8.7.2.2 Tracking Deformable Shapes

Chapter Contents (Back)
Deformable Curves. Contour Matching.

Leymarie, F.F., and Levine, M.D.,
Tracking Deformable Objects in the Plane Using an Active Contour Model,
PAMI(15), No. 6, June 1993, pp. 617-634.
IEEE DOI Segment the object in the noisy image and track it through frames. Applied to MAT:
See also Simulating the Grassfire Transform Using an Active Contour Model. BibRef 9306

Leymarie, F.F.,
Tracking and Describing Deformable Objects Using Active Contour Models,
McGill UniversityTR-CIM-90-9, McGill University, February 1990.
WWW Link. 9002
BibRef

Leymarie, F.F., and Levine, M.D.,
Curvature Morphology,
McGill UniversityTR-CIM-88-26, McGill University, December 1988.
WWW Link. 8812
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Pei, S.C.[Soo-Chang], Liou, L.G.[Lin-Gwo],
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Pei, S.C.[Soo-Chang], Liou, L.G.[Lin-Gwo],
Tracking a Planar Patch in Three-Dimensional Space by Affine Transformation in Monocular and Binocular Vision,
PR(26), No. 1, January 1993, pp. 23-31.
Elsevier DOI BibRef 9301

Pei, S.C.[Soo-Chang], Liou, L.G.[Lin-Gwo],
Finding the Motion, Position and Orientation of a Planar Patch in 3D Space from Scaled-Orthographic Projection,
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Pei, S.C.[Soo-Chang], Liou, L.G.[Lin-Gwo],
What Can Be Seen in a Noisy Optical-Flow Field Projected by a Moving Planar Patch in 3D Space,
PR(30), No. 9, September 1997, pp. 1401-1413.
Elsevier DOI 9708
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Jain, A.K., Zhong, Y., Lakshmanan, S.,
Object Matching Using Deformable Templates,
PAMI(18), No. 3, March 1996, pp. 267-278.
IEEE DOI Deformable Template. Object prototype gives prior knowledge of the shape. Using edges, find a match with the image with coarse-to-fine matching. BibRef 9603

Zhong, Y.[Yu], Jain, A.K.[Anil K.], and Dubuisson-Jolly, M.P.[Marie-Pierre],
Object Tracking Using Deformable Templates,
PAMI(22), No. 5, May 2000, pp. 544-549.
IEEE DOI 0008
BibRef
Earlier: ICCV98(440-445).
IEEE DOI Combine frame-to-frame changes and model to image matches. BibRef

Dubuisson-Jolly, M.P.[Marie-Pierre], Gupta, A.[Alok],
Tracking Deformable Templates Using a Shortest Path Algorithm,
CVIU(81), No. 1, January 2001, pp. 26-45.
DOI Link 0102
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Dubuisson-Jolly, M.P.[Marie-Pierre], Liang, C.C.[Cheng-Chung], and Gupta, A.[Alok],
Optimal Polyline Tracking for Artery Motion Compensation in Coronary Angiography,
ICCV98(414-419).
IEEE DOI BibRef 9800

Marques, J.S.[Jorge S.], Lemos, J.M.[Joao M.],
Optimal and suboptimal shape tracking based on multiple switched dynamic models,
IVC(19), No. 8, May 2001, pp. 539-550.
Elsevier DOI 0106
BibRef
Earlier:
Shape Tracking Based on Switched Dynamical Models,
ICIP99(II:954-958).
IEEE DOI BibRef

Cheikh, F.A.[Faouzi Alaya], Cramariuc, B.[Bogdan], Partio, M.[Mari], Reijonen, P.[Pasi], Gabbouj, M.[Moncef],
Ordinal-Measure Based Shape Correspondence,
JASP(2002), No. 4, 2002, pp. 362-371.
WWW Link. 0204
BibRef

Mech, R.[Roland], Marqués, F.[Ferran],
Objective Evaluation Criteria for 2D-Shape Estimation Results of Moving Objects,
JASP(2002), No. 4, 2002, pp. 401-409.
WWW Link. 0204
BibRef

Nascimento, J.C., Marques, J.S.,
Improving the robustness of parametric shape tracking with switched multiple models,
PR(35), No. 12, December 2002, pp. 2711-2718.
Elsevier DOI 0209

See also Trajectory Classification Using Switched Dynamical Hidden Markov Models. BibRef

Nascimento, J.C., Marques, J.S.,
An Adaptive Potential for Robust Shape Estimation,
IVC(21), No. 12-13, December 2003, pp. 1107-1116.
Elsevier DOI 0401
BibRef
Earlier: BMVC01(Session 4: Segmentation).
HTML Version. BibRef
Earlier:
Robust Shape Tracking in the Presence of Cluttered Background,
ICIP00(Vol III: 82-85).
IEEE DOI 0008
Instituto Superior Técnico 0110
Based on strokes detected in the image. BibRef

