11.2.4.1 Range and Color, RGB-D Segmentation and Analysis

Chapter Contents (Back)
Range Segmentation. RGB-D Segmentation. See also Color Applied to Segmentation.

Hulik, R.[Rostislav], Spanel, M.[Michal], Smrz, P.[Pavel], Materna, Z.[Zdenek],
Continuous plane detection in point-cloud data based on 3D Hough Transform,
JVCIR(25), No. 1, 2014, pp. 86-97.
Elsevier DOI 1502
Hough Transform. RGB-D sensor BibRef

Stückler, J.[Jörg], Behnke, S.[Sven],
Multi-resolution surfel maps for efficient dense 3D modeling and tracking,
JVCIR(25), No. 1, 2014, pp. 137-147.
Elsevier DOI 1502
BibRef
And: Corrigendum: JVCIR(26), No. 1, 2015, pp. 349-.
Elsevier DOI 1502
3D multi-resolution RGB-D image representation BibRef

Riaz, Z.[Zahid], Linder, T.[Thorsten], Behnke, S.[Sven], Worst, R.[Rainer], Surmann, H.[Hartmut],
Efficient Transmission and Rendering of RGB-D Views,
ISVC13(I:517-526).
Springer DOI 1310
BibRef

Liu, H.[Haowei], Philipose, M.[Matthai], Sun, M.T.[Ming-Ting],
Automatic objects segmentation with RGB-D cameras,
JVCIR(25), No. 4, 2014, pp. 709-718.
Elsevier DOI 1403
BibRef
Earlier:
Automatic object segmentation with 3-D cameras,
ICIP12(569-572).
IEEE DOI 1302
Boundary detection See also Recognizing object manipulation activities using depth and visual cues. BibRef

Richtsfeld, A.[Andreas], Mörwald, T.[Thomas], Prankl, J.[Johann], Zillich, M.[Michael], Vincze, M.[Markus],
Learning of perceptual grouping for object segmentation on RGB-D data,
JVCIR(25), No. 1, 2014, pp. 64-73.
Elsevier DOI 1502
Computer vision BibRef

Fäulhammer, T., Zillich, M.[Michael], Prankl, J.[Johann], Vincze, M.[Markus],
A multi-modal RGB-D object recognizer,
ICPR16(733-738)
IEEE DOI 1705
Cameras, Computational modeling, Feature extraction, Pipelines, Shape, Three-dimensional displays, Training BibRef

Zhou, K.[Kai], Richtsfeld, A.[Andreas], Varadarajan, K.M.[Karthik Mahesh], Zillich, M.[Michael], Vincze, M.[Markus],
Combining Plane Estimation with Shape Detection for Holistic Scene Understanding,
ACIVS11(736-747).
Springer DOI 1108
BibRef

Varadarajan, K.M.[Karthik Mahesh], Vincze, M.[Markus],
AfNet: The Affordance Network,
ACCV12(I:512-523).
Springer DOI 1304
BibRef
Earlier:
Knowledge Representation and Inference for Grasp Affordances,
CVS11(173-182).
Springer DOI 1109
BibRef
And:
Surface reconstruction for RGB-D data using real-time depth propagation,
Dense11(723-724).
IEEE DOI 1201
BibRef
Earlier:
Real-time depth diffusion for 3D surface reconstruction,
ICIP10(4149-4152).
IEEE DOI 1009
BibRef

Olufs, S.[Sven], Vincze, M.[Markus],
Room-structure estimation in Manhattan-like environments from dense 2˝D range data using minumum entropy and histograms,
WACV11(118-124).
IEEE DOI 1101
BibRef

Richtsfeld, M.[Mario], Vincze, M.[Markus],
Point Cloud Segmentation Based on Radial Reflection,
CAIP09(955-962).
Springer DOI 0909
BibRef

He, B.[Bei], Wang, G.J.[Gui-Jin], Zhang, C.[Cha],
Iterative transductive learning for automatic image segmentation and matting with RGB-D data,
JVCIR(25), No. 5, 2014, pp. 1031-1043.
Elsevier DOI 1406
Image matting BibRef

Camplani, M.[Massimo], del Blanco, C.R.[Carlos Roberto], Salgado, L.[Luis], Jaureguizar, F.[Fernando], García, N.[Narciso],
Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction,
MVA(25), No. 5, July 2014, pp. 1197-1210.
Springer DOI 1407
BibRef

Camplani, M.[Massimo], Salgado, L.[Luis],
Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers,
JVCIR(25), No. 1, 2014, pp. 122-136.
Elsevier DOI 1502
RGB-D cameras BibRef

Camplani, M.[Massimo], del Blanco, C.R.[Carlos R.], Salgado, L.[Luis], Jaureguizar, F.[Fernando], García, N.[Narciso],
Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers,
PRL(50), No. 1, 2014, pp. 23-33.
Elsevier DOI 1410
Region-based background modeling BibRef

Husain, F., Dellen, B., Torras, C.,
Consistent Depth Video Segmentation Using Adaptive Surface Models,
Cyber(45), No. 2, February 2015, pp. 266-278.
IEEE DOI 1502
image segmentation BibRef

Abramov, A.[Alexey], Pauwels, K.[Karl], Papon, J.[Jeremie], Worgotter, F.[Florentin], Dellen, B.[Babette],
Depth-supported real-time video segmentation with the Kinect,
WACV12(457-464).
IEEE DOI 1203
Depth aided. BibRef

Yang, J., Gan, Z., Li, K., Hou, C.,
Graph-Based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels,
Cyber(45), No. 5, May 2015, pp. 913-926.
IEEE DOI 1505
Color BibRef

