Luo, Y.[Ye],
Yuan, J.S.[Jun-Song],
Xue, P.[Ping],
Tian, Q.[Qi],
Saliency Density Maximization for Efficient Visual Objects Discovery,
CirSysVideo(21), No. 12, December 2011, pp. 1822-1834.
IEEE DOI
1112
BibRef
Earlier:
Saliency Density Maximization for Object Detection and Localization,
ACCV10(III: 396-408).
Springer DOI
1011
BibRef
Luo, Y.[Ye],
Zhao, G.Q.[Gang-Qiang],
Yuan, J.S.[Jun-Song],
Thematic Saliency Detection Using Spatial-Temporal Context,
LSVSM13(347-353)
IEEE DOI
1403
data mining
BibRef
Hu, K.T.[Kang-Ting],
Leou, J.J.[Jin-Jang],
Hsiao, H.H.[Han-Hui],
Spatiotemporal saliency detection and salient region determination
for H.264 videos,
JVCIR(24), No. 7, 2013, pp. 760-772.
Elsevier DOI
1309
BibRef
Earlier:
Visual attention region determination for H.264 videos,
ICPR12(2038-2041).
WWW Link.
1302
Spatial saliency
BibRef
Ren, Z.X.[Zhi-Xiang],
Gao, S.H.[Sheng-Hua],
Chia, L.T.[Liang-Tien],
Rajan, D.,
Regularized Feature Reconstruction for
Spatio-Temporal Saliency Detection,
IP(22), No. 8, 2013, pp. 3120-3132.
IEEE DOI
1307
Laplace transforms; video saliency detection
BibRef
Liu, Z.[Zhi],
Zou, W.B.[Wen-Bin],
Le Meur, O.[Olivier],
Saliency Tree: A Novel Saliency Detection Framework,
IP(23), No. 5, May 2014, pp. 1937-1952.
IEEE DOI
1405
image colour analysis
BibRef
Perrin, A.F.[Anne-Flore],
Zhang, L.[Lu],
Le Meur, O.[Olivier],
How Well Current Saliency Prediction Models Perform on UAVs Videos?,
CAIP19(I:311-323).
Springer DOI
1909
BibRef
Le Meur, O.[Olivier],
Robustness and repeatability of saliency models subjected to visual
degradations,
ICIP11(3285-3288).
IEEE DOI
1201
BibRef
Ren, Z.X.[Zhi-Xiang],
Gao, S.H.[Sheng-Hua],
Chia, L.T.[Liang-Tien],
Tsang, I.W.,
Region-Based Saliency Detection and Its Application in Object
Recognition,
CirSysVideo(24), No. 5, May 2014, pp. 769-779.
IEEE DOI
1405
Clustering algorithms
BibRef
Hu, Y.Q.[Yi-Qun],
Ren, Z.X.[Zhi-Xiang],
Rajan, D.[Deepu],
Chia, L.T.[Liang-Tien],
Salient Region Detection by Jointly Modeling Distinctness and
Redundancy of Image Content,
ACCV10(II: 515-526).
Springer DOI
1011
BibRef
Yi, Y.[Yang],
Ding, J.[Jia],
Lai, J.L.[Jie-Ling],
A novel video salient object extraction method based on visual
attention,
SP:IC(28), No. 1, January 2013, pp. 45-54.
Elsevier DOI
1301
Video object segmentation; Salient object extraction; Visual attention
model; Attention object growing
BibRef
Li, Y.,
Sheng, B.,
Ma, L.,
Wu, W.,
Xie, Z.,
Temporally Coherent Video Saliency Using Regional Dynamic Contrast,
CirSysVideo(23), No. 12, 2013, pp. 2067-2076.
IEEE DOI
1312
Computational efficiency.
Region-based visual dynamic contrast.
BibRef
Han, J.,
He, S.,
Qian, X.,
Wang, D.,
Guo, L.,
Liu, T.,
An Object-Oriented Visual Saliency Detection Framework Based on
Sparse Coding Representations,
CirSysVideo(23), No. 12, 2013, pp. 2009-2021.
IEEE DOI
1312
Computer vision
BibRef
Li, W.T.[Wei-Te],
Chang, H.S.[Haw-Shiuan],
Lien, K.C.[Kuo-Chin],
Chang, H.T.[Hui-Tang],
Wang, Y.F.,
Exploring Visual and Motion Saliency for
Automatic Video Object Extraction,
IP(22), No. 7, 2013, pp. 2600-2610.
