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.[Kevin 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.N.[Dimitris N.],
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.A.[Ferdous A.],
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
Meng, F.,
Li, H.,
Wu, Q.,
Ngan, K.N.,
Cai, J.,
Seeds-Based Part Segmentation by Seeds Propagation and Region
Convexity Decomposition,
MultMed(20), No. 2, February 2018, pp. 310-322.
IEEE DOI
1801
Image segmentation, Object detection, Proposals, Semantics, Shape,
Training data,
weakly supervised segmentation
BibRef
Li, J.,
Wong, H.C.,
Lo, S.L.,
Xin, Y.,
Multiple Object Detection by a Deformable Part-Based Model and an
R-CNN,
SPLetters(25), No. 2, February 2018, pp. 288-292.
IEEE DOI
1802
convolution, feature extraction, filtering theory, graph theory,
object detection, DPM, PASCAL VOC dataset, R-CNN,
region-based convolutional network (R-CNN)
BibRef
Casillas-Perez, D.[David],
Pizarro, D.[Daniel],
Fuentes-Jimenez, D.[David],
Mazo, M.[Manuel],
Bartoli, A.E.[Adrien E.],
Equiareal Shape-from-Template,
JMIV(61), No. 5, June 2019, pp. 607-626.
Springer DOI
1906
3D reconstruction of a deformable surface from a single image and a
reference.
BibRef
Zhang, Y.P.[Yan-Ping],
Liang, Q.K.[Qiao-Kang],
Zou, K.[Kunlin],
Li, Z.W.[Zheng-Wei],
Sun, W.[Wei],
Wang, Y.[Yaonan],
Self-supervised part segmentation via motion imitation,
IVC(120), 2022, pp. 104393.
Elsevier DOI
2204
Motion imitation, Self-supervised, Part segmentation
BibRef
Fuentes-Jimenez, D.[David],
Pizarro, D.[Daniel],
Casillas-Pérez, D.[David],
Collins, T.[Toby],
Bartoli, A.[Adrien],
Deep Shape-from-Template: Single-image quasi-isometric deformable
registration and reconstruction,
IVC(127), 2022, pp. 104531.
Elsevier DOI
2211
Monocular, 3D Model, Registration, Reconstruction, Wide-baseline,
Dense, Deformable, Shape-from-Template
BibRef
Rewatbowornwong, P.[Pitchaporn],
Tritrong, N.[Nontawat],
Suwajanakorn, S.[Supasorn],
Repurposing GANs for One-Shot Semantic Part Segmentation,
PAMI(45), No. 4, April 2023, pp. 5114-5125.
IEEE DOI
2303
BibRef
Earlier: A2, A1, A3:
CVPR21(4473-4483)
IEEE DOI
2111
Image segmentation, Task analysis, Semantics, Annotations, Training,
Representation learning, Generative adversarial networks,
generative model.
Image synthesis, Transfer learning, Pipelines, Feature extraction.
BibRef
Rewatbowornwong, P.[Pitchaporn],
Chatthee, N.[Nattanat],
Chuangsuwanich, E.[Ekapol],
Suwajanakorn, S.[Supasorn],
Zero-guidance Segmentation Using Zero Segment Labels,
ICCV23(1162-1172)
IEEE DOI Code:
WWW Link.
2401
BibRef
Parashar, S.[Shaifali],
Pizarro, D.[Daniel],
Bartoli, A.E.[Adrien E.],
Local Deformable 3D Reconstruction with Cartan's Connections,
PAMI(42), No. 12, December 2020, pp. 3011-3026.
IEEE DOI
2011
Deformable models,
Surface reconstruction, Image reconstruction, Solid modeling,
3D computer vision
BibRef
Lei, N.[Na],
Huang, J.[Jisui],
Chen, K.[Ke],
Ren, Y.[Yuxue],
Saucan, E.[Emil],
Wang, Z.C.[Zhen-Chang],
Shang, Y.Y.[Yuan-Yuan],
Ricci curvature based volumetric segmentation,
IVC(150), 2024, pp. 105192.
Elsevier DOI
2409
Ricci curvature, Variational model, Level set, Image segmentation
BibRef
Xie, G.H.[Guang-Hu],
Liu, Y.[Yang],
Ji, Y.M.[Yi-Ming],
Xie, Z.[Zongwu],
Cao, B.[Baoshi],
PSVMLP: Point and Shifted Voxel MLP for 3D deep learning,
PRL(185), 2024, pp. 1-7.
Elsevier DOI Code:
WWW Link.
2410
Deep learning, Shape part segmentation, Shape classification, Point clouds
BibRef
Kareem, A.[Amrin],
Lahoud, J.[Jean],
Cholakkal, H.[Hisham],
Paris3d: Reasoning-based 3d Part Segmentation Using Large Multimodal
Model,
ECCV24(LXXII: 466-482).
Springer DOI
2412
BibRef
Aniraj, A.[Ananthu],
Dantas, C.F.[Cassio F.],
Ienco, D.[Dino],
Marcos, D.[Diego],
Pdiscoformer: Relaxing Part Discovery Constraints with Vision
Transformers,
ECCV24(LXXXV: 256-272).
Springer DOI
2412
BibRef
Chen, Y.Y.[Yi-Yang],
Duan, L.[Lunhao],
Zhao, S.S.[Shan-Shan],
Ding, C.X.[Chang-Xing],
Tao, D.C.[Da-Cheng],
Local-consistent Transformation Learning for Rotation-invariant Point
Cloud Analysis,
CVPR24(5418-5427)
IEEE DOI Code:
WWW Link.