Wang, A.[Anran], Lu, J.W.[Ji-Wen], Cai, J.F.[Jian-Fei], Wang, G.[Gang], Cham, T.J.[Tat-Jen],
Unsupervised Joint Feature Learning and Encoding for RGB-D Scene Labeling,
IP(24), No. 11, November 2015, pp. 4459-4473.
IEEE DOI 1509
BibRef
Earlier: A1, A2, A4, A3, A5:
Multi-modal Unsupervised Feature Learning for RGB-D Scene Labeling,
ECCV14(V: 453-467).
Springer DOI 1408
image coding BibRef

Wang, A.[Anran], Cai, J.F.[Jian-Fei], Lu, J.W.[Ji-Wen], Cham, T.J.[Tat-Jen],
Modality and Component Aware Feature Fusion for RGB-D Scene Classification,
CVPR16(5995-6004)
IEEE DOI 1612
BibRef
Earlier:
MMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition,
ICCV15(1125-1133)
IEEE DOI 1602
Computer vision BibRef

Wang, A.[Anran], Lu, J.W.[Ji-Wen], Cai, J.F.[Jian-Fei], Cham, T.J.[Tat-Jen], Wang, G.[Gang],
Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition,
MultMed(17), No. 11, November 2015, pp. 1887-1898.
IEEE DOI 1511
Correlation BibRef

Tang, J., Jin, L., Li, Z., Gao, S.,
RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge,
MultMed(17), No. 11, November 2015, pp. 1899-1908.
IEEE DOI 1511
Data structures BibRef

Fehr, D.[Duc], Beksi, W.J.[William J.], Zermas, D.[Dimitris], Papanikolopoulos, N.[Nikolaos],
Covariance based point cloud descriptors for object detection and recognition,
CVIU(142), No. 1, 2016, pp. 80-93.
Elsevier DOI 1512
RGB-D data BibRef

Stückler, J.[Jörg], Waldvogel, B.[Benedikt], Schulz, H.[Hannes], Behnke, S.[Sven],
Dense real-time mapping of object-class semantics from RGB-D video,
RealTimeIP(10), No. 4, December 2015, pp. 599-609.
Springer DOI 1512
BibRef

Sanchez-Riera, J.[Jordi], Hua, K.L.[Kai-Lung], Hsiao, Y.S.[Yuan-Sheng], Lim, T.[Tekoing], Hidayati, S.C.[Shintami C.], Cheng, W.H.[Wen-Huang],
A comparative study of data fusion for RGB-D based visual recognition,
PRL(73), No. 1, 2016, pp. 1-6.
Elsevier DOI 1604
RGB-D BibRef

Ahmed, N.[Naveed], Khalifa, S.[Salam],
Time-coherent 3D animation reconstruction from RGB-D video,
SIViP(10), No. 4, April 2016, pp. 783-790.
Springer DOI 1604
BibRef

Thřgersen, M.[Mikkel], Escalera, S.[Sergio], Gonzŕlez, J.[Jordi], Moeslund, T.B.[Thomas B.],
Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields,
PRL(80), No. 1, 2016, pp. 208-215.
Elsevier DOI 1609
RGB-D sematic segmentation BibRef

Hasnat, M.A.[M. Abul], Alata, O.[Olivier], Trémeau, A.[Alain],
Joint Color-Spatial-Directional Clustering and Region Merging (JCSD-RM) for Unsupervised RGB-D Image Segmentation,
PAMI(38), No. 11, November 2016, pp. 2255-2268.
IEEE DOI 1610
BibRef
Earlier:
Unsupervised Clustering of Depth Images Using Watson Mixture Model,
ICPR14(214-219)
IEEE DOI 1412
BibRef
Earlier:
Unsupervised RGB-D image segmentation using joint clustering and region merging,
BMVC14(xx-yy).
HTML Version. 1410
Clustering methods BibRef

Li, X.[Xiao], Fang, M.[Min], Zhang, J.J.[Ju-Jie], Wu, J.Q.[Jin-Qiao],
Learning Coupled Classifiers with RGB images for RGB-D object recognition,
PR(61), No. 1, 2017, pp. 433-446.
Elsevier DOI 1705
Object recognition BibRef

Ibańez, R.[Rodrigo], Soria, Á.[Álvaro], Teyseyre, A.[Alfredo], Rodríguez, G.[Guillermo], Campo, M.[Marcelo],
Approximate string matching: A lightweight approach to recognize gestures with Kinect,
PR(62), No. 1, 2017, pp. 73-86.
Elsevier DOI 1705
Natural user interfaces BibRef

Deng, Z.[Zhuo], Todorovic, S.[Sinisa], Latecki, L.J.[Longin Jan],
Unsupervised object region proposals for RGB-D indoor scenes,
CVIU(154), No. 1, 2017, pp. 127-136.
Elsevier DOI 1612
BibRef
Earlier: A1, A3, Only:
Unsupervised Segmentation of RGB-D Images,
ACCV14(III: 423-435).
Springer DOI 1504
Object segmentation BibRef

Song, H.[Hangke], Liu, Z.[Zhi], Xie, Y.F.[Yu-Feng], Wu, L.S.[Li-Shan], Huang, M.[Mengke],
RGBD Co-Saliency Detection via Bagging-Based Clustering,
SPLetters(23), No. 12, December 2016, pp. 1722-1726.
IEEE DOI 1612
feature extraction BibRef

Wang, Z.L.[Zi-Lei], Xiang, D., Hou, S.H.[Sai-Hui], Wu, F.[Feng],
Background-Driven Salient Object Detection,
MultMed(19), No. 4, April 2017, pp. 750-762.
IEEE DOI 1704
Benchmark testing BibRef

Hou, S.H.[Sai-Hui], Wang, Z.L.[Zi-Lei], Wu, F.[Feng],
Deeply Exploit Depth Information for Object Detection,
Robust16(1092-1100)
IEEE DOI 1612
BibRef

