11.8.3 Deformable Models for Segmentation

Chapter Contents (Back)
Part Segmentation. Deformable Models. See also Interactive 3D Segmentations, Depth, Range, Stereo. See also Active Contours and Snakes, Region Segmentation Issues.

Delingette, H., Hebert, M., Ikeuchi, K.,
Shape Representation and Image Segmentation Using Deformable Surfaces,
IVC(10), No. 3, April 1992, pp. 132-144.
Elsevier DOI BibRef 9204
And: CVPR91(467-472).
IEEE DOI BibRef
And:
Deformable Surfaces: A Free-Form Shape Representation,
SPIE(1570), 1991, pp. 21-30. BibRef

Delingette, H., Hebert, M., Ikeuchi, K.,
Energy Functions for Regularization Algorithms,
SPIE(1570), 1991, pp. 104-115. BibRef 9100

Cohen, I.[Isaac], Cohen, L.D.[Laurent D.], Ayache, N.J.[Nicholas J.],
Using Deformable Surfaces to Segment 3-D Images and Infer Differential Structures,
CVGIP(56), No. 2, September 1992, pp. 242-263.
Elsevier DOI BibRef 9209
Earlier: ECCV92(648-652).
Springer DOI BibRef
Earlier:
Introducing New Deformable Surfaces to Segment 3D Images,
CVPR91(738-739).
IEEE DOI BibRef
And: TRInria 1403, May 1991. Fit a 3-D surface to range data using a discrete basis of continuous functions. This leads to a segmented description of the surface. BibRef

Cohen, I.[Isaac], Ayache, N.J.[Nicholas J.], and Sulger, P.[Patrick],
Tracking Points on Deformable Objects Using Curvature Information,
ECCV92(458-466).
Springer DOI BibRef 9200
And:
Tracking Points on Deformables Objects,
INRIATR 1595, February 1992. BibRef

Cohen, L.D.[Laurent D.],
Chemins Minimaux et Modeles Deformables en Analyse d'Images,
Traitement du Signal(20), No 3, December 2003, pp. 225-241.
PDF File. BibRef 0312

Cohen, L.D.[Laurent D.],
Methodes Variationnelles pour le Traitement d'images,
Memoire d'Habilitationa diriger des recherches. Universite Paris Dauphine, 1995. BibRef 9500

Gupta, A., and Bajcsy, R.,
Volumetric Segmentation of Range Images of 3D Objects Using Superquadric Models,
CVGIP(58), No. 3, November 1993, pp. 302-326.
DOI Link BibRef 9311
Earlier:
Surface and Volumetric Segmentation of Range Images Using Biquadrics and Superquadrics,
ICPR92(I:158-162).
IEEE DOI BibRef
And:
Integrated Approach for Surface and Volumetric Segmentation of Range Images Using Biquadrics and Superquadrics,
SPIE(1708), 1992, pp. 210-227. BibRef

Gupta, A.,
Surface and Volumetric Segmentation of Complex 3D Objects Using Parametric Shape Models,
Ph.D.Thesis, Computer and Information Science, 1991. BibRef 9100 UPennTR MS-CIS-91-45, Grasp Lab 128. BibRef

Leonardis, A., Gupta, A., Bajcsy, R.,
Segmentation of Range Images as the Search for Geometric Parametric Models,
IJCV(14), No. 3, April 1995, pp. 253-277.
Springer DOI BibRef 9504
Earlier:
Segmentation as the Search for the Best Description of Images in Terms of Primitives,
ICCV90(121-125).
IEEE DOI BibRef
And: UPennTR MS-CIS-90-30, GRASP LAB 215, May 1990. BibRef

Kumar, S., Han, S., Goldgof, D., Bowyer, K.W.,
On Recovering Hyperquadrics from Range Data,
PAMI(17), No. 11, November 1995, pp. 1079-1083.
IEEE DOI
WWW Link. BibRef 9511

