key color region,
US_Patent6,011,595, Jan 4, 2000
WWW Link. BibRef 0001

Chen, T.H.[Tsu-Han], Swain, C.T.[Cassandra Turner],
Method and apparatus for segmenting images prior to coding,
US_Patent6,301,385, Oct 9, 2001
WWW Link. BibRef 0110

Funayama, R.J.[Ryu-Ji], Konya, M.[Minehiro], Takezawa, H.[Hajime],
Image processing device,
US_Patent6,332,038, Dec 18, 2001
WWW Link. particular objects BibRef 0112

Vernon, D.[David],
Fourier Vision: Segmentation and Velocity Measurement Using the Fourier Transform,
KluwerBoston, June 2001. ISBN 0-7923-7413-4.
WWW Link. BibRef 0106

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
An Expectation-Maximisation Framework for Segmentation and Grouping,
IVC(20), No. 9-10, August 2002, pp. 725-738.
Elsevier DOI 0208
BibRef
Earlier: PercOrg01(xx-yy). 0106
Motion Segmentation. BibRef
Earlier:
A Maximum Likelihood Framework for Grouping and Segmentation,
EMMCVPR01(251-267).
Springer DOI 0205
BibRef
Earlier:
Maximum likelihood motion segmentation using eigen-decomposition,
CIAP01(63-68).
IEEE DOI 0210
BibRef
And:
An EM-like Algorithm for Motion Segmentation via Eigendecomposition,
BMVC01(Poster Session 1).
HTML Version. University of York 0110

See also graph-spectral approach to surface segmentation, A. BibRef

Robles-Kelly, A.[Antonio], Bors, A.G., Hancock, E.R.[Edwin R.],
Hierarchical Iterative Eigen Decomposition for Motion Segmentation,
ICIP01(II: 363-366).
IEEE DOI 0108
BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
A Maximum Likelihood Framework for Iterative Eigendecomposition,
ICCV01(I: 654-661).
IEEE DOI 0106
BibRef
And:
Grouping Line-segments using Eigenclustering,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
A probabilistic spectral framework for grouping and segmentation,
PR(37), No. 7, July 2004, pp. 1387-1405.
Elsevier DOI 0405
BibRef
Earlier:
An Expectation-Maximisation Framework for Perceptual Grouping,
VF01(594 ff.).
Springer DOI 0209
BibRef
And:
Perceptual Grouping using Eigendecomposition and the EM Algorithm,
SCIA01(O-Th1). 0206
Pairwise clustering and perceptual grouping. Model expressed in terms of two sets of parameters: cluster memberships which represent the affinity of objects to clusters and a matrix of link weights for pairs of tokens. BibRef

Rosin, P.L.[Paul L.],
Comments on 'ground from figure discrimination',
PRL(24), No. 15, November 2003, pp. 2761-2766.
Elsevier DOI 0308

See also Ground from Figure Discrimination. BibRef

Sakamoto, S.[Shizuo],
Method and device of object detectable and background removal, and storage media for storing program thereof,
US_Patent6,603,880, Aug 5, 2003
WWW Link. BibRef 0308

Gordon, G.G.[Gaile G.], Harville, M.[Michael], Woodfill, J.I.[John I.], Darrell, T.J.[Trevor J.],
Background estimation and segmentation based on range and color,
US_Patent6,661,918, Dec 9, 2003
WWW Link. BibRef 0312

Gordon, G.G.[Gaile G.], Darrell, T.J.[Trevor J.], Harville, M.[Michael], Woodfill, J.I.[John I.],
Background Estimation and Removal Based on Range and Color,
CVPR99(II: 459-464).
IEEE DOI Use both range and color, not just one. BibRef 9900

Pavlidis, G.P.[George P.], Chamzas, C.[Christodoulos],
Compressing the background layer in compound images, using JPEG and data filling,
SP:IC(20), No. 5, June 2005, pp. 487-502.
Elsevier DOI 0506
BibRef

Pavlidis, G.P.[George P.], Tsekeridou, S., Chamzas, C.[Christodoulos],
JPEG-matched data filling of sparse images,
ICIP04(I: 493-496).
IEEE DOI 0505
BibRef