Santiago, C.[Carlos], Marques, J.S.[Jorge S.],
Robust Shape Tracking With Multiple Models in Ultrasound Images,
IP(17), No. 3, March 2008, pp. 392-406.
IEEE DOI 0802
BibRef

Santiago, C.[Carlos], Nascimento, J.C.[Jacinto C.], Marques, J.S.[Jorge S.],
Combining an Active Shape and Motion Models for Object Segmentation in Image Sequences,
ICIP18(3703-3707)
IEEE DOI 1809
BibRef
Earlier:
A robust active shape model using an expectation-maximization framework,
ICIP14(6076-6080)
IEEE DOI 1502
BibRef
Earlier:
Non-rigid Object Segmentation Using Robust Active Shape Models,
AMDO14(160-169).
Springer DOI 1407
Motion segmentation, Image segmentation, Shape, Trajectory, Deformable models, Active shape model, Mathematical model, Vector Field. BibRef

Nascimento, J.C., Marques, J.S.,
Adaptive Snakes Using the EM Algorithm,
IP(14), No. 11, November 2005, pp. 1678-1686.
IEEE DOI 0510
BibRef
And: Errata: IP(15), No. 3, March 2006, pp. 788-788.
IEEE DOI 0604
BibRef

Medley, D.O.[Daniela O.], Santiago, C.[Carlos], Nascimento, J.C.[Jacinto C.],
Deep Active Shape Model for Robust Object Fitting,
IP(29), 2020, pp. 2380-2394.
IEEE DOI 2001
BibRef
Earlier:
Robust Feature Descriptors for Object Segmentation Using Active Shape Models,
ACIVS18(163-174).
Springer DOI 1810
Feature extraction, Shape, Probabilistic logic, Active appearance model, Image segmentation, active shape model BibRef

Medley, D.O.[Daniela O.], Santiago, C.[Carlos], Nascimento, J.C.[Jacinto C.],
CyCoSeg: A Cyclic Collaborative Framework for Automated Medical Image Segmentation,
PAMI(44), No. 11, November 2022, pp. 8167-8182.
IEEE DOI 2210
Image segmentation, Collaboration, Shape, Deformable models, Semantics, Standards, Segmentation, semantic networks, machine learning BibRef

Nascimento, J.C.[Jacinto C.], Carneiro, G.[Gustavo],
Deep Learning on Sparse Manifolds for Faster Object Segmentation,
IP(26), No. 10, October 2017, pp. 4978-4990.
IEEE DOI 1708
BibRef
Earlier:
Towards reduction of the training and search running time complexities for non-rigid object segmentation,
ICIP15(4713-4717)
IEEE DOI 1512

See also Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking, The. BibRef
Earlier:
Non-rigid Segmentation Using Sparse Low Dimensional Manifolds and Deep Belief Networks,
CVPR14(288-295)
IEEE DOI 1409
BibRef
Earlier:
Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds,
CVPR13(1963-1970)
IEEE DOI 1309
Image segmentation, Manifolds, Robustness, Search problems, Shape, Training, Visualization, Deep belief netwolks, defonnable objects, non-rigid segmentation. Sparse manifolds. Non-rigid top-down segmentation; deep belief network; manifold learning
See also Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data. BibRef

Nguyen, H.T.[Hieu Tat], Worring, M.[Marcel], van den Boomgaard, R., Smeulders, A.W.M.,
Tracking nonparameterized object contours in video,
IP(11), No. 9, September 2002, pp. 1081-1091.
IEEE DOI 0210

See also Watersnakes: Energy-Driven Watershed Segmentation. BibRef

Nguyen, H.T.[Hieu T.], Worring, M.[Marcel],
Multifeature Object Tracking using a Model-Free Approach,
CVPR00(I: 145-150).
IEEE DOI 0005
BibRef

Nguyen, H.T.[Hieu T.], Worring, M.[Marcel], van den Boomgaard, R.[Rein],
Occlusion Robust Adaptive Template Tracking,
ICCV01(I: 678-683).
IEEE DOI 0106
BibRef

Li, P.H.[Pei-Hua], Zhang, T.W.[Tian-Wen], Pece, A.E.C.[Arthur E.C.],
Visual contour tracking based on particle filters,
IVC(21), No. 1, January 2003, pp. 111-123.
Elsevier DOI 0301
BibRef

Li, P.H.[Pei-Hua], Zhang, T.W.[Tian-Wen], Ma, B.[Bo],
Unscented Kalman filter for visual curve tracking,
IVC(22), No. 2, 1 February 2004, pp. 157-164.
Elsevier DOI 0402
For each step, make multiple measurements on appropriately chosen sample points, thus obtaining the best observation according to the measurement density. BibRef

