18.3.4.7.2 Background Models, Textured Surfaces, Regions

Chapter Contents (Back)
Background. Background Model. Motion, Detection. See also Moving Object Extraction, Using Models or Analysis of Regions. See also Background Detection, Background Model.

Dumitras, A., Haskell, B.G.,
An Encoder-Decoder Texture Replacement Method With Application to Content-Based Movie Coding,
CirSysVideo(14), No. 6, June 2004, pp. 825-840.
IEEE Abstract. 0407
BibRef
Earlier:
A background modeling method by texture replacement and mapping with application to content-based movie coding,
ICIP02(I: 65-68).
IEEE DOI 0210
BibRef

Heikkilä, M.[Marko], Pietikäinen, M.[Matti],
A Texture-Based Method for Modeling the Background and Detecting Moving Objects,
PAMI(28), No. 4, April 2006, pp. 657-662.
IEEE DOI 0604
BibRef

Heikkila, M., Pietikainen, M., Heikkila, J.,
A Texture-based Method for Detecting Moving Objects,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Allili, M.S.[Mohand Said], Bouguila, N.[Nizar], Ziou, D.[Djemel],
Finite Generalized Gaussian Mixture Modeling and Applications to Image and Video Foreground Segmentation,
JEI(17), 2008, pp. 013005. BibRef 0800
Earlier: CRV07(183-190).
IEEE DOI 0705
BibRef
And:
A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling,
CRV07(503-509).
IEEE DOI 0705
See also Hybrid SEM Algorithm for High-Dimensional Unsupervised Learning Using a Finite Generalized Dirichlet Mixture, A. See also Thresholding-Based Segmentation Revisited Using Mixtures of Generalized Gaussian Distributions. BibRef

Boulmerka, A.[Aissa], Allili, M.S.[Mohand Sald],
Background Modeling in Videos Revisited Using Finite Mixtures of Generalized Gaussians and Spatial Information,
ICIP15(3660-3664)
IEEE DOI 1512
Background subtraction BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection,
MVA(25), No. 5, July 2014, pp. 1145-1162.
WWW Link. 1407
BibRef
Earlier:
Infinite Generalized Gaussian Mixture Modeling and Applications,
ICIAR11(I: 201-210).
Springer DOI 1106
See also Finite asymmetric generalized Gaussian mixture models learning for infrared object detection. See also Semantic Scene Classification with Generalized Gaussian Mixture Models. BibRef

Zhang, B.C.[Bao-Chang], Gao, Y.S.[Yong-Sheng], Zhao, S., Zhong, B.N.[Bi-Neng],
Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background,
CirSysVideo(21), No. 1, January 2011, pp. 29-38.
IEEE DOI 1103
BibRef
Earlier: A1, A2, A4, Only:
Complex background modeling and motion detection based on Texture Pattern Flow,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, B.C.[Bao-Chang], Zhong, B.N.[Bi-Neng], Cao, Y.[Yao],
Complex background modeling based on Texture Pattern Flow with adaptive threshold propagation,
JVCIR(22), No. 6, August 2011, pp. 516-521.
Elsevier DOI 1108
Motion detection; Adaptive threshold propagation; GMM; Background modeling; Background subtraction; Texture Pattern Flow; Binary pattern; Integral histogram BibRef

Ali, I.[Imtiaz], Mille, J.[Julien], Tougne, L.[Laure],
Space-time spectral model for object detection in dynamic textured background,
PRL(33), No. 13, 1 October 2012, pp. 1710-1716.
Elsevier DOI 1208
Background model; Local Fourier transform; Dynamic texture; Object detection BibRef

Rivera, A.R., Murshed, M., Kim, J., Chae, O.,
Background Modeling Through Statistical Edge-Segment Distributions,
CirSysVideo(23), No. 8, 2013, pp. 1375-1387.
IEEE DOI 1308
Adaptation models BibRef

Hong, S.[Seokjin], Kim, J., Rivera, A.R., Song, G., Chae, O.,
Edge shape pattern for background modeling based on hybrid local codes,
AVSS16(1-7)
IEEE DOI 1611
Adaptation models BibRef

Kim, J., Rivera, A.R., Ryu, B., Chae, O.,
Simultaneous Foreground Detection and Classification with Hybrid Features,
ICCV15(3307-3315)
IEEE DOI 1602
Adaptation models BibRef

Zhang, R., Gong, W., Grzeda, V., Yaworski, A., Greenspan, M.,
An Adaptive Learning Rate Method for Improving Adaptability of Background Models,
SPLetters(20), No. 12, 2013, pp. 1266-1269.
IEEE DOI 1311
Adaptation models BibRef