IEEE DOI conditional random field; foreground object extraction;
foreground-background region separation; visual saliency
1307
BibRef
Li, W.T.[Wei-Te],
Chang, H.T.[Hui-Tang],
Lyu, H.S.[Hermes Shing],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Automatic saliency inspired foreground object extraction from videos,
ICIP12(1089-1092).
IEEE DOI
1302
BibRef
Yang, J.,
Zhao, G.,
Yuan, J.,
Shen, X.,
Lin, Z.,
Price, B.,
Brandt, J.,
Discovering Primary Objects in Videos by Saliency Fusion and
Iterative Appearance Estimation,
CirSysVideo(26), No. 6, June 2016, pp. 1070-1083.
IEEE DOI
1606
Estimation
BibRef
Bhattacharya, S.[Saumik],
Venkatsh, K.S.,
Gupta, S.[Sumana],
Background estimation and motion saliency detection using total
variation-based video decomposition,
SIViP(11), No. 1, January 2017, pp. 113-121.
WWW Link.
1702
BibRef
Earlier: A1, A3, A2:
Visual saliency detection using video decomposition,
ICIP16(684-688)
IEEE DOI
1610
Cameras
BibRef
Duval-Poo, M.A.,
Noceti, N.,
Odone, F.[Francesca],
de Vito, E.[Ernesto],
Scale Invariant and Noise Robust Interest Points With Shearlets,
IP(26), No. 6, June 2017, pp. 2853-2867.
IEEE DOI
1705
Gaussian noise, feature extraction, image coding, Gaussian noise,
blob detection, blob-like features, compression artifacts,
directional multiscale framework, image compression,
shearlets framework,
BibRef
Malafronte, D.[Damiano],
Odone, F.[Francesca],
de Vito, E.[Ernesto],
Detecting Spatio-Temporally Interest Points Using the Shearlet
Transform,
IbPRIA17(501-510).
Springer DOI
1706
BibRef
Li, J.,
Xia, C.,
Chen, X.,
A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for
Video-Based Salient Object Detection,
IP(27), No. 1, January 2018, pp. 349-364.
IEEE DOI
1712
learning (artificial intelligence), object detection,
video coding, SOD dataset, benchmark dataset, eye-tracking data,
video dataset
BibRef
Sun, X.[Xiao],
Hu, Y.X.[Yu-Xing],
Zhang, L.M.[Lu-Ming],
Chen, Y.X.[Yan-Xiang],
Li, P.[Ping],
Xie, Z.[Zhao],
Liu, Z.G.[Zhen-Guang],
Camera-Assisted Video Saliency Prediction and Its Applications,
Cyber(48), No. 9, September 2018, pp. 2520-2530.
IEEE DOI
1809
Find important regions in the image.
cameras, computer vision, feature extraction,
learning (artificial intelligence), matrix decomposition,
video saliency
BibRef
Zhang, P.,
Yan, P.,
Wu, J.,
Liu, J.,
Shen, F.,
Unsupervised Saliency Detection in 3-D-Video Based on Multiscale
Segmentation and Refinement,
SPLetters(25), No. 9, September 2018, pp. 1384-1388.
IEEE DOI
1809
feature extraction, graph theory, image colour analysis,
image fusion, image motion analysis, image segmentation,
segmentation
BibRef
Pang, Y.,
Ye, L.,
Li, X.,
Pan, J.,
Incremental Learning With Saliency Map for Moving Object Detection,
CirSysVideo(28), No. 3, March 2018, pp. 640-651.
IEEE DOI
1804
Gaussian processes, image motion analysis, image sequences,
learning (artificial intelligence), minimisation,
subspace analysis
BibRef
Xu, B.,
Niu, Y.,
Accurate Object Segmentation for Video Sequences via
Temporal-Spatial-Frequency Saliency Model,
IEEE_Int_Sys(33), No. 1, January 2018, pp. 18-28.
IEEE DOI
1805
Cameras, Frequency-domain analysis, Image color analysis,
Image segmentation, Object segmentation, Video sequences,
visualization
BibRef
Le, T.N.[Trung-Nghia],
Sugimoto, A.[Akihiro],
Video Salient Object Detection Using Spatiotemporal Deep Features,
IP(27), No. 10, October 2018, pp. 5002-5015.