Zhao, L.J.[Li-Jun], Bai, H.H.[Hui-Hui], Wang, A.H.[An-Hong], Zhao, Y.[Yao], Zeng, B.[Bing],
Two-stage filtering of compressed depth images with Markov Random Field,
SP:IC(54), No. 1, 2017, pp. 11-22.
Elsevier DOI 1704
BibRef
Earlier: A1, A2, A3, A4, Only:
Joint iterative guidance filtering for compressed depth images,
VCIP16(1-4)
IEEE DOI 1701
MRF Color. Filter depth and color together. BibRef

Zhao, L.J.[Li-Jun], Liang, J.[Jie], Bai, H.H.[Hui-Hui], Wang, A.H.[An-Hong], Zhao, Y.[Yao],
Convolutional neural network-based depth image artifact removal,
ICIP17(2438-2442)
IEEE DOI 1803
Color, Feature extraction, Image coding, Image color analysis, Image resolution, Training, joint filtering BibRef

Qu, L., He, S., Zhang, J., Tian, J., Tang, Y., Yang, Q.,
RGBD Salient Object Detection via Deep Fusion,
IP(26), No. 5, May 2017, pp. 2274-2285.
IEEE DOI 1704
Electronic mail BibRef

Wang, A., Wang, M.,
RGB-D Salient Object Detection via Minimum Barrier Distance Transform and Saliency Fusion,
SPLetters(24), No. 5, May 2017, pp. 663-667.
IEEE DOI 1704
Image color analysis BibRef

Xu, X.Y.[Xiang-Yang], Li, Y.C.[Yun-Cheng], Wu, G.S.[Gang-Shan], Luo, J.B.[Jie-Bo],
Multi-modal deep feature learning for RGB-D object detection,
PR(72), No. 1, 2017, pp. 300-313.
Elsevier DOI 1708
RGB-D, objectness estimation BibRef

Moyŕ-Alcover, G.[Gabriel], Elgammal, A.[Ahmed], Jaume-i-Capó, A.[Antoni], Varona, J.[Javier],
Modeling depth for nonparametric foreground segmentation using RGBD devices,
PRL(96), No. 1, 2017, pp. 76-85.
Elsevier DOI 1709
Background, subtraction BibRef

Trabelsi, R.[Rim], Jabri, I.[Issam], Smach, F.[Fethi], Bouallegue, A.[Ammar],
Efficient and fast multi-modal foreground-background segmentation using RGBD data,
PRL(97), No. 1, 2017, pp. 13-20.
Elsevier DOI 1709
Background, subtraction BibRef

Su, W.[Wen], Wang, Z.F.[Zeng-Fu],
Widening residual skipped network for semantic segmentation,
IET-IPR(11), No. 10, October 2017, pp. 880-887.
DOI Link 1710
BibRef
Earlier:
Regularized fully convolutional networks for RGB-D semantic segmentation,
VCIP16(1-4)
IEEE DOI 1701
Brightness BibRef

Pagnutti, G.[Giampaolo], Minto, L.[Ludovico], Zanuttigh, P.[Pietro],
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting,
IET-CV(11), No. 8, December 2017, pp. 633-642.
DOI Link 1712
BibRef

Zheng, Y.B.[Ying-Bin], Ye, H.[Hao], Wang, L.[Li], Pu, J.[Jian],
Learning Multiviewpoint Context-Aware Representation for RGB-D Scene Classification,
SPLetters(25), No. 1, January 2018, pp. 30-34.
IEEE DOI 1801
feature extraction, image classification, image colour analysis, image representation, learning (artificial intelligence), scene classification BibRef

Tan, L.[Lu], Pan, Z.K.[Zhen-Kuan], Liu, W.Q.[Wan-Quan], Duan, J.M.[Jin-Ming], Wei, W.B.[Wei-Bo], Wang, G.D.[Guo-Dong],
Image Segmentation with Depth Information via Simplified Variational Level Set Formulation,
JMIV(60), No. 1, January 2018, pp. 1-17.
Springer DOI 1801
BibRef

Imamoglu, N.[Nevrez], Shimoda, W.[Wataru], Zhang, C.[Chi], Fang, Y.M.[Yu-Ming], Kanezaki, A.[Asako], Yanai, K.[Keiji], Nishida, Y.[Yoshifumi],
An integration of bottom-up and top-down salient cues on RGB-D data: saliency from objectness versus non-objectness,
SIViP(12), No. 2, February 2018, pp. 307-314.
Springer DOI 1802
BibRef

Yu, Q.H.[Qing-Hua], Liang, J.[Jie], Xiao, J.H.[Jun-Hao], Lu, H.M.[Hui-Min], Zheng, Z.Q.[Zhi-Qiang],
A Novel perspective invariant feature transform for RGB-D images,
CVIU(167), 2018, pp. 109-120.
Elsevier DOI 1804
RGB-D images, Spatial invariant, Local visual feature BibRef

Cai, Z., Long, Y., Shao, L.,
Adaptive RGB Image Recognition by Visual-Depth Embedding,
IP(27), No. 5, May 2018, pp. 2471-2483.
IEEE DOI 1804
Cameras, Image recognition, Kernel, Linear programming, Probability distribution, Task analysis, Training, RGB-D data, visual categorization BibRef

Karpushin, M.[Maxim], Valenzise, G.[Giuseppe], Dufaux, F.[Frédéric],
TRISK: A local features extraction framework for texture-plus-depth content matching,
IVC(71), 2018, pp. 1-16.
Elsevier DOI 1804
Texture-plus-depth, RGBD, Local feature, Keypoint detector, Descriptor, Viewpoint changes BibRef

Junejo, I.N.[Imran N.], Ahmed, N.[Naveed],
Foreground extraction for freely moving RGBD cameras,
IET-CV(12), No. 3, April 2018, pp. 322-331.
DOI Link 1804
BibRef