Aguiar, P.M.Q., Moura, J.M.F.,
Figure-ground segmentation from occlusion,
IP(14), No. 8, August 2005, pp. 1109-1124.
IEEE DOI 0508
BibRef

Chalmond, B., Francesconi, B., Herbin, S.,
Using Hidden Scale for Salient Object Detection,
IP(15), No. 9, August 2006, pp. 2644-2656.
IEEE DOI 0608
Scale and contrast interaction. First, spatial and contrast. BibRef

Zhang, H.[Hong], Carls, G.[Garry], Barnhill, S.D.[Stephen D.],
Computer-aided image analysis,
US_Patent6,996,549, Feb 7, 2006
WWW Link. BibRef 0602
And: US_Patent7,383,237, Jun 3, 2008
WWW Link. BibRef

Burgess, A.E.[Arthur E.], Judy, P.F.[Philip F.],
Signal detection in power-law noise: effect of spectrum exponents,
JOSA-A(24), No. 12, December 2007, pp. B52-B60.
WWW Link. 0801
Analyze backgrounds. BibRef

Levin, A.[Anat], Rav-Acha, A.[Alex], Lischinski, D.[Dani],
Spectral Matting,
PAMI(30), No. 10, October 2008, pp. 1699-1712.
IEEE DOI 0810
BibRef
Earlier: CVPR07(1-8).
IEEE DOI 0706
Award, CVPR, HM. Background/foreground. BibRef

Rhemann, C.[Christoph], Rother, C.[Carsten], Kohli, P.[Pushmeet], Gelautz, M.[Margrit],
A spatially varying PSF-based prior for alpha matting,
CVPR10(2149-2156).
IEEE DOI 1006
BibRef

Rhemann, C.[Christoph], Rother, C.[Carsten], Wang, J.[Jue], Gelautz, M.[Margrit], Kohli, P.[Pushmeet], Rott, P.[Pamela],
A perceptually motivated online benchmark for image matting,
CVPR09(1826-1833).
IEEE DOI 0906

See also Alpha Matting Evaluation Website.
See also stereo approach that handles the matting problem via image warping, A. BibRef

Rhemann, C.[Christoph], Rother, C.[Carsten], Rav-Acha, A.[Alex], Sharp, T.[Toby],
High resolution matting via interactive trimap segmentation,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Zhang, L.[Li],
In Situ Image Segmentation Using the Convexity of Illumination Distribution of the Light Sources,
PAMI(30), No. 10, October 2008, pp. 1786-1799.
IEEE DOI 0810
Use light source analysis to separate background pixels. BibRef

Fukuda, H.[Hiroshi], Ishihara, A.[Atsuhiko], Sakamoto, K.[Koichi], Tsubaki, H.[Hisayoshi], Watanabe, M.[Mikio],
Image capturing apparatus, main subject position determination method, and computer-readable medium storing program,
US_Patent7,339,606, Mar 4, 2008
WWW Link. BibRef 0803

Jimenez-Sanchez, A.R., Mendiola-Santibanez, J.D., Terol-Villalobos, I.R., Herrera-Ruiz, G., Vargas-Vazquez, D., Garcia-Escalante, J.J., Lara-Guevara, A.,
Morphological Background Detection and Enhancement of Images With Poor Lighting,
IP(18), No. 3, March 2009, pp. 613-623.
IEEE DOI 0903
BibRef

Shen, H.Y.[Hui-Ying], Coughlan, J.[James], Ivanchenko, V.[Volodymyr],
Figure-ground segmentation using factor graphs,
IVC(27), No. 7, 4 June 2009, pp. 854-863.
Elsevier DOI 0904
Figure-ground segmentation, Belief propagation, Factor graphs, Text detection Graphy models. BibRef

Dickinson, P.[Patrick], Hunter, A.[Andrew], Appiah, K.[Kofi],
A spatially distributed model for foreground segmentation,
IVC(27), No. 9, 3 August 2009, pp. 1326-1335.
Elsevier DOI 0906
Foreground segmentation, Background model, Spatial coherence, Mixture of Gaussians BibRef

Sun, W.[Wei], Spackman, S.P.[Stephen P.],
Multi-object segmentation by stereo mismatch,
MVA(20), No. 6, October 2009, pp. xx-yy.
Springer DOI 0910
No need to do full stereo reconstruction for foreground object extration. BibRef