IEEE DOI
1808
BibRef
Earlier:
Contrast Based Hierarchical Spatial-Temporal Saliency for Video,
PSIVT15(734-748).
Springer DOI
1602
feature extraction, image colour analysis, image representation,
image segmentation, object detection, random processes,
video object segmentation
BibRef
Chen, C.,
Li, S.,
Qin, H.,
Pan, Z.,
Yang, G.,
Bilevel Feature Learning for Video Saliency Detection,
MultMed(20), No. 12, December 2018, pp. 3324-3336.
IEEE DOI
1812
feature extraction, learning (artificial intelligence),
Markov processes, object detection, video signal processing,
Video Saliency Detection
BibRef
Chen, Y.[Yu],
Xiao, J.[Jing],
Hu, L.[Liuyi],
Chen, D.[Dan],
Wang, Z.Y.[Zhong-Yuan],
Li, D.S.[Deng-Shi],
Video Saliency Detection Using Spatiotemporal Cues,
IEICE(E101-D), No. 9, September 2018, pp. 2201-2208.
WWW Link.
1809
BibRef
Tang, Y.[Yi],
Zou, W.B.[Wen-Bin],
Jin, Z.[Zhi],
Chen, Y.H.[Yu-Huan],
Hua, Y.[Yang],
Li, X.[Xia],
Weakly Supervised Salient Object Detection With Spatiotemporal
Cascade Neural Networks,
CirSysVideo(29), No. 7, July 2019, pp. 1973-1984.
IEEE DOI
1907
Videos, Object detection, Spatiotemporal phenomena,
Neural networks, Saliency detection, Machine learning,
cascade fully convolutional network
BibRef
Peng, Q.,
Cheung, Y.,
Automatic Video Object Segmentation Based on Visual and Motion
Saliency,
MultMed(21), No. 12, December 2019, pp. 3083-3094.
IEEE DOI
1912
Manifolds, Visualization, Object segmentation, Motion segmentation,
Image segmentation, Autoregressive processes, Labeling,
graph model
BibRef
Wen, H.F.[Hong-Fa],
Zhou, X.F.[Xiao-Fei],
Sun, Y.[Yaoqi],
Zhang, J.Y.[Ji-Yong],
Yan, C.G.[Cheng-Gang],
Deep fusion based video saliency detection,
JVCIR(62), 2019, pp. 279-285.
Elsevier DOI
1908
Video saliency, Convolutional network, Feature integration, Boundary
BibRef
Patil, P.W.,
Murala, S.,
MSFgNet:
A Novel Compact End-to-End Deep Network for Moving Object Detection,
ITS(20), No. 11, November 2019, pp. 4066-4077.
IEEE DOI
1911
Videos, Object detection, Estimation, Feature extraction, Lighting,
Image color analysis, Saliency detection, Background estimation, CNN
BibRef
Patil, P.W.,
Thawakar, O.,
Dudhane, A.,
Murala, S.,
Motion Saliency Based Generative Adversarial Network for Underwater
Moving Object Segmentation,
ICIP19(1565-1569)
IEEE DOI
1910
Underwater medium, motion saliency,
Generative adversarial network, frame segmentation
BibRef
Maczyta, L.[Léo],
Bouthemy, P.[Patrick],
Meur, O.L.[Olivier Le],
CNN-based temporal detection of motion saliency in videos,
PRL(128), 2019, pp. 298-305.
Elsevier DOI
1912
Motion saliency, Temporal detection, CNN-based classification,
Dominant motion estimation, Video analysis
BibRef
Xu, M.,
Liu, B.,
Fu, P.,
Li, J.,
Hu, Y.H.,
Video Saliency Detection via Graph Clustering With Motion Energy and
Spatiotemporal Objectness,
MultMed(21), No. 11, November 2019, pp. 2790-2805.
IEEE DOI
1911
Spatiotemporal phenomena, Visualization, Dynamics, Proposals,
Feature extraction, Fuses, Video saliency,
graph clustering
BibRef
Chen, C.,
Wang, G.,
Peng, C.,
Zhang, X.,
Qin, H.,
Improved Robust Video Saliency Detection Based on Long-Term
Spatial-Temporal Information,
IP(29), No. 1, 2020, pp. 1090-1100.