Li, P.H.[Pei-Hua], Zhang, T.W.[Tian-Wen],
Visual contour tracking based on sequential importance sampling/resampling algorithm,
ICPR02(II: 564-568).
IEEE DOI 0211
BibRef

Li, P.H.[Pei-Hua],
Tensor-SIFT Based Earth Mover's Distance for Contour Tracking,
JMIV(46), No. 1, May 2013, pp. 44-65.
WWW Link. 1303
BibRef

Pece, A.E.C.[Arthur E.C.], Worrall, A.D.[Anthony D.],
A comparison between feature-based and EM-based contour tracking,
IVC(24), No. 11, 1 November 2006, pp. 1218-1232.
Elsevier DOI 0610
BibRef
Earlier:
Tracking with the EM Contour Algorithm,
ECCV02(I: 3 ff.).
Springer DOI 0205
Active contour for pose refinement and tracking. Generative model; EM algorithm; Kalman filter; Empirical information matrix BibRef

Pece, A.E.C.[Arthur E.C.],
Contour tracking based on marginalized likelihood ratios,
IVC(24), No. 3, 1 March 2006, pp. 301-317.
Elsevier DOI Generative model; Active contour; Particle filter; EM algorithm; Kalman filter 0604
BibRef
Earlier:
A Comparison of Active-Contour Models Based on Blurring and on Marginalization,
PETS05(333-340).
IEEE DOI 0602
BibRef
Earlier:
Tracking of Non-Gaussian Clusters in the PETS2001 Image Sequences,
PETS01(xx-yy). 0110
BibRef

Nava, F.P.[Fernando Pérez], Martel, A.F.[Antonio Falcón],
Wavelet modeling of contour deformations in Sobolev spaces for fitting and tracking applications,
PR(36), No. 5, May 2003, pp. 1119-1130.
Elsevier DOI 0301
Fitting in bayesian terms. BibRef

Zimmer, C., Labruyere, E., Meas-Yedid, V.[Vannary], Guillen-Aghion, N., Olivo-Marin, J.C.[Jean-Christophe],
Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing,
MedImg(21), No. 10, October 2002, pp. 1212-1221.
IEEE Top Reference. 0301
BibRef
And:
Improving active contours for segmentation and tracking of motile cells in videomicroscopy,
ICPR02(II: 286-289).
IEEE DOI 0211
BibRef

Olivo-Marin, J.C.[Jean-Christophe], Meas-Yedid, V.[Vannary],
Improving histology images segmentation through spatial constraints and supervision,
ICIP10(3633-3636).
IEEE DOI 1009
BibRef

Meas-Yedid, V.[Vannary], Olivo-Marin, J.C.[Jean-Christophe],
Active Contours for the Movement and Motility Analysis of Biological Objects,
ICIP00(Vol I: 196-199).
IEEE DOI 0008
BibRef

Nguyn Ngoc, S., Briquet-Laugier, F., Boulin, C., and Olivo-Marin, J.C.,
Adaptive Detection for Tracking Moving Biological Objects in Video Microscopy Sequences,
ICIP97(III: 484-487).
IEEE DOI BibRef 9700

Obando, D.F.G.[Daniel Felipe González], Olivo-Marin, J.C.[Jean-Christophe], Wendling, L.[Laurent], Meas-Yedid, V.[Vannary],
Vector-Based Morphological Operations on Polygons Using Straight Skeletons for Digital Pathology,
DGCI19(249-261).
Springer DOI 1905
BibRef

Zimmer, C., Olivo-Marin, J.C.,
Coupled Parametric Active Contours,
PAMI(27), No. 11, November 2005, pp. 1838-1842.
IEEE DOI 0510
Deal with occasional touching, contours do not merge. BibRef

Pecreaux, J., Zimmer, C., Olivo-Marin, J.C.,
Biophysical Active Contours for Cell Tracking I: Tension and Bending,
ICIP06(1949-1952).
IEEE DOI 0610
BibRef

de Chaumont, F., Dufour, A., Olivo-Marin, J.C.,
Tracking articulated objects with physics engines,
ICIP09(885-888).
IEEE DOI 0911
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Dufour, A., Shinin, V., Tajbakhsh, S., Guillen-Aghion, N., Olivo-Marin, J.C., Zimmer, C.,
Segmenting and Tracking Fluorescent Cells in Dynamic 3-D Microscopy With Coupled Active Surfaces,
IP(14), No. 9, September 2005, pp. 1396-1410.
IEEE DOI 0508
BibRef

