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Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis.

IEEE transactions on pattern analysis and machine intelligence

Sandler R, Lindenbaum M.
PMID: 21263163
IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1590-602. doi: 10.1109/TPAMI.2011.18. Epub 2011 Jan 28.

Nonnegative matrix factorization (NMF) approximates a given data matrix as a product of two low-rank nonnegative matrices, usually by minimizing the L2 or the KL distance between the data matrix and the matrix product. This factorization was shown to...

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Sandler R, Lindenbaum M. Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis. IEEE Trans Pattern Anal Mach Intell. 2011;33(8):1590-602doi: 10.1109/TPAMI.2011.18.
Sandler, R., & Lindenbaum, M. (2011). Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis. IEEE transactions on pattern analysis and machine intelligence, 33(8), 1590-602. https://doi.org/10.1109/TPAMI.2011.18
Sandler, Roman, and Lindenbaum, Michael. "Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis." IEEE transactions on pattern analysis and machine intelligence vol. 33,8 (2011): 1590-602. doi: https://doi.org/10.1109/TPAMI.2011.18
Sandler R, Lindenbaum M. Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis. IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1590-602. doi: 10.1109/TPAMI.2011.18. Epub 2011 Jan 28. PMID: 21263163.
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Algorithms for 3D shape scanning with a depth camera.

IEEE transactions on pattern analysis and machine intelligence

Cui Y, Schuon S, Thrun S, Stricker D, Theobalt C.
PMID: 23520250
IEEE Trans Pattern Anal Mach Intell. 2013 May;35(5):1039-50. doi: 10.1109/TPAMI.2012.190.

We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology,...

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Cui Y, Schuon S, Thrun S, et al. Algorithms for 3D shape scanning with a depth camera. IEEE Trans Pattern Anal Mach Intell. 2013;35(5):1039-50doi: 10.1109/TPAMI.2012.190.
Cui, Y., Schuon, S., Thrun, S., Stricker, D., & Theobalt, C. (2013). Algorithms for 3D shape scanning with a depth camera. IEEE transactions on pattern analysis and machine intelligence, 35(5), 1039-50. https://doi.org/10.1109/TPAMI.2012.190
Cui, Yan, et al. "Algorithms for 3D shape scanning with a depth camera." IEEE transactions on pattern analysis and machine intelligence vol. 35,5 (2013): 1039-50. doi: https://doi.org/10.1109/TPAMI.2012.190
Cui Y, Schuon S, Thrun S, Stricker D, Theobalt C. Algorithms for 3D shape scanning with a depth camera. IEEE Trans Pattern Anal Mach Intell. 2013 May;35(5):1039-50. doi: 10.1109/TPAMI.2012.190. PMID: 23520250.
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An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

IEEE transactions on pattern analysis and machine intelligence

Hu W, Li X, Tian G, Maybank S, Zhang Z.
PMID: 23520251
IEEE Trans Pattern Anal Mach Intell. 2013 May;35(5):1051-65. doi: 10.1109/TPAMI.2012.188.

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose...

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Hu W, Li X, Tian G, et al. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans Pattern Anal Mach Intell. 2013;35(5):1051-65doi: 10.1109/TPAMI.2012.188.
Hu, W., Li, X., Tian, G., Maybank, S., & Zhang, Z. (2013). An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE transactions on pattern analysis and machine intelligence, 35(5), 1051-65. https://doi.org/10.1109/TPAMI.2012.188
Hu, Weiming, et al. "An incremental DPMM-based method for trajectory clustering, modeling, and retrieval." IEEE transactions on pattern analysis and machine intelligence vol. 35,5 (2013): 1051-65. doi: https://doi.org/10.1109/TPAMI.2012.188
Hu W, Li X, Tian G, Maybank S, Zhang Z. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans Pattern Anal Mach Intell. 2013 May;35(5):1051-65. doi: 10.1109/TPAMI.2012.188. PMID: 23520251.
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Multiple Object Tracking Using K-Shortest Paths Optimization.

IEEE transactions on pattern analysis and machine intelligence

Berclaz J, Fleuret F, Türetken E, Fua P.
PMID: 21282851
IEEE Trans Pattern Anal Mach Intell. 2011 Sep;33(9):1806-19. doi: 10.1109/TPAMI.2011.21. Epub 2011 Feb 04.

Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame...

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Berclaz J, Fleuret F, Türetken E, et al. Multiple Object Tracking Using K-Shortest Paths Optimization. IEEE Trans Pattern Anal Mach Intell. 2011;33(9):1806-19doi: 10.1109/TPAMI.2011.21.
Berclaz, J., Fleuret, F., Türetken, E., & Fua, P. (2011). Multiple Object Tracking Using K-Shortest Paths Optimization. IEEE transactions on pattern analysis and machine intelligence, 33(9), 1806-19. https://doi.org/10.1109/TPAMI.2011.21
Berclaz, Jérôme, et al. "Multiple Object Tracking Using K-Shortest Paths Optimization." IEEE transactions on pattern analysis and machine intelligence vol. 33,9 (2011): 1806-19. doi: https://doi.org/10.1109/TPAMI.2011.21
Berclaz J, Fleuret F, Türetken E, Fua P. Multiple Object Tracking Using K-Shortest Paths Optimization. IEEE Trans Pattern Anal Mach Intell. 2011 Sep;33(9):1806-19. doi: 10.1109/TPAMI.2011.21. Epub 2011 Feb 04. PMID: 21282851.
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Dynamic Refraction Stereo.

IEEE transactions on pattern analysis and machine intelligence

Morris NJ, Kutulakos KN.
PMID: 21282852
IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1518-31. doi: 10.1109/TPAMI.2011.24. Epub 2011 Feb 04.

In this paper we consider the problem of reconstructing the 3D position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even...

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Morris NJ, Kutulakos KN. Dynamic Refraction Stereo. IEEE Trans Pattern Anal Mach Intell. 2011;33(8):1518-31doi: 10.1109/TPAMI.2011.24.
Morris, N. J., & Kutulakos, K. N. (2011). Dynamic Refraction Stereo. IEEE transactions on pattern analysis and machine intelligence, 33(8), 1518-31. https://doi.org/10.1109/TPAMI.2011.24
Morris, Nigel J W, and Kutulakos, Kiriakos N. "Dynamic Refraction Stereo." IEEE transactions on pattern analysis and machine intelligence vol. 33,8 (2011): 1518-31. doi: https://doi.org/10.1109/TPAMI.2011.24
Morris NJ, Kutulakos KN. Dynamic Refraction Stereo. IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1518-31. doi: 10.1109/TPAMI.2011.24. Epub 2011 Feb 04. PMID: 21282852.
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Active Learning Based on Locally Linear Reconstruction.

IEEE transactions on pattern analysis and machine intelligence

Zhang L, Chen C, Bu J, Cai D, He X, Huang TS.
PMID: 21282854
IEEE Trans Pattern Anal Mach Intell. 2011 Oct;33(10):2026-38. doi: 10.1109/TPAMI.2011.20. Epub 2011 Feb 04.

We consider the active learning problem, which aims to select the most representative points. Out of many existing active learning techniques, optimum experimental design (OED) has received considerable attention recently. The typical OED criteria minimize the variance of the...

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Zhang L, Chen C, Bu J, et al. Active Learning Based on Locally Linear Reconstruction. IEEE Trans Pattern Anal Mach Intell. 2011;33(10):2026-38doi: 10.1109/TPAMI.2011.20.
Zhang, L., Chen, C., Bu, J., Cai, D., He, X., & Huang, T. S. (2011). Active Learning Based on Locally Linear Reconstruction. IEEE transactions on pattern analysis and machine intelligence, 33(10), 2026-38. https://doi.org/10.1109/TPAMI.2011.20
Zhang, Lijun, et al. "Active Learning Based on Locally Linear Reconstruction." IEEE transactions on pattern analysis and machine intelligence vol. 33,10 (2011): 2026-38. doi: https://doi.org/10.1109/TPAMI.2011.20
Zhang L, Chen C, Bu J, Cai D, He X, Huang TS. Active Learning Based on Locally Linear Reconstruction. IEEE Trans Pattern Anal Mach Intell. 2011 Oct;33(10):2026-38. doi: 10.1109/TPAMI.2011.20. Epub 2011 Feb 04. PMID: 21282854.
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Real-Time Head Pose Tracking with Online Face Template Reconstruction.

