12.1.4.6 Fusion, General Multi-Modal

Chapter Contents (Back)
Fusion. Sensor Fusion. Multi-Modal.

Rogelj, P.[Peter], Kovacic, S.[Stanislav], Gee, J.C.[James C.],
Point similarity measures for non-rigid registration of multi-modal data,
CVIU(92), No. 1, October 2003, pp. 112-140.
Elsevier DOI 0310
BibRef

Hasan, M.[Mahmudul], Pickering, M.R.[Mark R.], Jia, X.P.[Xiu-Ping],
Robust Automatic Registration of Multimodal Satellite Images Using CCRE With Partial Volume Interpolation,
GeoRS(50), No. 10, October 2012, pp. 4050-4061.
IEEE DOI 1210
BibRef
Earlier:
Multi-modal Registration of SAR and Optical Satellite Images,
DICTA09(447-453).
IEEE DOI 0912
BibRef

Sutour, C.[Camille], Aujol, J.F.[Jean-François], Deledalle, C.A.[Charles-Alban], de Senneville, B.D.[Baudouin Denis],
Edge-Based Multi-modal Registration and Application for Night Vision Devices,
JMIV(53), No. 2, October 2015, pp. 131-150.
Springer DOI 1508
BibRef

Yu, J.G.[Jin-Gang], Gao, C.X.[Chang-Xin], Tian, J.W.[Jin-Wen],
Collaborative multicue fusion using the cross-diffusion process for salient object detection,
JOSA-A(33), No. 3, March 2016, pp. 404-415.
DOI Link 1603
Digital image processing BibRef

Pitts, B., Riggs, S.L., Sarter, N.,
Crossmodal Matching: A Critical but Neglected Step in Multimodal Research,
HMS(46), No. 3, June 2016, pp. 445-450.
IEEE DOI 1605
Equating perceived intensities of stimuli across two sensory modalities. BibRef

Wang, K.[Kaiye], He, R.[Ran], Wang, L.[Liang], Wang, W.[Wei], Tan, T.N.[Tie-Niu],
Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval,
PAMI(38), No. 10, October 2016, pp. 2010-2023.
IEEE DOI 1609
BibRef
Earlier: A1, A2, A4, A3, A5:
Learning Coupled Feature Spaces for Cross-Modal Matching,
ICCV13(2088-2095)
IEEE DOI 1403
BibRef
And: A1, A4, A2, A3, A5:
Multi-modal Subspace Learning with Joint Graph Regularization for Cross-Modal Retrieval,
ACPR13(236-240)
IEEE DOI 1408
Buildings. graph theory BibRef

Li, Q.[Qi], Sun, Z.A.[Zhen-An], He, R.[Ran], Tan, T.N.[Tie-Niu],
Joint Alignment and Clustering via Low-Rank Representation,
ACPR13(591-595)
IEEE DOI 1408
image representation BibRef

Wang, K.[Kaiye], Wang, W.[Wei], Wang, L.[Liang],
Learning unified sparse representations for multi-modal data,
ICIP15(3545-3549)
IEEE DOI 1512
Cross-modal retrieval BibRef

Zu, C.[Chen], Wang, Z.X.[Zheng-Xia], Zhang, D.Q.[Dao-Qiang], Liang, P.P.[Pei-Peng], Shi, Y.H.[Yong-Hong], Shen, D.G.[Ding-Gang], Wu, G.R.[Guo-Rong],
Robust multi-atlas label propagation by deep sparse representation,
PR(63), No. 1, 2017, pp. 511-517.
Elsevier DOI 1612
Hierarchical sparse representation BibRef

Song, G.L.[Guo-Li], Wang, S.H.[Shu-Hui], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Multimodal Similarity Gaussian Process Latent Variable Model,
IP(26), No. 9, September 2017, pp. 4168-4181.
IEEE DOI 1708
BibRef
And:
Multimodal Gaussian Process Latent Variable Models with Harmonization,
ICCV17(5039-5047)
IEEE DOI 1802
BibRef
Earlier:
Similarity Gaussian Process Latent Variable Model for Multi-modal Data Analysis,
ICCV15(4050-4058)
IEEE DOI 1602
Gaussian processes, content-based retrieval, gradient methods, learning (artificial intelligence), pattern classification, cross-modal content retrieval, distance preservation, gradient descent techniques, heterogeneous modalities, BibRef

Song, G.L.[Guo-Li], Wang, S.H.[Shu-Hui], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models,
PAMI(43), No. 3, March 2021, pp. 858-872.
IEEE DOI 2102
Data models, Kernel, Correlation, Semantics, Gaussian processes, Learning systems, Probabilistic logic, Multimodal learning, cross-modal retrieval BibRef