Chenouard, N.[Nicolas], Vernhettes, S.[Samantha], Bloch, I.[Isabelle], Olivo-Marin, J.C.[Jean-Christophe],
Morphological source separation for particle tracking in complex biological environments,
ICPR08(1-4).
IEEE DOI 0812
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Chenouard, N.[Nicolas], Bloch, I.[Isabelle], Olivo-Marin, J.C.[Jean-Christophe],
Multiple Hypothesis Tracking for Cluttered Biological Image Sequences,
PAMI(35), No. 11, 2013, pp. 2736-3750.
IEEE DOI 1309
BibRef
Earlier:
Multiple hypothesis tracking in cluttered condition,
ICIP09(3621-3624).
IEEE DOI 0911
Feature Tracking. BibRef
And:
Particle tracking in fluorescent microscopy images improved by morphological source separation,
ICIP09(821-824).
IEEE DOI 0911
BibRef
Earlier:
Feature-aided particle tracking,
ICIP08(1796-1799).
IEEE DOI 0810
Particle tracking BibRef

Koschan, A.F.[Andreas F.], Kang, S.K.[Sang-Kyu], Paik, J.K.[Joon-Ki], Abidi, B.R.[Besma R.], Abidi, M.A.[Mongi A.],
Color active shape models for tracking non-rigid objects,
PRL(24), No. 11, July 2003, pp. 1751-1765.
Elsevier DOI 0304
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Kang, S.K.[Sang-Kyu], Koschan, A.F.[Andreas F.], Zhang, H.S., Paik, J.K.[Joon-Ki], Abidi, B.R.[Besma R.], Abidi, M.A.[Mongi A.],
Hierarchical approach to enhanced active shape model for color video tracking,
ICIP02(I: 888-891).
IEEE DOI 0210
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Feghali, R., Mitiche, A.,
Spatiotemporal Motion Boundary Detection and Motion Boundary Velocity Estimation for Tracking Moving Objects With a Moving Camera: A Level Sets PDEs Approach With Concurrent Camera Motion Compensation,
IP(13), No. 11, November 2004, pp. 1473-1490.
IEEE DOI 0411
BibRef
Earlier:
Tracking with simultaneous camera motion substraction by level set spatio-temporal surface evolution,
ICIP03(III: 929-932).
IEEE DOI 0312
BibRef

Mitiche, A., Feghali, R., Mansouri, A.,
Tracking moving objects as spatio-temporal boundary detection,
Southwest02(106-110).
IEEE Top Reference. 0208
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Chung, D.[Desmond], MacLean, W.J.[W. James], Dickinson, S.J.[Sven J.],
Integrating region and boundary information for spatially coherent object tracking,
IVC(24), No. 7, July 2006, pp. 680-692.
Elsevier DOI
PDF File. 0608
BibRef
Integrating Region and Boundary Information for Improved Spatial Coherence in Object Tracking,
Non-Rigid04(3).
IEEE DOI 0502
Motion estimation; Boundary recovery; Parametric models; Motion layers BibRef

Tsechpenakis, G., Rapantzikos, K., Tsapatsoulis, N., Kollias, S.D.,
A snake model for object tracking in natural sequences,
SP:IC(19), No. 3, March 2004, pp. 219-238.
Elsevier DOI 0402
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Rapantzikos, K.[Konstantinos], Tsapatsoulis, N.[Nicolas], Avrithis, Y.S.[Yannis S.], Kollias, S.D.[Stefanos D.],
Bottom-up spatiotemporal visual attention model for video analysis,
IET-IPR(1), No. 2, June 2007, pp. 237-248.
DOI Link 0905
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Rapantzikos, K.[Konstantinos], Tsapatsoulis, N.[Nicolas], Avrithis, Y.S.[Yannis S.], Kollias, S.D.[Stefanos D.],
Spatiotemporal saliency for video classification,
SP:IC(24), No. 7, August 2009, pp. 557-571.
Elsevier DOI 0909
Spatiotemporal visual saliency; Video classification BibRef

Rapantzikos, K.[Konstantinos], Avrithis, Y.S.[Yannis S.], Kollias, S.D.[Stefanos D.],
Dense saliency-based spatiotemporal feature points for action recognition,
CVPR09(1454-1461).
IEEE DOI 0906
BibRef
Earlier:
Spatiotemporal saliency for event detection and representation in the 3D wavelet domain: potential in human action recognition,
CIVR07(294-301).
DOI Link 0707
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Varytimidis, C.[Christos], Rapantzikos, K.[Konstantinos], Avrithis, Y.S.[Yannis S.], Kollias, S.D.[Stefanos D.],
a-shapes for local feature detection,
PR(50), No. 1, 2016, pp. 56-73.
Elsevier DOI 1512
Local features BibRef

Tolias, G.[Giorgos], Avrithis, Y.S.[Yannis S.],
Speeded-up, relaxed spatial matching,
ICCV11(1653-1660).
IEEE DOI 1201
Determine the most appropriate matching model. BibRef