Kumar, S., Goldgof, D.,
Model Based Part Segmentation of Range Data: Hyperquadrics and Dividing Planes,
PBMCV95(SESSION 1). BibRef 9500
And:
A Robust Technique for the Estimation of the Deformable Hyperquadrics from Images,
ICPR94(A:74-78).
IEEE DOI BibRef

Han, S., Goldgof, D.B., and Bowyer, K.W.,
Using Hyperquadrics for Shape Recovery from Range Data,
ICCV93(492-496).
IEEE DOI BibRef 9300

Snell, J.W., Merickel, M.B., Ortega, J.M., Goble, J.C., Brookeman, J.R., Kassell, N.F.,
Model-Based Boundary Estimation of Complex Objects Using Hierarchical Active Surface Templates,
PR(28), No. 10, October 1995, pp. 1599-1609.
Elsevier DOI BibRef 9510

Nishida, H.,
A Structural Model of Shape Deformation,
PR(28), No. 10, October 1995, pp. 1611-1620.
Elsevier DOI For 2d: See also Structural Model of Curve Deformation by Discontinuous Transformations, A. BibRef 9510

DeCarlo, D.[Douglas], Metaxas, D.[Dimitri],
Blended Deformable Models,
PAMI(18), No. 4, April 1996, pp. 443-448.
IEEE DOI BibRef 9604 CVPR94(566-572).
IEEE DOI BibRef
And:
Adaptive Model Evolution Using Blending,
ICCV95(834-839).
IEEE DOI Description based on linear interpoloation of two parameterized shapes. Blend two separate shapes (cup handle and cup) for a better match. Handles more than genus 0 objects. 9605
BibRef

DeCarlo, D.[Douglas], Metaxas, D.[Dimitri],
Shape Evolution with Structural and Topological Changes Using Blending,
PAMI(20), No. 11, November 1998, pp. 1186-1205.
IEEE DOI 9811
BibRef

DeCarlo, D.[Douglas], Metaxas, D.[Dimitris],
Adjusting Shape Parameters Using Model-Based Optical Flow Residuals,
PAMI(24), No. 6, June 2002, pp. 814-823.
IEEE DOI 0206
BibRef
Earlier:
Deformable Model-Based Shape and Motion Analysis from Images using Motion Residual Error,
ICCV98(113-119).
IEEE DOI Shape of deformable model from optical flow. See also Optical Flow Constraints on Deformable Models with Applications to Face Tracking. BibRef

Malladi, R., Sethian, J.A.,
A Unified Approach to Noise Removal, Image-Enhancement, and Shape Recovery,
IP(5), No. 11, November 1996, pp. 1554-1568.
IEEE DOI 9611
BibRef
And:
A Real-Time Algorithm for Medical Shape Recovery,
ICCV98(304-310).
IEEE DOI BibRef

Kita, Y.,
Elastic-Model Driven Analysis of Several Views of a Deformable Cylindrical Object,
PAMI(18), No. 12, December 1996, pp. 1150-1162.
IEEE DOI 9701
BibRef
Earlier:
Model-Driven Contour Extraction for Physically Deformed Objects: Application to Analysis of Stomach X-Ray Images,
ICPR92(I:280-284).
IEEE DOI BibRef

Little, J.A., Hill, D.L.G., Hawkes, D.J.,
Deformations Incorporating Rigid Structures,
CVIU(66), No. 2, May 1997, pp. 223-232.
DOI Link 9705
BibRef
Earlier: MMBIA96(REGISTRATION II) BibRef

Dickinson, S.J., Metaxas, D.N., Pentland, A.P.,
The Role of Model-Based Segmentation in the Recovery of Volumetric Parts from Range Data,
PAMI(19), No. 3, March 1997, pp. 259-267.
IEEE DOI 9704
Aspects. Segmentation and shape from range data. Constrain the fitting using model views (aspects). BibRef