Slavcheva, M.[Miroslava], Kehl, W.[Wadim], Navab, N.[Nassir], Ilic, S.[Slobodan],
SDF-2-SDF Registration for Real-Time 3D Reconstruction from RGB-D Data,
IJCV(126), No. 6, June 2018, pp. 615-636.
Springer DOI 1804
BibRef
Earlier:
SDF-2-SDF: Highly Accurate 3D Object Reconstruction,
ECCV16(I: 680-696).
Springer DOI 1611
3D from RGB-D BibRef

Slavcheva, M.[Miroslava], Ilic, S.[Slobodan],
SDF-TAR: Parallel Tracking and Refinement in RGB-D Data using Volumetric Registration,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Guo, Y.R.[Yan-Rong], Chen, T.[Tao],
Semantic segmentation of RGBD images based on deep depth regression,
PRL(109), 2018, pp. 55-64.
Elsevier DOI 1806
Deep depth regression, RGBD semantic segmentation, Convolutional neural network, Fully convolutional network BibRef

Lu, F.X.[Fei-Xiang], Zhou, B.[Bin], Zhang, Y.[Yu], Zhao, Q.P.[Qin-Ping],
Real-time 3D scene reconstruction with dynamically moving object using a single depth camera,
VC(34), No. 6-8, June 2018, pp. 753-763.
Springer DOI 1806
Moving scanner. BibRef

Asif, U.[Umar], Bennamoun, M.[Mohammed], Sohel, F.A.[Ferdous A.],
A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling,
PAMI(40), No. 9, September 2018, pp. 2051-2065.
IEEE DOI 1808
BibRef
Earlier:
Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects,
ECCV14(V: 659-674).
Springer DOI 1408
Labeling, Solid modeling, Proposals, Semantics, Image reconstruction, Computational modeling, semantic segmentation BibRef

Belter, D.[Dominik], Nowicki, M.[Michal], Skrzypczynski, P.[Piotr],
Modeling spatial uncertainty of point features in feature-based RGB-D SLAM,
MVA(29), No. 5, July 2018, pp. 827-844.
WWW Link. 1808
BibRef

Lin, Y.[Yangbin], Wang, C.[Cheng], Zhai, D.[Dawei], Li, W.[Wei], Li, J.[Jonathan],
Toward better boundary preserved supervoxel segmentation for 3D point clouds,
PandRS(143), 2018, pp. 39-47.
Elsevier DOI 1808
Supervoxel segmentation, Point clouds, Subset selection, Over-segmentation BibRef

Kang, Z.Z.[Zhi-Zhong], Yang, J.[Juntao],
A probabilistic graphical model for the classification of mobile LiDAR point clouds,
PandRS(143), 2018, pp. 108-123.
Elsevier DOI 1808
Mobile LiDAR, Probabilistic graphical model, Classification, Super-voxelization, Latent Dirichlet allocation BibRef

Li, M.[Minglei], Sun, C.M.[Chang-Ming],
Refinement of LiDAR point clouds using a super voxel based approach,
PandRS(143), 2018, pp. 213-221.
Elsevier DOI 1808
Point cloud, Octree, Super voxel, Data refinement BibRef

Sun, Y.[Ying], Zhang, X.C.[Xin-Chang], Xin, Q.C.[Qin-Chuan], Huang, J.F.[Jian-Feng],
Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data,
PandRS(143), 2018, pp. 3-14.
Elsevier DOI 1808
LiDAR, High-resolution imagery, Multi-modal fusion, Multi-resolution segmentation, Semantic segmentation BibRef

Jeong, S.H.[Seung-Hwa], Lee, J.J.[Jung-Jin], Kim, B.[Bumki], Kim, Y.[Young_Hui], Noh, J.Y.[Jun-Yong],
Object Segmentation Ensuring Consistency Across Multi-Viewpoint Images,
PAMI(40), No. 10, October 2018, pp. 2455-2468.
IEEE DOI 1809
Cameras, Image color analysis, Image segmentation, Object segmentation, Optimization, depth projection. RGB-D Camera. Shape from motion. BibRef


Kaiser, A.[Adrien], Zepeda, J.A.Y.[Jose Alonso Ybanez], Boubekeur, T.[Tamy],
Proxy Clouds for Live RGB-D Stream Processing and Consolidation,
ECCV18(VI: 255-271).
Springer DOI 1810
BibRef

Kim, P.[Pyojin], Coltin, B.[Brian], Kim, H.J.[H. Jin],
Linear RGB-D SLAM for Planar Environments,
ECCV18(II: 350-366).
Springer DOI 1810
BibRef

Shi, Y.[Yifei], Xu, K.[Kai], Nießner, M.[Matthias], Rusinkiewicz, S.[Szymon], Funkhouser, T.[Thomas],
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction,
ECCV18(VIII: 767-784).
Springer DOI 1810
BibRef

Stefanczyk, M.[Maciej],
Improving RGB Descriptors Using Depth Cues,
ICCVG18(251-262).
Springer DOI 1810
BibRef

Wang, W.[Weiyue], Neumann, U.[Ulrich],
Depth-Aware CNN for RGB-D Segmentation,
ECCV18(XI: 144-161).
Springer DOI 1810
BibRef

Yi, R., Liu, Y., Lai, Y.,
Evaluation on the Compactness of Supervoxels,
ICIP18(2212-2216)
IEEE DOI 1809
Measurement, Videos, Shape, Correlation, Spatiotemporal phenomena, Complexity theory, Color, Supervoxel, compactness, metric evaluation BibRef

Siddiqua, A.[Ayesha], Fan, G.L.[Guo-Liang],
Supervised Deep-Autoencoder for Depth Image-Based 3D Model Retrieval,
WACV18(939-946)
IEEE DOI 1806
feature extraction, image classification, image coding, image retrieval, image segmentation, Training BibRef