IEEE DOI
1911
Saliency detection, Deep learning, Quality assessment, Trajectory,
Computational modeling, Color, Training, Video saliency detection,
long-term information revealing
BibRef
Lai, Q.,
Wang, W.,
Sun, H.,
Shen, J.,
Video Saliency Prediction Using Spatiotemporal Residual Attentive
Networks,
IP(29), No. 1, 2020, pp. 1113-1126.
IEEE DOI
1911
Visualization, Computational modeling, Spatiotemporal phenomena,
Dynamics, Task analysis, Data models, Predictive models,
video saliency
BibRef
Fang, Y.,
Ding, G.,
Li, J.,
Fang, Z.,
Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by
3D Convolutional Networks,
IP(28), No. 5, May 2019, pp. 2305-2318.
IEEE DOI
1903
convolutional neural nets, deconvolution, feature extraction,
image sequences, object detection, optimisation,
3D convolutional neural networks
BibRef
Fang, Y.M.[Yu-Ming],
Zhang, X.Q.[Xiao-Qiang],
Yuan, F.N.[Fei-Niu],
Imamoglu, N.[Nevrez],
Liu, H.[Haiwen],
Video saliency detection by gestalt theory,
PR(96), 2019, pp. 106987.
Elsevier DOI
1909
Visual attention, Video saliency detection, Gestalt theory,
Uncertainty weighting, Spatiotemporal saliency
BibRef
Wang, Q.[Qiong],
Zhang, L.[Lu],
Zou, W.B.[Wen-Bin],
Kpalma, K.[Kidiyo],
Salient video object detection using a virtual border and guided
filter,
PR(97), 2020, pp. 106998.
Elsevier DOI
1910
Video salient object detection, Distance transform,
Guided filter, Global motion
BibRef
Wu, Z.,
Su, L.,
Huang, Q.,
Learning Coupled Convolutional Networks Fusion for Video Saliency
Prediction,
CirSysVideo(29), No. 10, October 2019, pp. 2960-2971.
IEEE DOI
1910
computer vision, convolutional neural nets, feature extraction,
image motion analysis, inference mechanisms,
fully convolutional network
BibRef
Li, J.,
Fu, K.,
Zhao, S.,
Ge, S.,
Spatiotemporal Knowledge Distillation for Efficient Estimation of
Aerial Video Saliency,
IP(29), No. 1, 2020, pp. 1902-1914.
IEEE DOI
1912
Computational modeling, Redundancy, Estimation, Visualization,
Data models, Spatiotemporal phenomena, Drones,
aerial video
BibRef
Zhang, K.[Kao],
Chen, Z.Z.[Zhen-Zhong],
Video Saliency Prediction Based on Spatial-Temporal Two-Stream
Network,
CirSysVideo(29), No. 12, December 2019, pp. 3544-3557.
IEEE DOI
1912
Feature extraction, Predictive models, Streaming media,
Visualization, Spatiotemporal phenomena, Computational modeling,
deep learning
BibRef
Kavak, Y.[Yasin],
Erdem, E.[Erkut],
Erdem, A.[Aykut],
Hedging static saliency models to predict dynamic saliency,
SP:IC(81), 2020, pp. 115694.
Elsevier DOI
1912
Dynamic saliency, Hedge algorithm,
Decision theoretic online learning, Feature integration
BibRef
Jiang, L.[Lai],
Xu, M.[Mai],
Zhang, S.[Shanyi],
Sigal, L.[Leonid],
DeepCT: A novel deep complex-valued network with learnable transform
for video saliency prediction,
PR(102), 2020, pp. 107234.
Elsevier DOI
2003
Saliency prediction, Complex-valued network,
Learnable transform, Convolutional LSTM
BibRef
Fang, Y.M.[Yu-Ming],
Zhang, C.[Chi],
Min, X.[Xiongkuo],
Huang, H.[Hanqin],
Yi, Y.[Yugen],
Zhai, G.T.[Guang-Tao],
Lin, C.W.[Chia-Wen],
DevsNet: Deep Video Saliency Network using Short-term and Long-term
Cues,
PR(103), 2020, pp. 107294.
Elsevier DOI
2005
Video saliency detection, Spatiotemporal saliency,
3D convolution network (3D-ConvNet),
Bidirectional convolutional long-short term memory network (B-ConvLSTM)
BibRef
Li, Y.,
Li, S.,
Chen, C.,
Hao, A.,
Qin, H.,
Accurate and Robust Video Saliency Detection via Self-Paced Diffusion,
MultMed(22), No. 5, May 2020, pp. 1153-1167.