Fujimoto, K.[Ken'ichi], Musashi, M.[Mio], Yoshinaga, T.[Tetsuya],
Reduced model of discrete-time dynamic image segmentation system and its bifurcation analysis,
IJIST(19), No. 4, December 2009, pp. 283-289.
DOI Link 0912
Neural network model for segmentation. BibRef

Ghosh, K., Pal, S.K.,
Some Insights Into Brightness Perception of Images in the Light of a New Computational Model of Figure-Ground Segregation,
SMC-A(40), No. 4, July 2010, pp. 758-766.
IEEE DOI 1007
Human Vision. BibRef

Dong, Y., de Souza, G.N.,
Adaptive learning of multi-subspace for foreground detection under illumination changes,
CVIU(115), No. 1, January 2011, pp. 31-49.
Elsevier DOI 1011
Multiple eigensubspace, Local PCA, Incremental learning, Illumination invariance BibRef

Fuentes Pineda, G.[Gibran], Koga, H.[Hisashi], Watanabe, T.[Toshinori],
Scalable Object Discovery: A Hash-Based Approach to Clustering Co-occurring Visual Words,
IEICE(E94-D), No. 10, October 2011, pp. 2024-2035.
WWW Link. 1110
BibRef
Earlier:
Object Discovery by Clustering Correlated Visual Word Sets,
ICPR10(750-753).
IEEE DOI 1008
BibRef
Earlier:
Unsupervised Object Discovery from Images by Mining Local Features Using Hashing,
CIARP09(978-985).
Springer DOI 0911
Find groups of closely located features. BibRef

Cheng, C.[Chang], Koschan, A.F.[Andreas F.], Chen, C.H., Page, D.L.[David L.], Abidi, M.A.[Mongi A.],
Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization,
IP(21), No. 3, March 2012, pp. 1007-1019.
IEEE DOI 1203
BibRef
Earlier: A1, A2, A4, A5, only:
Scene image segmentation based on Perceptual Organization,
ICIP09(1801-1804).
IEEE DOI 0911
BibRef

Carreira, J.[João], Li, F.X.[Fu-Xin], Sminchisescu, C.[Cristian],
Object Recognition by Sequential Figure-Ground Ranking,
IJCV(98), No. 3, July 2012, pp. 243-262.
WWW Link. 1202
BibRef
Earlier: A2, A1, A3:
Object recognition as ranking holistic figure-ground hypotheses,
CVPR10(1712-1719).
IEEE DOI 1006
BibRef

Chen, T.T.[Tian-Tang], Li, H.L.[Hong-Liang],
Segmenting focused objects based on the Amplitude Decomposition Model,
PRL(33), No. 12, 1 September 2012, pp. 1536-1542.
Elsevier DOI 1208
Amplitude Decomposition Model, Detection, Segmentation, Focused objects, Low depth-of-field BibRef

Shen, J., Yang, W., Lu, Z., Liao, Q.,
Information integration for accurate foreground segmentation in complex scenes,
IET-IPR(6), No. 5, 2012, pp. 596-605.
DOI Link 1210
BibRef

Punithakumar, K.[Kumaradevan], Yuan, J.[Jing], Ben Ayed, I.[Ismail], Li, S.[Shuo], Boykov, Y.Y.[Yuri Y.],
A Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation,
SIIMS(5), No. 4, 2012, pp. 1333-1354.
DOI Link 1211
BibRef

Jian, M., Lam, K., Dong, J., Shen, L.,
Visual-Patch-Attention-Aware Saliency Detection,
Cyber(45), No. 8, August 2015, pp. 1575-1586.
IEEE DOI 1506
Computational modeling BibRef

Shi, Y.J.[Yan-Jiao], Yi, Y.G.[Yu-Gen], Yan, H.X.[He-Xin], Dai, J.Y.[Jiang-Yan], Zhang, M.[Ming], Kong, J.[Jun],
Region contrast and supervised locality-preserving projection-based saliency detection,
VC(31), No. 9, September 2015, pp. 1191-1205.
WWW Link. 1508
BibRef