Tsechpenakis, G.[Gabriel], Tsapatsoulis, N.[Nicolas], Kollias, S.D.[Stefanos D.],
Probabilistic Boundary-based Contour Tracking With Snakes In Natural Cluttered Video Sequences,
IJIG(4), No. 3, July 2004, pp. 469-498. 0407
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Kapsalas, P., Kollias, S.D.,
Affine morphological shape stable boundary regions (SSBR) for image representation,
ICIP11(3381-3384).
IEEE DOI 1201
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Tsechpenakis, G.[Gavriil], Chatzis, S.P.[Sotirios P.],
Deformable probability maps: Probabilistic shape and appearance-based object segmentation,
CVIU(115), No. 8, August 2011, pp. 1157-1169.
Elsevier DOI 1101
Segmentation; Deformable models; Graphical models BibRef

Ristivojevic, M., Konrad, J.[Janusz],
Space-Time Image Sequence Analysis: Object Tunnels and Occlusion Volumes,
IP(15), No. 2, February 2006, pp. 364-376.
IEEE DOI 0602
BibRef
Earlier: A2, A1:
Joint space-time image sequence segmentation based on volume competition and level sets,
ICIP02(I: 573-576).
IEEE DOI 0210
Find the volumn carved by the object through time. BibRef

Hsu, C.T.[Chiou-Ting], Hsieh, M.S.[Ming-Shen],
Region tracking for non-rigid video objects in a non-parametric MAP framework,
SP:IC(21), No. 3, March 2006, pp. 235-251.
Elsevier DOI 0604
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Earlier:
Segmentation of non-rigid object in a non-parametric MAP framework,
ICIP03(I: 997-1000).
IEEE DOI 0312
Map model; Non-parametric density estimation; Non-rigid motion; Curve evolution BibRef

Xiong, G.L.[Guang-Lei], Feng, C.[Chao], Ji, L.[Liang],
Dynamical Gaussian mixture model for tracking elliptical living objects,
PRL(27), No. 7, May 2006, pp. 838-842.
Elsevier DOI Gaussian mixture model; EM algorithm; Kalman filtering; Tracking 0604
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Wilson, C.A., Theriot, J.A.,
A Correlation-Based Approach to Calculate Rotation and Translation of Moving Cells,
IP(15), No. 7, July 2006, pp. 1939-1951.
IEEE DOI 0606
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Moreno-Noguer, F.[Francesc], Sanfeliu, A.[Alberto], Samaras, D.[Dimitris],
Integration of deformable contours and a multiple hypotheses Fisher color model for robust tracking in varying illuminant environments,
IVC(25), No. 3, March 2007, pp. 285-296.
Elsevier DOI 0701
Tracking; Deformable contours; Color adaption; Particle filters
See also Dependent Multiple Cue Integration for Robust Tracking.
See also Integration of Conditionally Dependent Object Features for Robust Figure/Background Segmentation. BibRef

Sum, K.W., Cheung, P.Y.S.[Paul Y.S.],
Boundary vector field for parametric active contours,
PR(40), No. 6, June 2007, pp. 1635-1645.
Elsevier DOI 0704
Parametric active contours; Snakes; External forces; Gradient vector flow; Boundary vector field; Capture range; Concave object extraction; Image segmentation; Medical image analysis BibRef

Jiang, H.[Hao], Drew, M.S.[Mark S.],
Shadow resistant tracking using inertia constraints,
PR(40), No. 7, July 2007, pp. 1929-1945.
Elsevier DOI 0704
BibRef
Earlier:
A predictive contour inertia snake model for general video tracking,
ICIP02(III: 413-416).
IEEE DOI 0210
Shadows; Tracking; Snakes; Active contours; Variational; Illumination invariance; Inertia BibRef

Allili, M.S.[Mohand Said], Ziou, D.[Djemel],
Globally adaptive region information for automatic color-texture image segmentation,
PRL(28), No. 15, 1 November 2007, pp. 1946-1956.
Elsevier DOI 0711
BibRef
Earlier:
Automatic Color-Texture Image Segmentation by Using Active Contours,
IWICPAS06(495-504).
Springer DOI 0608
BibRef
Earlier:
An Automatic Segmentation of Color Images by Using a Combination of Mixture Modelling and Adaptive Region Information: A Level Set Approach,
ICIP05(I: 305-308).
IEEE DOI 0512
Color; Texture; Polarity; Level sets; Automatic segmentation BibRef

Allili, M.S.[Mohand Said],
Wavelet Modeling Using Finite Mixtures of Generalized Gaussian Distributions: Application to Texture Discrimination and Retrieval,
IP(21), No. 4, April 2012, pp. 1452-1464.
IEEE DOI 1204
BibRef
Earlier:
Wavelet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions,
ICPR10(3143-3146).
IEEE DOI 1008
BibRef

