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.[Ryuji], 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

Koenderink, J.J.[Jan J.], van Doorn, A.J.[Andrea J.], Pont, S.C.[Sylvia C.], Richards, W.[Whitman],
Gestalt and phenomenal transparency,
JOSA-A(25), No. 1, January 2008, pp. 190-202.
WWW Link. 0801
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.[Yuri],
A Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation,
SIIMS(5), No. 4, 2012, pp. 1333-1354.
DOI Link 1211
BibRef

Amer, M.R.[Mohamed R.], Yousefi, S.[Siavash], Raich, R.[Raviv], Todorovic, S.[Sinisa],
Monocular Extraction of 2.1D Sketch Using Constrained Convex Optimization,
IJCV(112), No. 1, March 2015, pp. 23-42.
Springer DOI 1503
Depth ordering. 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.[Yujin],
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. computer vision, feature extraction, image classification, image representation, neural nets, vectors, convolutional neural networks 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

Phillips, C.J.[Cody J.], Derpanis, K.G.P.[Konstantinos G.P.], Daniilidis, K.[Kostas],
A novel stereoscopic cue for figure-ground segregation of semi-transparent objects,
RobPerc11(1100-1107).
IEEE DOI 1201
With depth. 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.[Xinbo], 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.[Tianshi], 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],
A Unified Contour-Pixel Model for Figure-Ground Segmentation,
ECCV10(V: 338-351).
Springer DOI 1009
BibRef

Fragkiadaki, K.[Katerina], Shi, J.B.[Jian-Bo],
Figure-Ground Image Segmentation Helps Weakly-Supervised Learning of Objects,
ECCV10(VI: 561-574).
Springer DOI 1009
BibRef

Arens, M.[Michael], Anderer, C.[Claus],
Measuring the quality of figure/ground segmentations,
OTCBVS10(52-59).
IEEE DOI 1006
BibRef

Ren, X.F.[Xiao-Feng], Gu, C.H.[Chun-Hui],
Figure-ground segmentation improves handled object recognition in egocentric video,
CVPR10(3137-3144).
IEEE DOI 1006
BibRef

Jahangiri, M.[Mohammad], Petrou, M.[Maria],
An attention model for extracting components that merit identification,
ICIP09(965-968).
IEEE DOI 0911
See also Component Identification in the 3D Model of a Building. BibRef

Liu, M.H.[Man-Hua], Yao, J.C.[Jian-Chao], Zhao, H.[Hui], Yap, K.H.[Kim-Hui],
Learning-Based Image Ground Segmentation Using Multiple Cues,
CISP09(1-5).
IEEE DOI 0910
BibRef

Yuan, L.[Liu], Chun, Y.[Yuan],
Automatic Segmentation of Background Defocused Nature Image,
CISP09(1-5).
IEEE DOI 0910
BibRef

Estrada, F.J.[Francisco J.], Fua, P.[Pascal], Lepetit, V.[Vincent], Susstrunk, S.[Sabine],
Appearance-based keypoint clustering,
CVPR09(1279-1286).
IEEE DOI 0906
Cluster sets of interest points into visually distinct structures using color and texture. BibRef

Achanta, R.[Radhakrishna], Hemami, S.[Sheila], Estrada, F.[Francisco], Susstrunk, S.[Sabine],
Frequency-tuned salient region detection,
CVPR09(1597-1604).
IEEE DOI 0906
See also Saliency detection using maximum symmetric surround. BibRef

Liu, Y.[Yuee], Zhang, J.L.[Jing-Lan], Tjondronegoro, D.[Dian], Geva, S.[Shlomo], Li, Z.R.[Zheng-Rong],
An improved image segmentation algorithm for salient object detection,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Sharma, G.[Gaurav], Chaudhury, S.[Santanu], Srivastava, J.B.,
Object Category Detection by Statistical Test of Hypothesis,
ICCVGIP08(474-480).
IEEE DOI 0812
BibRef

Fan, S., Ferrie, F.P.,
Structure Guided Salient Region Detector,
BMVC08(xx-yy).
PDF File. 0809
BibRef

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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 .