Kee, Y.[Youngwook], Lee, Y.[Yegang], Souiai, M.[Mohamed], Cremers, D.[Daniel], Kim, J.[Junmo],
Sequential Convex Programming for Computing Information-Theoretic Minimal Partitions: Nonconvex Nonsmooth Optimization,
SIIMS(10), No. 4, 2017, pp. 1845-1877.
DOI Link 1801
BibRef
Earlier: A1, A3, A4, A5, Only:
Sequential Convex Relaxation for Mutual Information-Based Unsupervised Figure-Ground Segmentation,
CVPR14(4082-4089)
IEEE DOI 1409
BibRef

Huang, X.M.[Xiao-Ming], Zhang, Y.J.[Yu-Jin],
Water flow driven salient object detection at 180 fps,
PR(76), No. 1, 2018, pp. 95-107.
Elsevier DOI 1801
Minimum Barrier Distance for Salient object detection. BibRef

Ortiz-Jaramillo, B., Kumcu, A., Platisa, L., Philips, W.,
Content-aware contrast ratio measure for images,
SP:IC(62), 2018, pp. 51-63.
Elsevier DOI 1802
Contrast of foreground object and background. Image content analysis, Image contrast ratio, Image fidelity assessment, Local contrast ratio BibRef

Zhang, Y.Y.[Ying Ying], Zhang, S.[Shuo], Zhang, P.[Ping], Zhang, X.G.[Xin-Gang],
Saliency detection via background and foreground null space learning,
SP:IC(70), 2019, pp. 271-281.
Elsevier DOI 1812
Saliency detection, Null space learning, Saliency optimization BibRef

Pan, Y.S.[Yong-Sheng], Xia, Y.[Yong], Shen, D.G.[Ding-Gang],
Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification,
IP(28), No. 10, October 2019, pp. 4716-4729.
IEEE DOI 1909
Detect foreground then encode it. feature extraction, image classification, image representation, neural nets, vectors, convolutional neural networks BibRef

Peng, T.[Tong], He, K.[Kun], Su, Y.[Yao], Hui, Z.W.[Zi-Wei],
Visual perception and local features for foreground-background segmentation,
IET-IPR(16), No. 6, 2022, pp. 1613-1625.
DOI Link 2204
BibRef

Markowitz, S.[Spencer], Snyder, C.[Corey], Eldar, Y.C.[Yonina C.], Do, M.N.[Minh N.],
Multimodal Unrolled Robust PCA for Background Foreground Separation,
IP(31), 2022, pp. 3553-3564.
IEEE DOI 2205
Radar, Cameras, Deep learning, Sensors, Real-time systems, Radar clutter, Principal component analysis, Radar, ISTA BibRef

Dundar, A.[Aysegul], Shih, K.J.[Kevin J.], Garg, A.[Animesh], Pottorf, R.[Robert], Tao, A.[Anrew], Catanzaro, B.[Bryan],
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos,
PAMI(44), No. 7, July 2022, pp. 3883-3894.
IEEE DOI 2206
Learning semantic keypoint-like representations without the use of expensive input keypoint annotations. Image reconstruction, Task analysis, Videos, Pipelines, Training, Image color analysis, Decoding, Unsupervised landmarks, keypoints, video prediction BibRef

Zhang, S.[Shiao], Chen, Y.L.[Yi-Lei], An, P.[Ping], Huang, X.P.[Xin-Peng], Yang, C.[Chao],
Light field occlusion removal network via foreground location and background recovery,
SP:IC(109), 2022, pp. 116853.
Elsevier DOI 2210
Light field image, Occlusion removal, Foreground location, Background recovery, Deep learning BibRef

Yang, Z.G.[Zhi-Gang], Shen, Y.H.[Ya-Hui], Hou, L.[Lin], Zhang, W.E.[Wei Emma], Chen, T.[Tao],
S^3Seg: A Three-Stage Unsupervised Foreground and Background Segmentation Network,
SPLetters(31), 2024, pp. 1484-1488.
IEEE DOI 2406
Image segmentation, Generators, Semantics, Image synthesis, Training, Task analysis, Foreground and background segmentation, GANs, radial loss BibRef

Kajo, I.[Ibrahim], Ruichek, Y.[Yassine], Kamel, N.[Nidal],
History Based Incremental Singular Value Decomposition for Background Initialization and Foreground Segmentation,
CIARP23(I:63-75).
Springer DOI 2312
BibRef