2410
Geometry, Point cloud compression, Codes, Shape, Fuses,
Perturbation methods, Rotation-invariant Representation,
3D Part Segmentation
BibRef
Du, B.[Bi'an],
Gao, X.[Xiang],
Hu, W.[Wei],
Liao, R.J.[Ren-Jie],
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing,
CVPR24(20850-20859)
IEEE DOI Code:
WWW Link.
2410
Point cloud compression, Training, Solid modeling, Shape,
Message passing, Semantics
BibRef
Umam, A.[Ardian],
Yang, C.K.[Cheng-Kun],
Chen, M.H.[Min-Hung],
Chuang, J.H.[Jen-Hui],
Lin, Y.Y.[Yen-Yu],
PartDistill: 3D Shape Part Segmentation by Vision-Language Model
Distillation,
CVPR24(3470-3479)
IEEE DOI Code:
WWW Link.
2410
Point cloud compression, Solid modeling, Codes, Shape,
Bidirectional control, 3d part segmentation,
cross-modal distillation
BibRef
Henrich, P.[Pit],
Gyenes, B.[Balázs],
Scheikl, P.M.[Paul Maria],
Neumann, G.[Gerhard],
Mathis-Ullrich, F.[Franziska],
Registered and Segmented Deformable Object Reconstruction from a
Single View Point Cloud,
WACV24(3117-3126)
IEEE DOI
2404
Point cloud compression, Deformable models, Training data,
Algorithms, 3D computer vision, Algorithms
BibRef
Sun, P.[Peize],
Chen, S.[Shoufa],
Zhu, C.C.[Chen-Chen],
Xiao, F.[Fanyi],
Luo, P.[Ping],
Xie, S.[Saining],
Yan, Z.C.[Zhi-Cheng],
Going Denser with Open-Vocabulary Part Segmentation,
ICCV23(15407-15419)
IEEE DOI
2401
BibRef
Kim, S.[Sihyeon],
Ko, J.[Juyeon],
Joo, M.[Minseok],
Cha, J.[Juhan],
Lee, J.W.[Jae-Won],
Kim, H.W.J.[Hyun-Woo J.],
Semantic-Aware Implicit Template Learning via Part Deformation
Consistency,
ICCV23(593-603)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, S.W.[Shao-Wei],
Gupta, S.[Saurabh],
Wang, S.L.[Shen-Long],
Building Rearticulable Models for Arbitrary 3D Objects from 4D Point
Clouds,
CVPR23(21138-21147)
IEEE DOI
2309
BibRef
Liu, M.H.[Ming-Hua],
Zhu, Y.[Yinhao],
Cai, H.[Hong],
Han, S.Z.[Shi-Zhong],
Ling, Z.[Zhan],
Porikli, F.M.[Fatih M.],
Su, H.[Hao],
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via
Pretrained Image-Language Models,
CVPR23(21736-21746)
IEEE DOI
2309
BibRef
Saha, O.[Oindrila],
Cheng, Z.[Zezhou],
Maji, S.[Subhransu],
Improving Few-Shot Part Segmentation Using Coarse Supervision,
ECCV22(XXX:283-299).
Springer DOI
2211
BibRef
Liu, X.[Xueyi],
Xu, X.M.[Xiao-Meng],
Rao, A.[Anyi],
Gan, C.[Chuang],
Yi, L.[Li],
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part
Segmentation,
CVPR22(11614-11624)
IEEE DOI
2210
Training, Machine learning, Task analysis,
Faces, Segmentation, grouping and shape analysis, Vision + graphics
BibRef
Koo, J.[Juil],
Huang, I.[Ian],
Achlioptas, P.[Panos],
Guibas, L.J.[Leonidas J.],
Sung, M.[Minhyuk],
PartGlot:
Learning Shape Part Segmentation from Language Reference Games,
CVPR22(16484-16493)
IEEE DOI
2210
Geometry, Training, Shape, Annotations, Target recognition, Semantics,
Vision+language, Machine learning, Segmentation, grouping and shape analysis
BibRef
Naha, S.,
Xiao, Q.,
Banik, P.,
Reza, M.A.,
Crandall, D.J.,
Pose-Guided Knowledge Transfer for Object Part Segmentation,
VL3W20(3961-3955)
IEEE DOI
2008
Image segmentation, Visualization, Training, Cats, Horses, Semantics
BibRef
Ufer, N.[Nikolai],
Lui, K.T.[Kam To],
Schwarz, K.[Katja],
Warkentin, P.[Paul],
Ommer, B.[Björn],
Weakly Supervised Learning of Dense Semantic Correspondences and
Segmentation,
GCPR19(456-470).
Springer DOI
1911
BibRef
Molnar, J.,
Tasnadi, E.,
Kintses, B.,
Farkas, Z.,
Pal, C.,
Horvath, P.,
Danka, T.,
Active Surfaces for Selective Object Segmentation in 3D,
DICTA17(1-7)
IEEE DOI
1804
image segmentation, medical image processing,
advanced image processing methods, biomedical applications,
BibRef
Pho, K.,
Vu, H.,
Le, B.,
Adaptive cascade threshold learning from negative samples for
deformable part models,
ICIP17(1547-1551)
IEEE DOI
1803
Face, Learning systems, Object detection, Strain,
Training data, Deformable Part Models, cascade models, threshold learning
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.J.[Xiao-Jie],
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.Y.[Guo-Yan],
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 .