Allili, M.S.[Mohand Saïd], Baaziz, N.[Nadia], Mejri, M.,
Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications,
MultMed(16), No. 3, April 2014, pp. 772-784.
IEEE DOI 1405
Gaussian distribution BibRef

Yapi, D.[Daniel], Allili, M.S.[Mohand Said],
Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions,
ICPR22(464-469)
IEEE DOI 2212
Wavelet transforms, Correlation, Heavily-tailed distribution, Graphical models, Image color analysis, Image retrieval, Layout, color-texture retrieval BibRef

Allili, M.S.[Mohand Saïd], Baaziz, N.[Nadia],
Contourlet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions,
CAIP11(II: 446-454).
Springer DOI 1109
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Nouboukpo, A.[Adama], Allili, M.S.[Mohand Said],
Spatially-Coherent Segmentation Using Hierarchical Gaussian Mixture Reduction Based on Cauchy-Schwarz Divergence,
ICIAR19(I:388-396).
Springer DOI 1909
BibRef

Boulmerka, A.[Aissa], Allili, M.S.[Mohand Said],
Thresholding-Based Segmentation Revisited Using Mixtures of Generalized Gaussian Distributions,
ICPR12(2894-2897).
WWW Link. 1302

See also Finite Generalized Gaussian Mixture Modeling and Applications to Image and Video Foreground Segmentation. BibRef

Allili, M.S.[Mohand Saïd],
Effective object tracking by matching object and background models using active contours,
ICIP09(873-876).
IEEE DOI 0911
BibRef
And:
Object Contour Tracking Using Foreground and Background Distribution Matching,
CIARP09(954-961).
Springer DOI 0911
BibRef

Allili, M.S.[Mohand Saïd], Ziou, D.[Djemel],
Active contours for video object tracking using region, boundary and shape information,
SIViP(1), No. 2, June 2007, pp. 101-117.
Springer DOI 0707
BibRef
Earlier:
Adaptive Appearance Model for Object Contour Tracking in Videos,
CRV07(510-518).
IEEE DOI 0705
BibRef
Earlier:
A Robust Video Object Tracking by Using Active Contours,
OTCBVS06(135).
IEEE DOI 0609
BibRef
And:
Object Contour Tracking in Videos by Matching Finite Mixture Models,
AVSBS06(35-35).
IEEE DOI 0611
BibRef
Earlier:
An Automatic Segmentation Combining Mixture Analysis and Adaptive Region Information: A Level Set Approach,
CRV05(73-80).
IEEE DOI 0505

See also Hybrid SEM Algorithm for High-Dimensional Unsupervised Learning Using a Finite Generalized Dirichlet Mixture, A. BibRef

Allili, M.S.[Mohand Said], Ziou, D.[Djemel],
An approach for dynamic combination of region and boundary information in segmentation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Allili, M.S.[Mohand Said], Ziou, D.[Djemel],
Object of Interest segmentation and Tracking by Using Feature Selection and Active Contours,
OTCBVS07(1-8).
IEEE DOI 0706
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And:
Using Feature Selection For Object Segmentation and Tracking,
CRV07(191-200).
IEEE DOI 0705
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Seise, M.[Matthias], McKenna, S.J.[Stephen J.], Ricketts, I.W.[Ian W.], Wigderowitz, C.A.[Carlos A.],
Learning Active Shape Models for Bifurcating Contours,
MedImg(26), No. 5, May 2007, pp. 666-677.
IEEE DOI 0705
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Earlier:
Double Contour Active Shape Models,
BMVC05(xx-yy).
HTML Version. 0509
Segmentation of tibial and femoral contours in knee X-ray images. BibRef

Seise, M.[Matthias], McKenna, S.J.[Stephen J.], Ricketts, I.W.[Ian W.], Wigderowitz, C.A.[Carlos A.],
Parts-based segmentation with overlapping part models using Markov chain Monte Carlo,
IVC(27), No. 5, 2 April 2009, pp. 504-513.
Elsevier DOI 0904
BibRef
Earlier:
Segmenting Multiple Objects with Overlapping Appearance and Uncertainty,
BMVC06(II:839).
PDF File. 0609
Probabilistic segmentation; Model-based segmentation; Markov chain Monte Carlo BibRef

McAllister, G., McKenna, S.J., Ricketts, I.W.,
MLESAC tracking with 2D revolute-prismatic articulated models,
ICPR02(II: 725-728).
IEEE DOI 0211
BibRef

Schreiber, D.[David],
Robust template tracking with drift correction,
PRL(28), No. 12, 1 September 2007, pp. 1483-1491.
Elsevier DOI 0707
Template tracking; The Lucas-Kanade algorithm; Robust least squares BibRef

