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Amplitude Decomposition Model; Detection; Segmentation; Focused
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1506
Computational modeling
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1909
Detect foreground then encode it.
computer vision, feature extraction, image classification,
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ICIP15(4873-4877)
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1512
Object recognition. Salience to divide into
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ICIP14(3327-3331)
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1502
Computational modeling
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1502
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1402
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ICCV13(1081-1088)
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1403
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1402
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1211
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1210
Decision boundary between object and background
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1409
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1210
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1208
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1208
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Figure-ground segmentation by transferring window masks,
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IEEE DOI
1208
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1201
With depth.
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1201
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Object detection and segmentation from joint embedding of parts and
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ICCV11(2142-2149).
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1201
Segmentation and figure/ground from single grouping. Integrate lowlevel
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VCIP11(1-4).
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1201
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Prominent object sharpening using reference image,
IASP11(232-234).
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1112
Only sharpen the object, not background.
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Neuro-visually inspired figure-ground segregation,
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1112
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Accurate Foreground Segmentation without Pre-learning,
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Simultaneous Segmentation and Figure/Ground Organization Using Angular
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ECCV10(II: 450-464).
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1009
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Cluster sets of interest points into visually distinct structures using
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See also Saliency detection using maximum symmetric surround.
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Background Cut,
ECCV06(II: 628-641).
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0608
Real-time foreground layer extraction.
Attenuate background contrast, leave foreground.
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Figure/Ground Assignment in Natural Images,
ECCV06(II: 614-627).
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Foveated Figure-Ground Segmentation and Its Role in Recognition,
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Simultaneous Matting and Compositing,
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An Iterative Optimization Approach for Unified Image Segmentation and
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ICCV05(II: 936-943).
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Background Estimation as a Labeling Problem,
ICCV05(II: 1034-1041).
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Illusory Surface Perception Using a Hierarchical Neural Network Model
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Pyramid Transform and Scale-Space Analysis in Image Analysis,
WTFCV11(78-109).
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Figure Field Analysis of Linear Scale-Space Image,
ScaleSpace05(374-385).
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A Background Maintenance Model in the Spatial-Range Domain,
SMVP04(141-152).
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Foreground/Background codebook descriptions.
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Multiplicative Background-Foreground Estimation Under Uncontrolled
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Motion05(II: 20-27).
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WACV05(I: 37-44).
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Features from patches, how to exclude the background.
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Fusing Complementary Operators to Enhance Foreground/Background
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Improved adaptive gaussian mixture model for background subtraction,
ICPR04(II: 28-31).
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Detecting Distinguished Regions by Saliency,
SCIA03(208-215).
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Background subtraction using competing models in the block-DCT domain,
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Probabilistic and Voting Approaches to Cue Integration for
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ECCV02(III: 469 ff.).
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Bayesian Estimation of Layers from Multiple Images,
ECCV02(III: 487 ff.).
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To extract the foreground objects. (Blue screen technique.)
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Figure-Ground separation.
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Convex Relaxation for Figure-Ground Discrimination and Perceptual
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PercOrg01(353-360).
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Relaxations for Binary Image Partitioning and Perceptual Grouping,
DAGM01(353-360).
Award, DAGM.
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Integration of Perceptual Grouping and Depth,
ICPR00(Vol I: 295-298).
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Tsaptsinos, D.,
Jones, G.A.,
Foreground-background Segmentation by Cellular Neural Networks,
ICPR00(Vol II: 438-441).
IEEE DOI
0009
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Pao, H.,
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Measuring Convexity for Figure/Ground Separation,
ICCV99(948-955).
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Geiger, D.[Davi],
Kumaran, K.[Krishnan],
Parida, L.[Laxmi],
Visual Organization for Figure/Ground Separation,
CVPR96(155-160).
IEEE DOI
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9600
Geiger, D.,
Kumaran, K.,
Visual Organization of Illusory Surfaces,
ECCV96(I:413-424).
Springer DOI Use occlusions to find the most salient contours.
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Stricker, M.,
Leonardis, A.,
Figure-Ground Segmentation Using Tabu Search,
SCV95(605-610).
IEEE DOI Swiss Federal Institute of Technology. U. of Ljubljana.
Compared with mean field annealing algorithm.
BibRef
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Huang, Q.[Qian],
Dam, B.,
Steele, D.,
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Niblack, W.,
Foreground/background segmentation of color images by integration of
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ICIP95(I: 246-249).
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Heitger, F., and
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A Computational Model of Neural Contour Processing:
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ICCV93(32-40).
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Shimaya, A.,
Yoroizawa, I.,
A Cognitive Model Of Figure Segregation,
IJCAI91(366-372).
BibRef
9100
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 .