18.3.4.5 Interactive Motion Segmentation

Chapter Contents (Back)
Motion Segmentation. Interactive Segmentation.
See also Interactive Region Segmentations, Snakes, User-Assisted Segmentation.
See also Video Instance Segmentation.

Rochan, M.[Mrigank], Rahman, S.[Shafin], Bruce, N.D.B.[Neil D.B.], Wang, Y.[Yang],
Weakly supervised object localization and segmentation in videos,
IVC(56), No. 1, 2016, pp. 1-12.
Elsevier DOI 1609
Weakly supervised BibRef
Earlier:
Segmenting Objects in Weakly Labeled Videos,
CRV14(119-126)
IEEE DOI 1406
Computational modeling BibRef

Rochan, M.[Mrigank], Wang, Y.[Yang],
Latent SVM for Object Localization in Weakly Labeled Videos,
CRV15(200-207)
IEEE DOI 1507
BibRef
Earlier:
Efficient Object Localization and Segmentation in Weakly Labeled Videos,
ISVC14(I: 172-181).
Springer DOI 1501
Birds BibRef

Chang, X., Ma, Z., Lin, M., Yang, Y., Hauptmann, A.G.,
Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection,
IP(26), No. 8, August 2017, pp. 3911-3920.
IEEE DOI 1707
computer games, data mining, entertainment, human computer interaction, image classification, image motion analysis, learning (artificial intelligence), Kinect sensing devices, Kinect-based entertainment data sets, Schatten-p norm, classification model, correlation mining, data higher order effect, fast Kinect motion detection, feature interaction augmented sparse learning, fitness, gaming, human-computer interaction entertainment, linear algorithm, Data mining, Entertainment industry, Feature extraction, Motion detection, Real-time systems, Sensors, Skeleton, Feature interaction augmented sparse learning, fast, kinect, motion, detection BibRef

Liang, X.D.[Xiao-Dan], Wei, Y.C.[Yun-Chao], Lin, L.[Liang], Chen, Y.P.[Yun-Peng], Shen, X.H.[Xiao-Hui], Yang, J.C.[Jian-Chao], Yan, S.C.[Shui-Cheng],
Learning to Segment Human by Watching YouTube,
PAMI(39), No. 7, July 2017, pp. 1462-1468.
IEEE DOI 1706
Detectors, Image segmentation, Motion segmentation, Optimization, Semantics, Training, YouTube, Human segmentation, convolutional neural network, incremental learning, weakly-supervised, learning BibRef

Wang, Y.[Yi], Luo, Z.M.[Zhi-Ming], Jodoin, P.M.[Pierre-Marc],
Interactive deep learning method for segmenting moving objects,
PRL(96), No. 1, 2017, pp. 66-75.
Elsevier DOI 1709
Motion, detection BibRef

McHugh, J.M.[J. Mike], Konrad, J.[Janusz], Saligrama, V.[Venkatesh], Jodoin, P.M.[Pierre-Marc], Castanon, D.[David],
Motion detection with false discovery rate control,
ICIP08(873-876).
IEEE DOI 0810
BibRef

Lin, F.Q.[Fan-Qing], Chou, Y.[Yao], Martinez, T.[Tony],
Flow Adaptive Video Object Segmentation,
IVC(94), 2020, pp. 103864.
Elsevier DOI 2003
Video object segmentation, Optical flow, Online adaptation, Semi-supervised, Interactive, Object tracking BibRef

Chen, Z., Guo, C., Lai, J., Xie, X.,
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos,
CirSysVideo(30), No. 6, June 2020, pp. 1613-1624.
IEEE DOI 2006
Videos, Ice, Object segmentation, Encoding, Motion segmentation, Trajectory, Decoding, Video object segmentation, interactively constrained encoding BibRef

Zhang, Q., Wang, X., Wang, S., Sun, Z., Kwong, S., Jiang, J.,
Learning to Explore Saliency for Stereoscopic Videos Via Component-Based Interaction,
IP(29), 2020, pp. 5722-5736.
IEEE DOI 2005
Videos, Visualization, Stereo image processing, Computational modeling, deep learning BibRef

Yin, Y.J.[Ying-Jie], Xu, D.[De], Wang, X.G.[Xin-Gang], Zhang, L.[Lei],
AGUnet: Annotation-guided U-net for fast one-shot video object segmentation,
PR(110), 2021, pp. 107580.
Elsevier DOI 2011
Fully-convolutional Siamese network, U-net, Interactive image segmentation, Video object segmentation BibRef