IEEE DOI
2005
Saliency detection, Proposals, Video sequences, Spatial coherence,
Computational modeling, Optical imaging, Optical sensors,
self-paced saliency diffusion
BibRef
Zhou, F.[Feng],
Shuai, H.[Hui],
Liu, Q.S.[Qing-Shan],
Guo, G.D.[Guo-Dong],
Flow driven attention network for video salient object detection,
IET-IPR(14), No. 6, 11 May 2020, pp. 997-1004.
DOI Link
2005
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
Wang, Q.[Qiong],
Zhang, L.[Lu],
Li, Y.[Yan],
Kpalma, K.[Kidiyo],
Overview of deep-learning based methods for salient object detection
in videos,
PR(104), 2020, pp. 107340.
Elsevier DOI
2005
Deep-learning, Salient object detection, Video
BibRef
Shokri, M.[Mohammad],
Harati, A.[Ahad],
Taba, K.[Kimya],
Salient object detection in video using deep non-local neural
networks,
JVCIR(68), 2020, pp. 102769.
Elsevier DOI
2005
Video saliency detection, Deep learning,
Non-local neural networks, Fully convolutional neural networks
BibRef
Xu, M.,
Liu, B.,
Fu, P.,
Li, J.,
Hu, Y.H.,
Feng, S.,
Video Salient Object Detection via Robust Seeds Extraction and
Multi-Graphs Manifold Propagation,
CirSysVideo(30), No. 7, July 2020, pp. 2191-2206.
IEEE DOI
2007
Spatiotemporal phenomena, Dynamics, Manifolds, Object detection,
Reliability, Visualization, Feature extraction, Video saliency,
multi-graphs saliency propagation
BibRef
Chen, J.[Jin],
Song, H.H.[Hui-Hui],
Zhang, K.H.[Kai-Hua],
Liu, B.[Bo],
Liu, Q.S.[Qing-Shan],
Video saliency prediction using enhanced spatiotemporal alignment
network,
PR(109), 2021, pp. 107615.
Elsevier DOI
2009
Video saliency prediction, Feature alignment,
Deformable convolution, Bidirectional ConvLSTM
BibRef
Wang, B.,
Liu, W.,
Han, G.,
He, S.,
Learning Long-Term Structural Dependencies for Video Salient Object
Detection,
IP(29), 2020, pp. 9017-9031.
IEEE DOI
2009
Predictive models, Object detection, Feature extraction,
Saliency detection, Convolution, Merging, Object recognition,
supervoxel
BibRef
Yan, P.,
Li, G.,
Xie, Y.,
Li, Z.,
Wang, C.,
Chen, T.,
Lin, L.,
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels,
ICCV19(7283-7292)
IEEE DOI
2004
convolutional neural nets, image colour analysis,
image enhancement, image motion analysis, image sequences, Coherence
BibRef
Le, T.,
Sugimoto, A.,
Semantic Instance Meets Salient Object:
Study on Video Semantic Salient Instance Segmentation,
WACV19(1779-1788)
IEEE DOI
1904
image segmentation, image sequences, mobile robots,
object detection, robot vision, video signal processing,
Dogs
BibRef
Fan, D.P.[Deng-Ping],
Wang, W.[Wenguan],
Cheng, M.M.[Ming-Ming],
Shen, J.B.[Jian-Bing],
Shifting More Attention to Video Salient Object Detection,
CVPR19(8546-8556).
IEEE DOI
2002
BibRef
Song, H.M.[Hong-Mei],
Wang, W.[Wenguan],
Zhao, S.[Sanyuan],
Shen, J.B.[Jian-Bing],
Lam, K.M.[Kin-Man],
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection,
ECCV18(XI: 744-760).