Tek, H.[Huseyin], Kimia, B.B.[Benjamin B.],
Volumetric Segmentation of Medical Images by Three-Dimensional Bubbles,
CVIU(65), No. 2, February 1997, pp. 246-258.
DOI Link 9704
BibRef
Earlier: PBMCV95(SESSION 1) BibRef
Earlier:
Image Segmentation by Reaction-Diffusion Bubbles,
ICCV95(156-162).
IEEE DOI BibRef
And:
Shock-Based Reaction-Diffusion Bubbles for Image Segmentation,
CVRMed95(XX-YY). Combine the two parameters to better fit objects. BibRef

Caselles, V.[Vincent], Kimmel, R.[Ron], Sapiro, G.[Guillermo], Sbert, C.[Catalina],
Minimal-Surfaces Based Object Segmentation,
PAMI(19), No. 4, April 1997, pp. 394-398.
IEEE DOI 9705
BibRef
And:
Three Dimensional Object Modeling via Minimal Surfaces,
ECCV96(I:97-106).
Springer DOI Start deformable surface outside the object, move it toward the object. BibRef
And:
Minimal Surfaces: A Three-Dimensional Segmentation Approach,
TRTechnion TR 973, June 1995. Deformable surface moving toward the object. BibRef

Rougon, N.E., Preteux, F.,
Directional Adaptive Deformable Models For Segmentation,
JEI(7), No. 1, January 1998, pp. 231-256. 9807
BibRef
Earlier:
Understanding the structure of diffusive scale-spaces,
ICPR96(II: 844-848).
IEEE DOI 9608
A Gauge Theory Approach. (Institut National de Telecom., F) BibRef

Ruff, C.R., Hughes, S.W., Hawkes, D.J.,
Volume estimation from sparse planar images using deformable models,
IVC(17), No. 8, June 1999, pp. 559-565.
Elsevier DOI BibRef 9906
Earlier: BMVC97(xx-yy).
HTML Version. BibRef

Cheung, K.W.[Kwok-Wai], Yeung, D.Y.[Dit-Yan], Chin, R.T.[Roland T.],
On deformable models for visual pattern recognition,
PR(35), No. 7, July 2002, pp. 1507-1526.
Elsevier DOI 0204
BibRef

van Ginneken, B.[Bram], Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.,
Active shape model segmentation with optimal features,
MedImg(21), No. 8, August 2002, pp. 924-933.
IEEE Top Reference. 0301
BibRef
Earlier:
A Non-Linear Gray-Level Appearance Model Improves Active Shape Model Segmentation,
MMBIA01(xx-yy). 0110
BibRef

Butakoff, C.[Costantine], Frangi, A.F.[Alejandro F.],
A Framework for Weighted Fusion of Multiple Statistical Models of Shape and Appearance,
PAMI(28), No. 11, November 2006, pp. 1847-1857.
IEEE DOI 0609
Eigenspace fusion method of several active shape and active appearance models. Facial Verification. Conclude: fusion is useful when the model needs to be updated online or when the original observations are absent. BibRef

Sukno, F.M.[Federico M.], Ordás, S.[Sebastián], Butakoff, C.[Constantine], Cruz, S.[Santiago], Frangi, A.F.[Alejandro F.],
Active Shape Models with Invariant Optimal Features: Application to Facial Analysis,
PAMI(29), No. 7, July 2007, pp. 1105-1117.
IEEE DOI 0706
Facial Features. BibRef
Earlier:
Active Shape Models with Invariant Optimal Features (IOF-ASMs),
AVBPA05(365).
Springer DOI 0509
Accurate segmentation of prominant features. See also Automatic Pose Correction for Local Feature-Based Face Authentication. See also Bilinear Models for Spatio-Temporal Point Distribution Analysis: Application to Extrapolation of Left Ventricular, Biventricular and Whole Heart Cardiac Dynamics. BibRef