Li, K.[Ke], Zou, C.Q.[Chang-Qing], Bu, S.[Shuhui], Liang, Y.[Yun], Zhang, J.[Jian], Gong, M.L.[Ming-Lun],
Multi-modal feature fusion for geographic image annotation,
PR(73), No. 1, 2018, pp. 1-14.
Elsevier DOI 1709
Convolutional neural networks, (CNNs) BibRef

Amer, M.R.[Mohamed R.], Shields, T.[Timothy], Siddiquie, B.[Behjat], Tamrakar, A.[Amir], Divakaran, A.[Ajay], Chai, S.[Sek],
Deep Multimodal Fusion: A Hybrid Approach,
IJCV(126), No. 2-4, April 2018, pp. 440-456.
Springer DOI 1804
BibRef

Amer, M.R.[Mohamed R.], Siddiquie, B.[Behjat], Khan, S.[Saad], Divakaran, A.[Ajay], Sawhney, H.S.[Harpreet S.],
Multimodal fusion using dynamic hybrid models,
WACV14(556-563)
IEEE DOI 1406
Computational modeling BibRef

Wang, R.[Ruili], Ji, W.T.[Wan-Ting], Liu, M.Z.[Ming-Zhe], Wang, X.[Xun], Weng, J.[Jian], Deng, S.[Song], Gao, S.Y.[Su-Ying], Yuan, C.A.[Chang-An],
Review on mining data from multiple data sources,
PRL(109), 2018, pp. 120-128.
Elsevier DOI 1806
Multiple data source mining, Pattern analysis, Data classification, Data clustering, Data fusion BibRef

Alvén, J.[Jennifer], Kahl, F.[Fredrik], Landgren, M.[Matilda], Larsson, V.[Viktor], Ulén, J.[Johannes], Enqvist, O.[Olof],
Shape-aware label fusion for multi-atlas frameworks,
PRL(124), 2019, pp. 109-117.
Elsevier DOI 1906
Multi-atlas label fusion, Shape models, Medical image segmentation BibRef

Gao, L.[Lin], Battistelli, G.[Giorgio], Chisci, L.[Luigi],
Multiobject Fusion With Minimum Information Loss,
SPLetters(27), 2020, pp. 201-205.
IEEE DOI 2002
Generalized covariance intersection, Kullback-Leibler divergence, random finite set, data fusion, linear opinion pool BibRef

Liu, R.S.[Ri-Sheng], Liu, J.Y.[Jin-Yuan], Jiang, Z.Y.[Zhi-Ying], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan],
A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion,
IP(30), 2021, pp. 1261-1274.
IEEE DOI 2012
Image fusion, Task analysis, Transforms, Optimization, Magnetic resonance imaging, Dictionaries, neural networks BibRef


Son, C.H., Zhang, X.P.,
Multimodal fusion via a series of transfers for noise removal,
ICIP17(530-534)
IEEE DOI 1803
Image representation, Imaging, Pattern recognition, Visual communication, Near-infrared imaging, multimodal fusion BibRef

Shrivastava, A.[Ashish], Rastegari, M.[Mohammad], Shekhar, S.[Sumit], Chellappa, R.[Rama], Davis, L.S.[Larry S.],
Class consistent multi-modal fusion with binary features,
CVPR15(2282-2291)
IEEE DOI 1510
BibRef

Kasiri, K.[Keyvan], Fieguth, P.W.[Paul W.], Clausi, D.A.[David A.],
Self-similarity measure for multi-modal image registration,
ICIP16(4498-4502)
IEEE DOI 1610
BibRef
Earlier:
Structural Representations for Multi-modal Image Registration Based on Modified Entropy,
ICIAR15(82-89).
Springer DOI 1507
Brain. BibRef

Glodek, M.[Michael], Schels, M.[Martin], Palm, G.[Gunther], Schwenker, F.[Friedhelm],
Multi-modal Fusion based on classifiers using reject options and Markov Fusion Networks,
ICPR12(1084-1087).
WWW Link. 1302
BibRef

Forsberg, D.[Daniel], Farnebäck, G.[Gunnar], Knutsson, H.[Hans], Westin, C.F.[Carl-Fredrik],
Multi-modal Image Registration Using Polynomial Expansion and Mutual Information,
WBIR12(40-49).
Springer DOI 1208
BibRef

Town, C.[Christopher], Zhu, Z.G.[Zhi-Gang],
Sensor Fusion and Environmental Modelling for Multimodal Sentient Computing,
MSCSAS07(1-2).
IEEE DOI 0706
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Fusion, Range or Depth and Intensity or Color Data .


Last update:Mar 3, 2021 at 15:01:44