Guo, L.[Lin], Fan, G.L.[Guo-Liang], Sheng, W.[Weihua],
Robust object detection by cuboid matching with local plane optimization in indoor RGB-D images,
VCIP17(1-4)
IEEE DOI 1804
geometry, image colour analysis, object detection, optimisation, cuboid candidates, cuboid initialization, indoor scene BibRef

Aakerberg, A., Nasrollahi, K., Heder, T.,
Improving a deep learning based RGB-D object recognition model by ensemble learning,
IPTA17(1-6)
IEEE DOI 1804
convolution, feedforward neural nets, image colour analysis, learning (artificial intelligence), object recognition, RGB-D BibRef

Chen, C.F., Bolas, M., Rosenberg, E.S.,
View-dependent virtual reality content from RGB-D images,
ICIP17(2931-2935)
IEEE DOI 1803
Cameras, Color, Computational modeling, Image color analysis, Rendering (computer graphics), Solid modeling, Virtual Reality BibRef

Li, Y., Zhang, J., Cheng, Y., Huang, K., Tan, T.,
Semantics-guided multi-level RGB-D feature fusion for indoor semantic segmentation,
ICIP17(1262-1266)
IEEE DOI 1803
Feature extraction, Fuses, Image segmentation, Legged locomotion, Semantics, Streaming media, Sun, Indoor semantic segmentation, RGB-D BibRef

Fu, X., Chen, C., Li, J., Wang, C., Kuo, C.C.J.,
Image segmentation using contour, surface, and depth cues,
ICIP17(81-85)
IEEE DOI 1803
Copper, Estimation, Image edge detection, Image segmentation, Reliability, Spectral Graph BibRef

Trabelsi, R.[Rim], Jabri, I.[Issam], Melgani, F.[Farid], Smach, F.[Fethi], Conci, N.[Nicola], Bouallegue, A.[Ammar],
Complex-Valued Representation for RGB-D Object Recognition,
PSIVT17(17-27).
Springer DOI 1802
BibRef

Zia, S., Yüksel, B., Yüret, D., Yemez, Y.,
RGB-D Object Recognition Using Deep Convolutional Neural Networks,
DeepLearn-G17(887-894)
IEEE DOI 1802
Feature extraction, Image color analysis, Object recognition, BibRef

Lahoud, J., Ghanem, B.,
2D-Driven 3D Object Detection in RGB-D Images,
ICCV17(4632-4640)
IEEE DOI 1802
image colour analysis, learning (artificial intelligence), multilayer perceptrons, object detection, search problems, BibRef

Lin, D., Chen, G., Cohen-Or, D., Heng, P.A., Huang, H.,
Cascaded Feature Network for Semantic Segmentation of RGB-D Images,
ICCV17(1320-1328)
IEEE DOI 1802
feature extraction, feedforward neural nets, image colour analysis, image representation, image segmentation, Visualization BibRef

Ahmadi, S.S., Khotanlou, H.,
Enhance support relation extraction accuracy using improvement of segmentation in RGB-D images,
IPRIA17(166-169)
IEEE DOI 1712
computer vision, feature extraction, image colour analysis, image enhancement, image segmentation, Support relation extraction BibRef

Chen, H.[Hao], Li, Y.F.[You-Fu], Su, D.[Dan],
RGB-D Saliency Detection by Multi-stream Late Fusion Network,
CVS17(459-468).
Springer DOI 1711
BibRef

Fan, H.[Heng], Mei, X.[Xue], Prokhorov, D.[Danil], Ling, H.B.[Hai-Bin],
RGB-D Scene Labeling with Multimodal Recurrent Neural Networks,
PBVS17(203-211)
IEEE DOI 1709
Computational modeling, Computer vision, Correlation, Feature extraction, Labeling, Recurrent neural networks, Semantics BibRef

Hodan, T.[Tomáš], Haluza, P.[Pavel], Obdržálek, Š.[Štepán], Matas, J.[Jirí], Lourakis, M.[Manolis], Zabulis, X.[Xenophon],
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects,
WACV17(880-888)
IEEE DOI 1609
Dataset, RBG-D.
WWW Link. (Slow response) Image color analysis, Image sensors, Pose estimation, Sensors, Solid modeling, Three-dimensional displays, Training BibRef

Perez-Yus, A.[Alejandro], Bermudez-Cameo, J.[Jesus], Guerrero, J.J.[Jose J.], Lopez-Nicolas, G.[Gonzalo],
Depth and Motion Cues with Phosphene Patterns for Prosthetic Vision,
ACVR17(1516-1525)
IEEE DOI 1802
Cameras, Image resolution, Navigation, Prosthetics, Visualization BibRef

Bermudez-Cameo, J.[Jesus], Badias-Herbera, A.[Alberto], Guerrero-Viu, M.[Manuel], Lopez-Nicolas, G.[Gonzalo], Guerrero, J.J.[Jose J.],
RGB-D Computer Vision Techniques for Simulated Prosthetic Vision,
IbPRIA17(427-436).
Springer DOI 1706
BibRef

Audet, F.[François], Allili, M.S.[Mohand Said], Cretu, A.M.[Ana-Maria],
Salient Object Detection in Images by Combining Objectness Clues in the RGBD Space,
ICIAR17(247-255).
Springer DOI 1706
BibRef

Wang, S.T.[Song-Tao], Zhou, Z.[Zhen], Qu, H.B.[Han-Bing], Li, B.[Bin],
RGB-D saliency detection under Bayesian framework,
ICPR16(1881-1886)
IEEE DOI 1705
Computational modeling, Feature extraction, Image color analysis, Solid modeling, Three-dimensional displays, Two dimensional displays, Visualization BibRef