Springer DOI
1810
BibRef
Hoo, W.L.,
Chan, C.S.,
Anisotropic Partial Differential Equation Based Video Saliency
Detection,
ICIP18(2311-2315)
IEEE DOI
1809
Saliency detection, Image color analysis, Tensile stress,
Feature extraction, Task analysis, Discrete cosine transforms,
partial differential equation
BibRef
Xiao, B.[Bo],
Wang, B.[Bin],
Efficient HD video and image salient object detection with
hierarchical boolean map approach,
ICIVC17(1-7)
IEEE DOI
1708
Acceleration, Algorithm design and analysis, Feature extraction,
Floods, Image resolution, Object detection, Visualization,
hierarchical boolean map, parallel processing,
salient object detection, video consistency
BibRef
Zhang, L.[Long],
Liu, Y.J.[Yu-Jun],
Han, S.D.[Shou-Dong],
Video segmentation based on strong target constrained video saliency,
ICIVC17(356-360)
IEEE DOI
1708
Adaptive optics, Image color analysis, Image segmentation,
Optical imaging, Robustness, Target tracking,
full-connected conditional random field, object proposal,
super-pixel, video saliency, video, segmentation
BibRef
Wei, L.,
Wang, F.,
Li, X.,
Wu, F.,
Xiao, J.,
Graph-theoretic spatiotemporal context modeling for video saliency
detection,
ICIP17(4197-4201)
IEEE DOI
1803
Atomic layer deposition, Context modeling, Manifolds,
Saliency detection, Spatiotemporal phenomena,
Video saliency
BibRef
Hu, Y.T.[Yuan-Ting],
Huang, J.B.[Jia-Bin],
Schwing, A.G.[Alexander G.],
VideoMatch: Matching Based Video Object Segmentation,
ECCV18(VIII: 56-73).
Springer DOI
1810
BibRef
And:
Unsupervised Video Object Segmentation Using Motion Saliency-Guided
Spatio-Temporal Propagation,
ECCV18(I: 813-830).
Springer DOI
1810
BibRef
Chan, K.L.,
Saliency/non-saliency segregation in video sequences using
perception-based local ternary pattern features,
MVA17(510-513)
DOI Link
1708
Adaptation models, Color, Computational modeling, History,
Image color analysis, Mathematical model, Video sequences
BibRef
Karimi, A.H.,
Shafiee, M.J.,
Scharfenberger, C.,
Ben Daya, I.,
Haider, S.,
Talukdar, N.,
Clausi, D.A.,
Wong, A.,
Spatio-temporal saliency detection using abstracted fully-connected
graphical models,
ICIP16(694-698)
IEEE DOI
1610
Computational complexity
BibRef
Xue, K.[Kang],
Wang, X.[Xiying],
Ma, G.Y.[Geng-Yu],
Wang, H.T.[Hai-Tao],
Nam, D.[Dong_Kyung],
A video saliency detection method based on spatial and motion
information,
ICIP15(412-416)
IEEE DOI
1512
Classification; Motion; Saliency; Video
BibRef
Lee, T.[Teahyung],
Hwangbo, M.[Myung],
Alan, T.[Tanfer],
Tickoo, O.[Omesh],
Iyer, R.[Ravishankar],
Low-complexity HOG for efficient video saliency,
ICIP15(3749-3752)
IEEE DOI
1512
low-complexity HOG; video saliency
BibRef
Mansour, H.[Hassan],
Rane, S.[Shantanu],
Boufounos, P.T.[Petros T.],
Vetro, A.[Anthony],
Video querying via compact descriptors of visually salient objects,
ICIP14(2789-2793)
IEEE DOI
1502
Accuracy
BibRef
Hiratani, A.,
Nakashima, R.,
Matsumiya, K.,
Kuriki, I.,
Shioiri, S.,
Considerations of Self-Motion in Motion Saliency,
ACPR13(783-787)
IEEE DOI
1408
feature extraction
BibRef
Agarwal, D.,
Soni, N.,
Namboodiri, A.M.,
Salient object detection in SfM point cloud,
NCVPRIPG13(1-4)
IEEE DOI
1408
image motion analysis
BibRef
Gan, C.[Chuang],
Qin, Z.C.[Zeng-Chang],
Xu, J.[Jia],
Wan, T.[Tao],
Salient object detection in image sequences via spatial-temporal cue,
VCIP13(1-6)
IEEE DOI
1402
feature extraction
BibRef
Xia, Y.[Yang],
Hu, R.M.[Rui-Min],
Huang, Z.K.[Zhen-Kun],
Su, Y.[Yin],
A novel method for generation of motion saliency,
ICIP10(4685-4688).
IEEE DOI
1009
BibRef
Taylor, G.W.[Graham W.],
Fergus, R.[Rob],
Le Cun, Y.L.[Yann L.],
Bregler, C.[Christoph],
Convolutional Learning of Spatio-temporal Features,
ECCV10(VI: 140-153).
Springer DOI
1009
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Interactive Motion Segmentation .