Sukno, F.M.[Federico M.], Frangi, A.F.[Alejandro F.],
Reliability Estimation for Statistical Shape Models,
IP(17), No. 12, December 2008, pp. 2442-2455.
IEEE DOI 0811
BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Yuille, A.L.[Alan L.],
Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories,
PAMI(31), No. 1, January 2009, pp. 114-128.
IEEE DOI 0812
See also Active Mask Hierarchies for Object Detection. BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Yuille, A.L.[Alan L.],
Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing,
PAMI(32), No. 6, June 2010, pp. 1029-1043.
IEEE DOI 1004
Detect, segment, parse and match deformable objects. HDT: Hierarchical deformable template. 5 levels. Recursive describe elementary structures to form complex structures. See also Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation. BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Lin, C.X.[Chen-Xi], Yuille, A.L.[Alan L.],
Max Margin Learning of Hierarchical Configural Deformable Templates (HCDTs) for Efficient Object Parsing and Pose Estimation,
IJCV(93), No. 1, May 2011, pp. 1-21.
WWW Link. 1104
See also Recursive Segmentation and Recognition Templates for Image Parsing. BibRef

Zhu, L.L.[Long Leo], Lin, C.X.[Chen-Xi], Huang, H.[Haoda], Chen, Y.H.[Yuan-Hao], Yuille, A.L.[Alan L.],
Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion,
ECCV08(II: 759-773).
Springer DOI 0810
Hierarchical model for deformable objects. BibRef

Arambula Cosio, F., Marquez Flores, J.A., Padilla Castaneda, M.A.,
Use of simplex search in active shape models for improved boundary segmentation,
PRL(31), No. 9, 1 July 2010, pp. 806-817.
Elsevier DOI 1004
Boundary segmentation; Active shape models; Simplex search BibRef

Mishra, A.K.[Akshaya K.], Fieguth, P.W.[Paul W.], Clausi, D.A.[David A.],
From active contours to active surfaces,
CVPR11(2121-2128).
IEEE DOI 1106
BibRef
Earlier:
Decoupled Active Surface for Volumetric Image Segmentation,
CRV10(293-300).
IEEE DOI 1005
See also Bayesian Information Flow Approach to Image Segmentation, A. See also Decoupled Active Contour (DAC) for Boundary Detection. BibRef

Wong, T.H.[Tsz Ho], Leach, G.[Geoff], Zambetta, F.[Fabio],
Virtual subdivision for GPU based collision detection of deformable objects using a uniform grid,
VC(27), No. 6-8, June 2011, pp. 829-838.
WWW Link. 1205
BibRef

Wong, T.H.[Tsz Ho], Leach, G.[Geoff], Zambetta, F.[Fabio],
An adaptive octree grid for GPU-based collision detection of deformable objects,
VC(30), No. 6-8, June 2014, pp. 729-738.
WWW Link. 1407
BibRef

Krueger, M.[Matthias], Delmas, P.[Patrice], Gimel'farb, G.L.[Georgy L.],
Robust and efficient object segmentation using pseudo-elastica,
PRL(34), No. 8, June 2013, pp. 833-845.
Elsevier DOI 1305
BibRef
Earlier:
Efficient Image Segmentation Using Weighted Pseudo-Elastica,
CAIP11(I: 59-67).
Springer DOI 1109
BibRef
Earlier:
Active Contour Based Segmentation of 3D Surfaces,
ECCV08(II: 350-363).
Springer DOI 0810
Object segmentation; Image segmentation; Second-order energy; Curvature regularity; Active contour; Elastica BibRef

Ye, J.B.[Jian-Bo], Yu, Y.Z.[Yi-Zhou],
A fast modal space transform for robust nonrigid shape retrieval,
VC(32), No. 5, May 2016, pp. 553-568.
Springer DOI 1605
BibRef

Wang, X.P.[Xu-Peng], Sohel, F.[Ferdous], Bennamoun, M.[Mohammed], Guo, Y.L.[Yu-Lan], Lei, H.[Hang],
Scale space clustering evolution for salient region detection on 3D deformable shapes,
PR(71), No. 1, 2017, pp. 414-427.
Elsevier DOI 1707
Deformable shape segmentation BibRef