Chen, B., Yang, J.H.[Jian-Hao], Ding, M.[Mengru], Liu, T.L.[Tian-Liang], Zhang, X.P.[Xin-Peng],
Quaternion-type moments combining both color and depth information for RGB-D object recognition,
ICPR16(704-708)
IEEE DOI 1705
Color, Face, Image color analysis, Neurons, Object recognition, Quaternions, Redundancy, RGB-D object recognition, color image, depth information, quaternion, moment BibRef

Pang, G.[Guan], Neumann, U.[Ulrich],
3D point cloud object detection with multi-view convolutional neural network,
ICPR16(585-590)
IEEE DOI 1705
Complexity theory, Detectors, Object detection, Search problems, Three-dimensional displays, Training, Two dimensional displays. 3D object extraction from point clouds. BibRef

Takabe, A., Takehara, H., Kawai, N., Sato, T., Machida, T., Nakanishi, S., Yokoya, N.,
Moving object detection from a point cloud using photometric and depth consistencies,
ICPR16(561-566)
IEEE DOI 1705
Cameras, Data models, Histograms, Measurement by laser beam, Object detection, Solid modeling, Three-dimensional, displays BibRef

Wang, S.T.[Song-Tao], Zhou, Z.[Zhen], Qu, H.B.[Han-Bing], Li, B.[Bin],
Visual Saliency Detection for RGB-D Images with Generative Model,
ACCV16(V: 20-35).
Springer DOI 1704
BibRef

Yang, C.L.[Cheng-Liang], Sethi, M.[Manu], Rangarajan, A.[Anand], Ranka, S.[Sanjay],
Supervoxel-Based Segmentation of 3D Volumetric Images,
ACCV16(I: 37-53).
Springer DOI 1704
BibRef

Drozdov, G.[Gilad], Shapiro, Y.[Yevgengy], Gilboa, G.[Guy],
Robust Recovery of Heavily Degraded Depth Measurements,
3DV16(56-65)
IEEE DOI 1701
image colour analysis BibRef

Seychell, D.[Dylan], Debono, C.J.[Carl James],
Efficient object selection using depth and texture information,
VCIP16(1-4)
IEEE DOI 1701
Image color analysis BibRef

Lombardi, S., Nishino, K.,
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images,
3DV16(305-313)
IEEE DOI 1701
computer vision BibRef

Li, S., Handa, A., Zhang, Y., Calway, A.,
HDRFusion: HDR SLAM Using a Low-Cost Auto-Exposure RGB-D Sensor,
3DV16(314-322)
IEEE DOI 1701
SLAM (robots) BibRef

Georgakis, G., Reza, M.A., Mousavian, A., Le, P.H., KošeckŽá, J.,
Multiview RGB-D Dataset for Object Instance Detection,
3DV16(426-434)
IEEE DOI 1701
Clutter BibRef

Peng, H.[Houwen], Li, B.[Bing], Xiong, W.H.[Wei-Hua], Hu, W.M.[Wei-Ming], Ji, R.R.[Rong-Rong],
RGBD Salient Object Detection: A Benchmark and Algorithms,
ECCV14(III: 92-109).
Springer DOI 1408
BibRef

Ren, Z.[Zhile], Sudderth, E.B.[Erik B.],
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients,
CVPR16(1525-1533)
IEEE DOI 1612
BibRef

Shigematsu, R., Feng, D., You, S., Barnes, N.[Nick],
Learning RGB-D Salient Object Detection Using Background Enclosure, Depth Contrast, and Top-Down Features,
CogCV17(2749-2757)
IEEE DOI 1802
Computer architecture, Feature extraction, Histograms, Image color analysis, Microprocessors, Object detection, Visualization BibRef

Feng, D., Barnes, N., You, S.,
HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection,
DICTA17(1-8)
IEEE DOI 1804
computer vision, graph theory, image colour analysis, object detection, HOSO, KDF, RGB-D data, RGB-D saliency, Three-dimensional displays BibRef

Feng, D., Barnes, N.[Nick], You, S., McCarthy, C.[Chris],
Local Background Enclosure for RGB-D Salient Object Detection,
CVPR16(2343-2350)
IEEE DOI 1612
BibRef

Liu, C.[Chen], Kohli, P.[Pushmeet], Furukawa, Y.[Yasutaka],
Layered Scene Decomposition via the Occlusion-CRF,
CVPR16(165-173)
IEEE DOI 1612
BibRef

de Gregorio, D.[Daniele], Tombari, F.[Federico], di Stefano, L.[Luigi],
RobotFusion: Grasping with a Robotic Manipulator via Multi-view Reconstruction,
6DPose16(III: 634-647).
Springer DOI 1611
BibRef

Sharma, A.[Abhishek], Grau, O.[Oliver], Fritz, M.[Mario],
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels,
DeepLearn16(III: 236-250).
Springer DOI 1611
BibRef

Tzionas, D.[Dimitrios], Gall, J.[Juergen],
Reconstructing Articulated Rigged Models from RGB-D Videos,
6DPose16(III: 620-633).
Springer DOI 1611
BibRef

Wang, J.H.[Jing-Hua], Wang, Z.H.[Zhen-Hua], Tao, D.C.[Da-Cheng], See, S.[Simon], Wang, G.[Gang],
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks,
ECCV16(V: 664-679).
Springer DOI 1611
BibRef

Liu, X.D.[Xin-Da], Wang, X.M.[Xue-Ming], Jiang, S.Q.[Shu-Qiang],
RGB-D scene classification via heterogeneous model fusion,
ICIP16(499-503)
IEEE DOI 1610
Boolean functions BibRef

Yun, J.S., Sim, J.Y.,
Supervoxel-based saliency detection for large-scale colored 3D point clouds,
ICIP16(4062-4066)
IEEE DOI 1610
Clustering algorithms BibRef