Chen, B.[Bowen], Ling, H.[Huan], Zeng, X.H.[Xiao-Hui], Gao, J.[Jun], Xu, Z.[Ziyue], Fidler, S.[Sanja],
Scribblebox: Interactive Annotation Framework for Video Object Segmentation,
ECCV20(XIII:293-310).
Springer DOI 2011
BibRef

Yin, Z.[Zhaoyuan], Zheng, J.[Jia], Luo, W.X.[Wei-Xin], Qian, S.[Shenhan], Zhang, H.[Hanling], Gao, S.H.[Sheng-Hua],
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild,
CVPR21(15440-15449)
IEEE DOI 2111
Measurement, Codes, Annotations, Object segmentation, Reinforcement learning, Markov processes BibRef

Cheng, H.K.[Ho Kei], Tai, Y.W.[Yu-Wing], Tang, C.K.[Chi-Keung],
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion,
CVPR21(5555-5564)
IEEE DOI 2111
Codes, Fuses, Filtering, Object segmentation, Pattern recognition, Sparks BibRef

Heo, Y.[Yuk], Koh, Y.J.[Yeong Jun], Kim, C.S.[Chang-Su],
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps,
CVPR21(7318-7326)
IEEE DOI 2111
BibRef
Earlier:
Interactive Video Object Segmentation Using Global and Local Transfer Modules,
ECCV20(XVII:297-313).
Springer DOI 2011
Codes, Object segmentation, Inspection, Reliability engineering, Feature extraction, Data mining BibRef

Miao, J., Wei, Y., Yang, Y.,
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation,
CVPR20(10363-10372)
IEEE DOI 2008
Object segmentation, Robustness, Benchmark testing, Neural networks, Motion segmentation, Semantics, Streaming media BibRef

Deng, H.M.[Han-Ming], Hua, Y.[Yang], Song, T.[Tao], Zhang, Z.P.[Zong-Pu], Xue, Z.G.[Zhen-Gui], Ma, R.H.[Ru-Hui], Robertson, N.[Neil], Guan, H.B.[Hai-Bing],
Object Guided External Memory Network for Video Object Detection,
ICCV19(6677-6686)
IEEE DOI 2004
inference mechanisms, matrix algebra, object detection, video signal processing, feature map aggregation, Proposals BibRef

Oh, S.W.[Seoung Wug], Lee, J.Y.[Joon-Young], Xu, N.[Ning], Kim, S.J.[Seon Joo],
Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks,
CVPR19(5242-5251).
IEEE DOI 2002
BibRef

Oh, S.W.[Seoung Wug], Lee, J.Y.[Joon-Young], Sunkavalli, K., Kim, S.J.[Seon Joo],
Fast Video Object Segmentation by Reference-Guided Mask Propagation,
CVPR18(7376-7385)
IEEE DOI 1812
Training, Convolution, Object segmentation, Streaming media, Decoding, Benchmark testing, Video sequences BibRef

Lu, Y.[Yao], Bai, X.[Xue], Shapiro, L.G.[Linda G.], Wang, J.[Jue],
Coherent Parametric Contours for Interactive Video Object Segmentation,
CVPR16(642-650)
IEEE DOI 1612
BibRef

Dondera, R.[Radu], Morariu, V.I.[Vlad I.], Wang, Y.[Yulu], Davis, L.S.[Larry S.],
Interactive video segmentation using occlusion boundaries and temporally coherent superpixels,
WACV14(784-791)
IEEE DOI 1406
Adaptive optics BibRef

Ortega, M.,
Hook: Heuristics for selecting 3D moving objects in dense target environments,
3DUI13(119-122)
IEEE DOI 1406
interactive systems BibRef

Castagno, R., Sodomaco, A.,
Estimation of image feature reliability for an interactive video segmentation scheme,
ICIP98(I: 938-942).
IEEE DOI 9810
BibRef

Ariki, Y., Kanade, T., Sakai, T.,
An Interactive Image Modeling and Tracing System for Moving Pictures,
ICPR78(681-685). BibRef 7800

Wolferts, K.,
Special Problems in Interactive Image Processing for Traffic Analysis,
ICPR74(1-2). BibRef 7400

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Video Instance Segmentation .


Last update:Jan 13, 2022 at 22:02:22