Cheng, D., Gong, Y., Wang, J., Zheng, N.,
Balanced Mixture of Deformable Part Models With Automatic Part Configurations,
CirSysVideo(27), No. 9, September 2017, pp. 1962-1973.
IEEE DOI 1709
Computational modeling, Deformable models, Feature extraction, Object detection, Shape, Standards, Training, Automatic, expectation-maximization (EM) framework, mixture, part, configurations BibRef


Yang, J.[Jun], Li, G.[Ge], Wang, W.[Wenmin], Wang, R.G.[Rong-Gang],
An Empirical Study of Deformable Part Model with fast feature pyramid,
ICPR16(567-572)
IEEE DOI 1705
Computational modeling, Deformable models, Detectors, Feature extraction, Histograms, Mathematical model, Object, detection BibRef

Wang, P., Shen, X., Lin, Z., Cohen, S., Price, B.L., Yuille, A.L.,
Joint Object and Part Segmentation Using Deep Learned Potentials,
ICCV15(1573-1581)
IEEE DOI 1602
Context BibRef

Transue, S.[Shane], Choi, M.H.[Min-Hyung],
Deformable Object Behavior Reconstruction Derived Through Simultaneous Geometric and Material Property Estimation,
ISVC15(II: 474-485).
Springer DOI 1601
BibRef

Chan, K.C.[Kai Chi], Ayvaci, A.[Alper], Heisele, B.[Bernd],
Partially occluded object detection by finding the visible features and parts,
ICIP15(2130-2134)
IEEE DOI 1512
Deformable Part Models; Partially Occluded Object Detection BibRef

Girshick, R.[Ross], Iandola, F.[Forrest], Darrell, T.J.[Trevor J.], Malik, J.[Jitendra],
Deformable part models are convolutional neural networks,
CVPR15(437-446)
IEEE DOI 1510
BibRef

Sun, C.[Chaobo], Wang, X.[Xiaojie], Lu, P.[Peng],
Object Ranking on Deformable Part Models with Bagged LambdaMART,
ACCV14(II: 59-71).
Springer DOI 1504
BibRef

Ghiasi, G.[Golnaz], Fowlkes, C.C.[Charless C.],
Using Segmentation to Predict the Absence of Occluded Parts,
BMVC15(xx-yy).
DOI Link 1601
BibRef
Earlier:
Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,
CVPR14(1899-1906)
IEEE DOI 1409
Face Detection; Occlusion; Pose Estimation BibRef

Xie, W.G.[Wei-Guo], Schumann, S.[Steffen], Franke, J.[Jochen], Grützner, P.A.[Paul Alfred], Nolte, L.P.[Lutz-Peter], Zheng, G.[Guoyan],
Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models,
MLMI12(258-265).
Springer DOI 1211
BibRef

Allain, B.[Benjamin], Franco, J.S.[Jean-Sébastien], Boyer, E.[Edmond], Tung, T.[Tony],
On Mean Pose and Variability of 3D Deformable Models,
ECCV14(II: 284-297).
Springer DOI 1408
BibRef

Letouzey, A.[Antoine], Boyer, E.[Edmond],
Progressive shape models,
CVPR12(190-197).
IEEE DOI 1208
Recover deformable mesh models through sequences. BibRef

Chen, C.[Chao], Freedman, D.[Daniel],
Topology Noise Removal for Curve and Surface Evolution,
MCV10(31-42).
Springer DOI 1009
BibRef

Park, J.H.[Jong-Hyun], Cho, W.H.[Wan-Hyun], Park, S.Y.[Soon-Young], Kim, S.[Sunworl], Kim, S.Y.[Sooh-Yung], Ahn, G.[Gukdong], Lee, M.[Myungeun], Lee, G.S.[Guee-Sang],
Segmentation of 3D object in volume dataset using active deformable model,
ICIP10(4121-4124).
IEEE DOI 1009
BibRef