Feng, J.[Jie], Wang, Y.[Yan], Chang, S.F.[Shih-Fu],
3D shape retrieval using a single depth image from low-cost sensors,
WACV16(1-9)
IEEE DOI 1606
Computational modeling. RGB-D sensors. BibRef

Tang, B., Zhou, Y., Yu, Y., Du, S.,
Higher-order class-specific priors for semantic segmentation of 3D outdoor scenes,
WACV16(1-9)
IEEE DOI 1606
Analytical models BibRef

Liu, S., Li, W., Ogunbona, P., Chow, Y.W.,
Creating Simplified 3D Models with High Quality Textures,
DICTA15(1-8)
IEEE DOI 1603
RGB-D data. BibRef

Zaki, H.F.M., Shafait, F., Mian, A.,
Modeling 2D Appearance Evolution for 3D Object Categorization,
DICTA16(1-8)
IEEE DOI 1701
BibRef
Earlier:
Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Wetherall, J., Taylor, M., Hurley-Smith, D.,
Investigation into the effects of transmission-channel fidelity loss in RGBD sensor data for SLAM,
WSSIP15(81-84)
IEEE DOI 1603
SLAM (robots) BibRef

Fooladgar, F., Kasaei, S.,
Semantic Segmentation of RGB-D Images Using 3D and Local Neighbouring Features,
DICTA15(1-7)
IEEE DOI 1603
computer vision BibRef

Pham, T.T.[Trung T.], Reid, I.D.[Ian D.], Latif, Y.[Yasir], Gould, S.[Stephen],
Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling,
ICCV15(2246-2254)
IEEE DOI 1602
RGB-D. Computational modeling BibRef

Cheng, Y., Cai, R., Zhang, C., Li, Z., Zhao, X., Huang, K., Rui, Y.,
Query Adaptive Similarity Measure for RGB-D Object Recognition,
ICCV15(145-153)
IEEE DOI 1602
Art BibRef

Hachama, M., Ghanem, B., Wonka, P.,
Intrinsic Scene Decomposition from RGB-D Images,
ICCV15(810-818)
IEEE DOI 1602
Coherence BibRef

Chandra, S.[Siddhartha], Chrysos, G.G.[Grigorios G.], Kokkinos, I.[Iasonas],
Surface Based Object Detection in RGBD Images,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Soni, N.[Nishit], Namboodiri, A.M.[Anoop M.], Jawahar, C.V., Ramalingam, S.[Srikumar],
Semantic Classification of Boundaries of an RGBD Image,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Liu, J.[Jing], Ren, T.[Tongwei], Bei, J.[Jia],
Elastic Edge Boxes for Object Proposal on RGB-D Images,
MMMod16(I: 199-211).
Springer DOI 1601
BibRef

Xue, H.Y.[Hao-Yang], Gu, Y.[Yun], Li, Y.J.[Yi-Jun], Yang, J.[Jie],
RGB-D saliency detection via mutual guided manifold ranking,
ICIP15(666-670)
IEEE DOI 1512
Depth map cues; Mutual guided manifold ranking; Saliency detection BibRef

Petrelli, A.[Alioscia], di Stefano, L.[Luigi],
Learning to Weight Color and Depth for RGB-D Visual Search,
CIAP17(I:648-659).
Springer DOI 1711
BibRef

Petrelli, A.[Alioscia], Pau, D.[Danilo], di Stefano, L.[Luigi],
Analysis of Compact Features for RGB-D Visual Search,
CIAP15(II:14-24).
Springer DOI 1511
BibRef

Geiger, A.[Andreas], Wang, C.[Chaohui],
Joint 3D Object and Layout Inference from a Single RGB-D Image,
GCPR15(183-195).
Springer DOI 1511
BibRef

Nakaguro, Y.[Yoichi], Qureshi, W.S.[Waqar S.], Dailey, M.N.[Matthew N.], Ekpanyapong, M.[Mongkol], Bunnun, P.[Pished], Tungpimolrut, K.[Kanokvate],
Volumetric 3D Reconstruction and Parametric Shape Modeling from RGB-D Sequences,
CIAP15(I:500-516).
Springer DOI 1511
BibRef

Zhu, C.[Chen], Bilgeri, S.[Simon], Günther, C.[Christoph],
Spatial Uncertainty Model of a Three-View RGB-D Camera System,
ISVC14(II: 117-128).
Springer DOI 1501
Combine 2 view stereo with RBG-D for more complete modeling. BibRef

Fu, H.Z.[Hua-Zhu], Xu, D.[Dong], Lin, S.[Stephen], Liu, J.[Jiang],
Object-based RGBD image co-segmentation with mutex constraint,
CVPR15(4428-4436)
IEEE DOI 1510
BibRef

Banica, D.[Dan], Sminchisescu, C.[Cristian],
Second-order constrained parametric proposals and sequential search-based structured prediction for semantic segmentation in RGB-D images,
CVPR15(3517-3526)
IEEE DOI 1510
BibRef

Cinque, L.[Luigi], Danani, A.[Alessandro], Dondi, P.[Piercarlo], Lombardi, L.[Luca],
Real-Time Foreground Segmentation with Kinect Sensor,
CIAP15(II:56-65).
Springer DOI 1511
BibRef

Wan, S.H.[Shao-Hua], Aggarwal, J.K.,
Robust object recognition in RGB-D egocentric videos based on Sparse Affine Hull Kernel,
PBVS15(97-104)
IEEE DOI 1510
Cameras BibRef

Ren, J.[Jianqiang], Gong, X.J.[Xiao-Jin], Yu, L.[Lu], Zhou, W.H.[Wen-Hui], Yang, M.Y.[Michael Ying],
Exploiting global priors for RGB-D saliency detection,
FusionDynamic15(25-32)
IEEE DOI 1510
Image color analysis BibRef