Yang, Y.[Yu], Bilen, H.[Hakan], Zou, Q.[Qiran], Cheung, W.Y.[Wing Yin], Ji, X.Y.[Xiang-Yang],
Learning Foreground-Background Segmentation from Improved Layered GANs,
WACV22(366-375)
IEEE DOI 2202
Training, Deep learning, Image segmentation, Generative adversarial networks, Noise measurement, Grouping and Shape BibRef

Forte, M.[Marco],
Approximate Fast Foreground Colour Estimation,
ICIP21(1909-1913)
IEEE DOI 2201
Runtime, Image color analysis, Estimation, Robustness, Alpha Matting, Foreground Estimation, Compositing, Despill BibRef

Sultana, M.[Maryam], Mahmood, A.[Arif], Bouwmans, T.[Thierry], Khan, M.H.[Muhammad Haris], Jung, S.K.[Soon Ki],
Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods,
RSLCV21(181-188)
IEEE DOI 2112
Deep learning, Learning systems, Computational modeling, Dynamics, Lighting, Benchmark testing BibRef

Oh, S.J., Benenson, R., Khoreva, A., Akata, Z., Fritz, M., Schiele, B.,
Exploiting Saliency for Object Segmentation from Image Level Labels,
CVPR17(5038-5047)
IEEE DOI 1711
Image segmentation, Labeling, Semantics, Shape, Training BibRef

Yang, L.F.[Li-Feng], Hu, Q.H.[Qing-Hua], Zhao, L.[Lei], Li, Y.[Yin],
Salience based hierarchical fuzzy representation for object recognition,
ICIP15(4873-4877)
IEEE DOI 1512
Object recognition. Salience to divide into Object, boundary, background. Then features. BibRef

Wu, X.Y.[Xian-Yan], Han, Q.[Qi], Niu, X.M.[Xia-Mu],
An adaptive transfer scheme based on sparse representation for figure-ground segmentation,
ICIP14(3327-3331)
IEEE DOI 1502
Computational modeling BibRef

Murasaki, K.[Kazuhiko], Sudo, K.[Kyoko], Taniguchi, Y.[Yukinobu],
Occlusion boundary detection based on mid-level figure/ground assignment features,
ICIP14(4707-4711)
IEEE DOI 1502
Accuracy BibRef

Gallo, I.[Ignazio], Zamberletti, A.[Alessandro], Albertini, S.[Simone], Noce, L.[Lucia],
High Entropy Ensembles for Holistic Figure-ground Segmentation,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Li, F.[Feng], Porikli, F.M.[Fatih M.],
Harmonic Variance: A Novel Measure for In-focus Segmentation,
BMVC13(xx-yy).
DOI Link 1402
Foreground detection BibRef

Liu, Q.G.[Qie-Gen], Liu, J.B.[Jian-Bo], Dong, P.[Pei], Liang, D.[Dong],
SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition,
ICCV13(1081-1088)
IEEE DOI 1403
Image decomposition, Structural decorrelating, Structure gradient BibRef

St-Laurent, L.[Louis], Prevost, D.[Donald], Maldague, X.[Xavier],
Combination of thermal and color images for accurate foreground / background segmentation in outdoor environment,
ICIP13(3431-3435)
IEEE DOI 1402
Sensor fusion BibRef

Zhang, Z.[Zhong], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Liu, S.[Shuang], Zhou, W.[Wen],
Multi-scale Fusion of Texture and Color for Background Modeling,
AVSS12(154-159).
IEEE DOI 1211
BibRef

Razavian, A.S.[Ali Sharif], Azizpour, H.[Hossein], Sullivan, J.[Josephine], Carlsson, S.[Stefan],
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition,
DeepLearn14(512-519)
IEEE DOI 1409
BibRef

Aghazadeh, O.[Omid], Azizpour, H.[Hossein], Sullivan, J.[Josephine], Carlsson, S.[Stefan],
Mixture Component Identification and Learning for Visual Recognition,
ECCV12(VI: 115-128).
Springer DOI 1210
Decision boundary between object and background BibRef

Zhu, W.J.[Wang-Jiang], Liang, S.[Shuang], Wei, Y.C.[Yi-Chen], Sun, J.[Jian],
Saliency Optimization from Robust Background Detection,
CVPR14(2814-2821)
IEEE DOI 1409
BibRef