Zhang, J.D.[Jing-Dan], Zhou, S.H.K.[Shao-Hua Kevin], Comaniciu, D.[Dorin], McMillan, L.[Leonard],
Discriminative Learning for Deformable Shape Segmentation: A Comparative Study,
ECCV08(I: 711-724).
Springer DOI 0810
BibRef
Earlier:
Conditional density learning via regression with application to deformable shape segmentation,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Arias, P.[Pablo], Randall, G.[Gregory], Sapiro, G.[Guillermo],
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods,
CVPR07(1-8).
IEEE DOI 0706
Dealing with outliers. Apply to deformable shapes. BibRef

Ye, J.[Jian], Yanovsky, I.[Igor], Dong, B.[Bin], Gandlin, R.[Rima], Brandt, A.[Achi], Osher, S.J.[Stanley J.],
Multigrid Narrow Band Surface Reconstruction via Level Set Functions,
ISVC12(I: 61-70).
Springer DOI 1209
BibRef

Yanovsky, I.[Igor], Thompson, P.M.[Paul M.], Osher, S.J.[Stanley J.], Vese, L.A.[Luminita A.], Leow, A.D.[Alex D.],
Multiphase Segmentation of Deformation using Logarithmic Priors,
Fusion07(1-6).
IEEE DOI 0706
BibRef

Kimura, A., Takama, Y., Yamazoe, Y.[Yu], Tanaka, S., Tanaka, H.T.,
Parallel volume segmentation with tetrahedral adaptive grid,
ICPR04(II: 281-286).
IEEE DOI 0409
BibRef

van Ginneken, B., Loog, M.,
Pixel Position Regression: Application to Medical Image Segmentation,
ICPR04(III: 718-721).
IEEE DOI 0409
BibRef

Bowden, R., Mitchell, T.A., Sahardi, M.,
Real-time Dynamic Deformable Meshes for Volumetric Segmentation and Visualisation,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Dickens, M.M., Gleason, S.S., Sari-Sarraf, H.,
Volumetric segmentation via 3D active shape models,
Southwest02(248-252).
IEEE Top Reference. 0208
BibRef

Ohuchi, M., Saito, T.,
Three-dimensional shape modeling with extended hyperquadrics,
3DIM01(262-269).
IEEE DOI 0106
BibRef

Little, J.J.[James J.],
Deforming Surface Features Lines in Intrinsic Coordinates,
ICPR00(Vol I: 291-294).
IEEE DOI 0009
BibRef

Delingette, H.,
Initialization of Deformable Models from 3D Data,
ICCV98(311-316).
IEEE DOI BibRef 9800

Jones, T.N.[Timothy N.], Metaxas, D.N.[Dimitris N.],
Image Segmentation Based on the Integration of Pixel Affinity and Deformable Models,
CVPR98(330-337).
IEEE DOI BibRef 9800

Jones, T.N., and Metaxas, D.N.,
Segmentation Using Deformable Models with Affinity-Based Localization,
CVRMed-MRCAS97(53-62).
HTML Version. BibRef 9700

Wu, K.N.[Ke-Nong], Levine, M.D.[Martin D.],
Segmenting 3D objects into geons,
CIAP95(320-334).
Springer DOI 9509
BibRef
And:
3D Part Segmentation: A New Physics-Based Approach,
SCV95(311-316).
IEEE DOI McGill University. Object boundaries are assumed to be at strong concavities. For 2d work: See also 2D Shape Segmentation: A New Approach. BibRef

Sato, Y., Ohya, J., and Ishii, K.,
Recovery of Hierarchical Part Structure of 3D Shape from Range Image,
CVPR92(699-702).
IEEE DOI BibRef 9200

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Nonrigid, Non-Rigid, Deformable Motion Analysis and Tracking .


Last update:Sep 25, 2017 at 16:36:46