Liu, W.[Wei], Ji, R.R.[Rong-Rong], Li, S.[Shaozi],
Towards 3D object detection with bimodal deep Boltzmann machines over RGBD imagery,
CVPR15(3013-3021)
IEEE DOI 1510
BibRef

Diebold, J.[Julia], Demmel, N.[Nikolaus], Hazirbas, C.[Caner], Moeller, M.[Michael], Cremers, D.[Daniel],
Interactive Multi-label Segmentation of RGB-D Images,
SSVM15(294-306).
Springer DOI 1506
BibRef

Nghiem, A.T.[Anh-Tuan], Bremond, F.[Francois],
Background subtraction in people detection framework for RGB-D cameras,
AVSS14(241-246)
IEEE DOI 1411
Cameras BibRef

Trabelsi, R.[Rim], Smach, F.[Fethi], Jabri, I.[Issam], Bouallegue, A.[Ammar],
Multimodal Background Modeling Using RGB-Depth Features,
CIARP14(884-892).
Springer DOI 1411
BibRef

Hickson, S.[Steven], Essa, I.[Irfan], Christensen, H.[Henrik],
Semantic Instance Labeling Leveraging Hierarchical Segmentation,
WACV15(1068-1075)
IEEE DOI 1503
Accuracy BibRef

Hickson, S.[Steven], Birchfield, S.[Stan], Essa, I.[Irfan], Christensen, H.[Henrik],
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos,
CVPR14(344-351)
IEEE DOI 1409
4D Segmentation BibRef

Barrera, F.[Fernando], Padoy, N.[Nicolas],
Piecewise Planar Decomposition of 3D Point Clouds Obtained from Multiple Static RGB-D Cameras,
3DV14(194-201)
IEEE DOI 1503
Cameras BibRef

Lim, H.[Hyon], Lim, J.W.[Jong-Woo], Kim, H.J.[H. Jin],
Online 3D Reconstruction and 6-DoF Pose Estimation for RGB-D Sensors,
CVVT14(238-254).
Springer DOI 1504
BibRef

Zhang, Q.S.[Quan-Shi], Song, X.[Xuan], Shao, X.W.[Xiao-Wei], Zhao, H.J.[Hui-Jing], Shibasaki, R.[Ryosuke],
When 3D Reconstruction Meets Ubiquitous RGB-D Images,
CVPR14(700-707)
IEEE DOI 1409
3D reconstruction. Using category models. Rotation, 3D and texture. BibRef

Kerl, C.[Christian], Souiai, M.[Mohamed], Sturm, J.[Jurgen], Cremers, D.[Daniel],
Towards Illumination-Invariant 3D Reconstruction Using ToF RGB-D Cameras,
3DV14(39-46)
IEEE DOI 1503
Cameras BibRef

Perera, S.[Samunda], Barnes, N.[Nick],
1-Point Rigid Motion Estimation and Segmentation with a RGB-D Camera,
DICTA13(1-8)
IEEE DOI 1402
BibRef
Earlier:
Maximal Cliques Based Rigid Body Motion Segmentation with a RGB-D Camera,
ACCV12(II:120-133).
Springer DOI 1304
BibRef
Earlier:
A Simple and Practical Solution to the Rigid Body Motion Segmentation Problem Using a RGB-D Camera,
DICTA11(494-500).
IEEE DOI 1205
cameras BibRef

Steinbrucker, F.[Frank], Kerl, C.[Christian], Cremers, D.[Daniel],
Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences,
ICCV13(3264-3271)
IEEE DOI 1403
BibRef

Weikersdorfer, D.[David], Schick, A.[Alexander], Cremers, D.[Daniel],
Depth-adaptive supervoxels for RGB-D video segmentation,
ICIP13(2708-2712)
IEEE DOI 1402
RGB-D; Superpixels; Supervoxels; Video Analysis; Video Segmentation BibRef

Guan, L.[Li], Yu, T.[Ting], Tu, P.[Peter], Lim, S.N.[Ser-Nam],
Simultaneous image segmentation and 3D plane fitting for RGB-D sensors: An iterative framework,
PCP12(49-56).
IEEE DOI 1207
BibRef

Srinivasan, N.[Natesh], Dellaert, F.[Frank],
A Rao-Blackwellized MCMC algorithm for recovering piecewise planar 3D models from multiple view RGBD images,
ICIP14(5392-5392)
IEEE DOI 1502
Bayes methods BibRef

Dellaert, F.[Frank],
Factor Graphs for Fast and Scalable 3D Reconstruction and Mapping,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Erdogan, C.[Can], Paluri, M.[Manohar], Dellaert, F.[Frank],
Planar Segmentation of RGBD Images Using Fast Linear Fitting and Markov Chain Monte Carlo,
CRV12(32-39).
IEEE DOI 1207
BibRef

Weikersdorfer, D.[David], Gossow, D.[David], Beetz, M.[Michael],
Depth-adaptive superpixels,
ICPR12(2087-2090).
WWW Link. 1302
BibRef

Kim, B.S.[Byung-Soo], Kohli, P.[Pushmeet], Savarese, S.[Silvio],
3D Scene Understanding by Voxel-CRF,
ICCV13(1425-1432)
IEEE DOI 1403
3D reconstruction; RGB-D; Scene understanding BibRef

Sallem, N.K.[Nizar K.], Devy, M.[Michel],
Extended GrabCut for 3D and RGB-D Point Clouds,
ACIVS13(354-365).
Springer DOI 1311
BibRef

Specht, A.R., Devy, M.,
Surface segmentation using a modified ball-pivoting algorithm,
ICIP04(III: 1931-1934).
IEEE DOI 0505
BibRef

Sappa, A.D., Devy, M.,
Fast range image segmentation by an edge detection strategy,
3DIM01(292-299).
IEEE DOI 0106
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
The Facet Model for Descriptions .


Last update:Oct 15, 2018 at 09:19:25