Schreiber, D.[David],
Generalizing the Lucas-Kanade algorithm for histogram-based tracking,
PRL(29), No. 7, 1 May 2008, pp. 852-861.
Elsevier DOI 0804
Template tracking; The Lucas-Kanade algorithm; Histogram-based tracking; Kernel-based tracking; Robust least squares; Pedestrian tracking
See also Iterative Image Registration Technique with an Application to Stereo Vision, An. BibRef

Schreiber, D.[David],
Incorporating symmetry into the Lucas-Kanade framework,
PRL(30), No. 7, 1 May 2009, pp. 690-698.
Elsevier DOI 0904
Template tracking; Region alignment; The Lucas-Kanade algorithm; Bilateral symmetry; Symmetry detection; Symmetry tracking BibRef

Shekhovtsov, A.[Alexander], Kovtun, I.[Ivan], Hlavac, V.[Vaclav],
Efficient MRF Deformation Model for Non-Rigid Image Matching,
CVIU(112), No. 1, October 2008, pp. 91-99.
Elsevier DOI 0810
BibRef
Earlier: CVPR07(1-6).
IEEE DOI 0706
Markov random fields; MRF; Message passing; TRW-S; Energy minimization; Motion estimation; Optical flow; Image registration BibRef

Zaki, M., Youssef, M.,
TNRAC: a system for tracking multiple moving non-rigid objects using an active camera,
SIViP(3), No. 2, June 2009, pp. xx-yy.
Springer DOI 0903
active contour models. Block matching, reject bad feature points. BibRef

Garcia Trigo, P.[Pablo], Johan, H.[Henry], Imagire, T.[Takashi], Nishita, T.[Tomoyuki],
Interactive Region Matching for 2D Animation Coloring Based on Feature's Variation,
IEICE(E92-D), No. 6, June 2009, pp. 1289-1295.
WWW Link. 0907
Store the region and match regions through frames. BibRef

Chung, C.Y.[Chih-Yuan], Chen, H.H.,
Video Object Extraction via MRF-Based Contour Tracking,
CirSysVideo(20), No. 1, January 2010, pp. 149-155.
IEEE DOI 1002
BibRef

Dimitrios, A.[Arabadjis], Rousopoulos, P.[Panayiotis], Papaodysseus, C.[Constantin], Panagopoulos, M.[Mihalis], Loumou, P.[Panayiota], Theodoropoulos, G.[Georgios],
A General Methodology for the Determination of 2D Bodies Elastic Deformation Invariants: Application to the Automatic Identification of Parasites,
PAMI(32), No. 5, May 2010, pp. 799-814.
IEEE DOI 1003
Use 2D image and deformation characteristics to get undeformed shape.
See also Identification of Geometrical Shapes in Paintings and its Application to Demonstrate the Foundations of Geometry in 1650 B.C.. BibRef

Goldin, I.[Ishay], Delosme, J.M.[Jean-Marc], Bruckstein, A.M.[Alfred M.],
Vesicles and Amoebae: On Globally Constrained Shape Deformation,
JMIV(37), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
Vesicles and amoebae: Globally constrained shape evolutions,
NORDIA08(1-8).
IEEE DOI 0806
BibRef

Mai, F., Chang, C.Q., Hung, Y.S.,
A subspace approach for matching 2D shapes under affine distortions,
PR(44), No. 2, February 2011, pp. 210-221.
Elsevier DOI 1011
BibRef
And:
Affine-invariant shape matching and recognition under partial occlusion,
ICIP10(4605-4608).
IEEE DOI 1009
Shape registration; Affine transformation; Subspace; 2D shape BibRef

Michel, D.[Damien], Oikonomidis, I.[Iasonas], Argyros, A.A.[Antonis A.],
Scale invariant and deformation tolerant partial shape matching,
IVC(29), No. 7, June 2011, pp. 459-469.
Elsevier DOI 1101
BibRef
Earlier: A2, A3, Only:
Deformable 2D Shape Matching Based on Shape Contexts and Dynamic Programming,
ISVC09(II: 460-469).
Springer DOI 0911
Partial shape matching; 2D shape descriptors; Dynamic programming BibRef