Satpathy, A.[Amit], Jiang, X.D.[Xu-Dong], Eng, H.L.[How-Lung],
LBP-Based Edge-Texture Features for Object Recognition,
IP(23), No. 5, May 2014, pp. 1953-1964.
IEEE DOI 1405
BibRef
Earlier: A1, A3, A2:
Difference of Gaussian Edge-Texture Based Background Modeling for Dynamic Traffic Conditions,
ISVC08(I: 406-417).
Springer DOI 0812
edge detection BibRef

Shah, M.[Munir], Deng, J.D.[Jeremiah D.], Woodford, B.J.[Brendon J.],
Video background modeling: Recent Approaches, Issues and Our Proposed Techniques,
MVA(25), No. 5, July 2014, pp. 1105-1119.
WWW Link. 1407
See also Self-adaptive CodeBook (SACB) model for real-time background subtraction, A. BibRef

Bloisi, D.D.[Domenico D.], Pennisi, A.[Andrea], Iocchi, L.[Luca],
Background modeling in the maritime domain,
MVA(25), No. 5, July 2014, pp. 1257-1269.
WWW Link. 1407
BibRef

Basah, S.N.[Shafriza Nisha], Bab-Hadiashar, A.[Alireza], Hoseinnezhad, R.[Reza],
Analysis of planar-motion segmentation using affine fundamental matrix,
IET-CV(8), No. 6, 2014, pp. 658-669.
DOI Link 1502
BibRef
Earlier: A1, A3, A2:
Conditions for Segmentation of Motion with Affine Fundamental Matrix,
ISVC09(I: 415-424).
Springer DOI 0911
BibRef
Earlier: A1, A2, A3:
Conditions for Segmentation of 2D Translations of 3D Objects,
CIAP09(82-91).
Springer DOI 0909
BibRef
Earlier: A1, A3, A2:
Limits of Motion-Background Segmentation Using Fundamental Matrix Estimation,
DICTA08(250-256).
IEEE DOI 0812
Monte Carlo methods. Segment 3D objects with motion information. BibRef

Varadarajan, S.[Sriram], Wang, H.B.[Hong-Bin], Miller, P.[Paul], Zhou, H.Y.[Hui-Yu],
Fast convergence of regularised Region-based Mixture of Gaussians for dynamic background modelling,
CVIU(136), No. 1, 2015, pp. 45-58.
Elsevier DOI 1506
BibRef
Earlier:
Regularised region-based Mixture of Gaussians for dynamic background modelling,
AVSS14(19-24)
IEEE DOI 1411
Momentum. Convergence. Gaussian processes BibRef

Varadarajan, S.[Sriram], Miller, P.[Paul], Zhou, H.Y.[Hui-Yu],
Region-based Mixture of Gaussians modelling for foreground detection in dynamic scenes,
PR(48), No. 11, 2015, pp. 3488-3503.
Elsevier DOI 1506
BibRef
Earlier:
Spatial mixture of Gaussians for dynamic background modelling,
AVSS13(63-68)
IEEE DOI 1311
Region based modelling BibRef

Zhong, Z., Zhang, B., Lu, G., Zhao, Y., Xu, Y.,
An Adaptive Background Modeling Method for Foreground Segmentation,
ITS(18), No. 5, May 2017, pp. 1109-1121.
IEEE DOI 1705
Adaptation models, Intelligent transportation systems, Lighting, Motion segmentation, Object detection, Shape, Surveillance, Foreground segmentation, adaptive background updating, background, modeling BibRef


Yoshinaga, S., Shimada, A., Nagahara, H., Taniguchi, R.I., Kajitani, K., Naito, T.,
Multi-layered Background Modeling for Complex Environment Surveillance,
ACPR13(278-283)
IEEE DOI 1408
image motion analysis BibRef

Hall, E.C.[Eric C.], Willett, R.M.[Rebecca M.],
Foreground and background reconstruction in poisson video,
ICIP13(2484-2488)
IEEE DOI 1402
Background estimation BibRef

Stagliano, A.[Alessandra], Noceti, N.[Nicoletta], Verri, A.[Alessandro], Odone, F.[Francesca],
Background modeling through dictionary learning,
ICIP13(2524-2528)
IEEE DOI 1402
background modeling;dictionary learning;sparse coding BibRef

Kim, J.[Jaemyun], Rivera, A.R.[Adin Ramirez], Song, G.[Gihun], Ryu, B.Y.[Byung-Yong], Chae, O.[Oksam],
Edge-segment-based Background Modeling: Non-parametric online background update,
AVSS13(214-219)
IEEE DOI 1311
Adaptation models BibRef

Liang, D.[Dong], Kaneko, S.[Shun'ichi], Hashimoto, M.[Manabu], Iwata, K.[Kenji], Zhao, X.[Xinyue],
Co-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenes,
PR(48), No. 4, 2015, pp. 1374-1390.
Elsevier DOI 1502
Object detection BibRef