Wei, Y.C.[Yi-Chen], Wen, F.[Fang], Zhu, W.J.[Wang-Jiang], Sun, J.[Jian],
Geodesic Saliency Using Background Priors,
ECCV12(III: 29-42).
Springer DOI 1210
BibRef

Dai, Z.W.[Zhen-Wen], Lucke, J.[Jorg],
Unsupervised learning of translation invariant occlusive components,
CVPR12(2400-2407).
IEEE DOI 1208
Object extraction BibRef

Moshe, Y.[Yair], Hel-Or, H.[Hagit], Hel-Or, Y.[Yacov],
Foreground detection using spatiotemporal projection kernels,
CVPR12(3210-3217).
IEEE DOI 1208
BibRef

Kuettel, D.[Daniel], Ferrari, V.[Vittorio],
Figure-ground segmentation by transferring window masks,
CVPR12(558-565).
IEEE DOI 1208
BibRef

Rosenfeld, A.[Amir], Weinshall, D.[Daphna],
Extracting foreground masks towards object recognition,
ICCV11(1371-1378).
IEEE DOI 1201
BibRef

Maire, M.[Michael], Yu, S.X.[Stella X.], Perona, P.[Pietro],
Hierarchical Scene Annotation,
BMVC13(xx-yy).
DOI Link 1402
BibRef
And:
Object detection and segmentation from joint embedding of parts and pixels,
ICCV11(2142-2149).
IEEE DOI 1201
Segmentation and figure/ground from single grouping. Integrate lowlevel with high-level part detectors. BibRef

Zhao, X.D.[Xu-Dong], Liu, P.[Peng], Liu, J.[Jiafeng], Tang, X.L.[Xiang-Long],
Adaptive background estimation of outdoor illumination variations for foreground detection,
VCIP11(1-4).
IEEE DOI 1201
BibRef

He, S.B.[Shuang-Bai], Yang, J.Q.[Jia-Qian],
Prominent object sharpening using reference image,
IASP11(232-234).
IEEE DOI 1112
Only sharpen the object, not background. BibRef

Ghosh, K.[Kuntal], Roy, A.[Anirban],
Neuro-visually inspired figure-ground segregation,
ICIIP11(1-6).
IEEE DOI 1112
BibRef

Kuang, Z.H.[Zhang-Hui], Zhou, H.[Hao], Wong, K.Y.K.[Kwan-Yee K.],
Accurate Foreground Segmentation without Pre-learning,
ICIG11(331-337).
IEEE DOI 1109
BibRef

Maruta, H.[Hidenori], Ishii, M.[Masahiro], Sato, M.[Makoto],
Salient region extraction based on local extrema of natural images,
ICIP10(1113-1116).
IEEE DOI 1009
BibRef

Han, B.[Bing], Gao, X.B.[Xin-Bo], Walsh, V.[Vincent], Tcheang, L.[Lili],
A saliency map method with cortex-like mechanisms and sparse representation,
CIVR10(259-265).
DOI Link 1007
Visual attention. Non-specific conspicuous object detection. BibRef

Albarelli, A.[Andrea], Rodola, E.[Emanuele], Cavallarin, A.[Alberto], Torsello, A.[Andrea],
Robust Figure Extraction on Textured Background: A Game-Theoretic Approach,
ICPR10(360-363).
IEEE DOI 1008

See also Robust Game-Theoretic Inlier Selection for Bundle Adjustment. BibRef

Qin, G.[Ge], Vrusias, B.[Bogdan], Gillam, L.[Lee],
Background Filtering for Improving of Object Detection in Images,
ICPR10(922-925).
IEEE DOI 1008
BibRef

Maire, M.[Michael],
Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding,
ECCV10(II: 450-464).
Springer DOI 1009
BibRef

Gao, T.S.[Tian-Shi], Packer, B.[Benjamin], Koller, D.[Daphne],
A segmentation-aware object detection model with occlusion handling,
CVPR11(1361-1368).
IEEE DOI 1106
BibRef

Packer, B.[Ben], Gould, S.[Stephen], Koller, D.[Daphne],
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Visual Organization for Figure/Ground Separation,
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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Sensors for Machine Vision, Image Sensors .