Chiverton, J.[John], Xie, X.H.[Xiang-Hua], Mirmehdi, M.[Majid],
Automatic Bootstrapping and Tracking of Object Contours,
IP(21), No. 3, March 2012, pp. 1231-1245.
IEEE DOI 1203
BibRef
Earlier: A1, A3, A2:
On-line Learning of Shape Information for Object Segmentation and Tracking,
BMVC09(xx-yy).
PDF File. 0909
BibRef
Earlier: A1, A3, A2:
Variational Maximum A Posteriori model similarity and dissimilarity matching,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And: A1, A2, A3:
Tracking with Active Contours Using Dynamically Updated Shape Information,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Zhang, K.H.[Kai-Hua], Song, H.H.[Hui-Hui],
Real-Time Visual Tracking Via Online Weighted Multiple Instance Learning,
PR(46), No. 1, January 2013, pp. 397-411.
Elsevier DOI 1209
Visual tracking; Multiple instance learning; Tracking by detection; Sliding window
See also Active Contours with Selective Local or Global Segmentation: A New Formulation and Level Set Method.
See also Real-Time Object Tracking Via Online Discriminative Feature Selection. BibRef

Staneva, V., Younes, L.,
Modeling and Estimation of Shape Deformation for Topology-Preserving Object Tracking,
SIIMS(7), No. 1, 2014, pp. 427-455.
DOI Link 1404
BibRef

Staneva, V., Younes, L.,
Learning Shape Trends: Parameter Estimation in Diffusions on Shape Manifolds,
Diff-CVML17(717-725)
IEEE DOI 1709
Conferences, Diffusion processes, Estimation, Manifolds, Mathematical model, Shape BibRef

Sun, X.[Xin], Yao, H.X.[Hong-Xun],
A refined particle filter based on determined level set model for robust contour tracking,
MVA(25), No. 7, October 2014, pp. 1727-1736.
WWW Link. 1410
BibRef

Sun, X.[Xin], Yao, H.X.[Hong-Xun], Zhang, S.P.[Sheng-Ping], Sun, M.[Mingui],
Non-rigid object tracking by adaptive data-driven kernel,
ICIP13(2958-2962)
IEEE DOI 1402
BibRef
Earlier: A1, A2, A3, Only:
A novel supervised level set method for non-rigid object tracking,
CVPR11(3393-3400).
IEEE DOI 1106
Object tracking;active contour;adaptive kernel;mean shift BibRef

Sun, X.[Xin], Yao, H.X.[Hong-Xun], Zhang, S.P.[Sheng-Ping], Li, D.[Dong],
Non-Rigid Object Contour Tracking via a Novel Supervised Level Set Model,
IP(24), No. 11, November 2015, pp. 3386-3399.
IEEE DOI 1509
edge detection BibRef

Sun, X.[Xin], Yao, H.X.[Hong-Xun], Zhang, S.P.[Sheng-Ping],
Contour tracking via on-line discriminative appearance modeling based level sets,
ICIP11(2317-2320).
IEEE DOI 1201
BibRef

Moreno-Noguer, F.[Francesc], Porta, J.M.[Josep M.],
A Bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images,
IVC(52), No. 1, 2016, pp. 141-153.
Elsevier DOI 1609
BibRef
Earlier:
Probabilistic simultaneous pose and non-rigid shape recovery,
CVPR11(1289-1296).
IEEE DOI 1106
Deformable surfaces BibRef

Agudo, A.[Antonio], Moreno-Noguer, F.[Francesc],
Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion,
IJCV(122), No. 2, April 2017, pp. 371-387.
Springer DOI 1704
BibRef
And:
Global Model with Local Interpretation for Dynamic Shape Reconstruction,
WACV17(264-272)
IEEE DOI 1609
BibRef
Earlier:
Learning Shape, Motion and Elastic Models in Force Space,
ICCV15(756-764)
IEEE DOI 1602
BibRef
And:
Simultaneous pose and non-rigid shape with particle dynamics,
CVPR15(2179-2187)
IEEE DOI 1510
Computational modeling, Deformable models, Modal analysis, Shape, Standards, Surface reconstruction,
See also Sequential Non-Rigid Structure from Motion Using Physical Priors.
See also scalable, efficient, and accurate solution to non-rigid structure from motion, A. BibRef

Sanchez-Riera, J.[Jordi], Ostlund, J.[Jonas], Fua, P.[Pascal], Moreno-Noguer, F.[Francesc],
Simultaneous pose, correspondence and non-rigid shape,
CVPR10(1189-1196).
IEEE DOI 1006
BibRef

Wang, L.[Long], Yan, S.[Shen], Zhen, J.A.[Jian-An], Liu, Y.[Yu], Zhang, M.[Maojun], Zhang, G.F.[Guo-Feng], Zhou, X.W.[Xiao-Wei],
Deep Active Contours for Real-time 6-DoF Object Tracking,
ICCV23(13988-13998)
IEEE DOI Code:
WWW Link. 2401
BibRef

Veeravasarapu, V., Goel, A., Mittal, D., Singh, M.,
ProAlignNet: Unsupervised Learning for Progressively Aligning Noisy Contours,
CVPR20(9668-9676)
IEEE DOI 2008
Shape, Transforms, Loss measurement, Noise measurement, Feature extraction, Training BibRef