Liang, D.[Dong], Kaneko, S.[Shun'ichi], Hashimoto, M.[Manabu], Iwatao, K.[Kenji], Zhao, X.[Xinyue], Satoh, Y.[Yutaka],
Co-occurrence-based adaptive background model for robust object detection,
AVSS13(401-406)
IEEE DOI 1311
Adaptation models BibRef

Wang, T.T.[Ting-Ting], Liang, J.Z.[Jiu-Zhen], Wang, X.L.[Xiao-Long], Wang, S.Z.[Shi-Zheng],
Background modeling using Local Binary Patterns Of Motion Vector,
VCIP12(1-5).
IEEE DOI 1302
BibRef

Zhang, X.G.[Xian-Guo], Tian, Y.[Yonghong], Huang, T.J.[Tie-Jun], Gao, W.[Wen],
Low-complexity and high-efficiency background modeling for surveillance video coding,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Yumiba, R.[Ryo], Miyoshi, M.[Masanori], Fujiyoshi, H.[Hironobu],
Moving object detection with background model based on spatio-temporal texture,
WACV11(352-359).
IEEE DOI 1101
BibRef

Zhong, F.[Fan], Jiang, X.B.[Xin-Bo], Qin, X.Y.[Xue-Ying], Peng, Q.S.[Qun-Sheng],
Motion-based easy initialization of online foreground segmentation,
CVRS12(52-57).
IEEE DOI 1302
BibRef

Zhong, F.[Fan], Qin, X.Y.[Xue-Ying], Peng, Q.S.[Qun-Sheng],
Transductive segmentation of live video with non-stationary background,
CVPR10(2189-2196).
IEEE DOI 1006
BibRef

Murai, Y.[Yasuhiro], Fujiyoshi, H.[Hironobu], Kazui, M.[Masato],
Incident Detection Based on Dynamic Background Modeling and Statistical Learning Using Spatio-Temporal Features,
MVA09(156-).
PDF File. 0905
BibRef

Wu, X.Y.[Xiao-Yu], Wang, Y.S.[Yang-Sheng], Li, J.T.[Ji-Tuo],
Video Background Segmentation Using Adaptive Background Models,
CIAP09(623-632).
Springer DOI 0909
BibRef

Su, S.T.[Shu-Te], Chen, Y.Y.[Yung-Yaw],
Moving Object Segmentation Using Improved Running Gaussian Average Background Model,
DICTA08(24-31).
IEEE DOI 0812
BibRef

Murai, Y.[Yasuhiro], Fujiyoshi, H.[Hironobu], Kanade, T.[Takeo],
Combined Object Detection and Segmentation by Using Space-Time Patches,
ACCV07(I: 915-924).
Springer DOI 0711
BibRef

Crnojevic, V.[Vladimir], Antic, B.[Borislav], Culibrk, D.[Dubravko],
Optimal wavelet differencing method for robust motion detection,
ICIP09(645-648).
IEEE DOI 0911
BibRef

Culibrk, D.[Dubravko], Antic, B.[Borislav], Crnojevic, V.[Vladimir],
Real-time Stable Texture Regions Extraction for Motion-based Object Segmentation,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Ahn, J.K.[Jae-Kyun], Kim, C.S.[Chang-Su],
Real-time segmentation of objects from video sequences with non-stationary backgrounds using spatio-temporal coherence,
ICIP08(1544-1547).
IEEE DOI 0810
BibRef

Ramirez-Rivera, A.[Adin], Murshed, M.[Mahbub], Chae, O.[Oksam],
Object detection through edge behavior modeling,
AVSBS11(273-278).
IEEE DOI 1111
BibRef
And: A2, A1, A3:
Moving Edge Segment Matching for the Detection of Moving Object,
ICIAR11(I: 274-283).
Springer DOI 1106
BibRef

Dewan, M.A.A.[M. Ali Akber], Hossain, M.J.[M. Julius], Chae, O.[Oksam],
Background Independent Moving Object Segmentation Using Edge Similarity Measure,
ICIAR07(318-329).
Springer DOI 0708
BibRef

Yu, H.C.[Hong-Chuan], Bennamoun, M.,
A Phase Correlation Approach to Active Vision,
CAIP05(57).
Springer DOI 0509
Motion of object and background. BibRef

Russell, D.M., Gong, S.G.[Shao-Gang],
Segmenting Highly Textured Nonstationary Background,
BMVC07(xx-yy).
PDF File. 0709
BibRef
Earlier:
Minimum Cuts of A Time-Varying Background,
BMVC06(II:809).
PDF File. 0609
BibRef
Earlier:
A highly efficient block-based dynamic background model,
AVSBS05(417-422).
IEEE DOI 0602
BibRef

Zhong, J.[Jing], Sclaroff, S.,
Segmenting foreground objects from a dynamic textured background via a robust Kalman filter,
ICCV03(44-50).
IEEE DOI 0311
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Motion Sequence, Background Subtraction .


Last update:Sep 18, 2017 at 11:34:11