IEEE transactions on pattern analysis and machine intelligence

Li S, Ngan KN, Paramesran R, Sheng L.
PMID: 26584487
IEEE Trans Pattern Anal Mach Intell. 2016 Sep;38(9):1922-8. doi: 10.1109/TPAMI.2015.2500221. Epub 2015 Nov 12.

We propose a real-time method to accurately track the human head pose in the 3-dimensional (3D) world. Using a RGB-Depth camera, a face template is reconstructed by fitting a 3D morphable face model, and the head pose is determined...

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Li S, Ngan KN, Paramesran R, et al. Real-Time Head Pose Tracking with Online Face Template Reconstruction. IEEE Trans Pattern Anal Mach Intell. 2015;38(9):1922-8doi: 10.1109/TPAMI.2015.2500221.
Li, S., Ngan, K. N., Paramesran, R., & Sheng, L. (2016). Real-Time Head Pose Tracking with Online Face Template Reconstruction. IEEE transactions on pattern analysis and machine intelligence, 38(9), 1922-8. https://doi.org/10.1109/TPAMI.2015.2500221
Li, Songnan, et al. "Real-Time Head Pose Tracking with Online Face Template Reconstruction." IEEE transactions on pattern analysis and machine intelligence vol. 38,9 (2016): 1922-8. doi: https://doi.org/10.1109/TPAMI.2015.2500221
Li S, Ngan KN, Paramesran R, Sheng L. Real-Time Head Pose Tracking with Online Face Template Reconstruction. IEEE Trans Pattern Anal Mach Intell. 2016 Sep;38(9):1922-8. doi: 10.1109/TPAMI.2015.2500221. Epub 2015 Nov 12. PMID: 26584487.
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Connected Filtering on Tree-Based Shape-Spaces.

IEEE transactions on pattern analysis and machine intelligence

Xu Y, Geraud T, Najman L.
PMID: 26415150
IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1126-40. doi: 10.1109/TPAMI.2015.2441070. Epub 2015 Sep 22.

Connected filters are well-known for their good contour preservation property. A popular implementation strategy relies on tree-based image representations: for example, one can compute an attribute characterizing the connected component represented by each node of the tree and keep...

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Xu Y, Geraud T, Najman L. Connected Filtering on Tree-Based Shape-Spaces. IEEE Trans Pattern Anal Mach Intell. 2015;38(6):1126-40doi: 10.1109/TPAMI.2015.2441070.
Xu, Y., Geraud, T., & Najman, L. (2016). Connected Filtering on Tree-Based Shape-Spaces. IEEE transactions on pattern analysis and machine intelligence, 38(6), 1126-40. https://doi.org/10.1109/TPAMI.2015.2441070
Xu, Yongchao, et al. "Connected Filtering on Tree-Based Shape-Spaces." IEEE transactions on pattern analysis and machine intelligence vol. 38,6 (2016): 1126-40. doi: https://doi.org/10.1109/TPAMI.2015.2441070
Xu Y, Geraud T, Najman L. Connected Filtering on Tree-Based Shape-Spaces. IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1126-40. doi: 10.1109/TPAMI.2015.2441070. Epub 2015 Sep 22. PMID: 26415150.
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Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search.

IEEE transactions on pattern analysis and machine intelligence

Inoue N, Shinoda K.
PMID: 26415151
IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1170-84. doi: 10.1109/TPAMI.2015.2481390. Epub 2015 Sep 23.

Searching for matches to high-dimensional vectors using hard/soft vector quantization is the most computationally expensive part of various computer vision algorithms including the bag of visual word (BoW). This paper proposes a fast computation method, Neighbor-to-Neighbor (NTN) search [1]...

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Inoue N, Shinoda K. Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search. IEEE Trans Pattern Anal Mach Intell. 2015;38(6):1170-84doi: 10.1109/TPAMI.2015.2481390.
Inoue, N., & Shinoda, K. (2016). Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search. IEEE transactions on pattern analysis and machine intelligence, 38(6), 1170-84. https://doi.org/10.1109/TPAMI.2015.2481390
Inoue, Nakamasa, and Shinoda, Koichi. "Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search." IEEE transactions on pattern analysis and machine intelligence vol. 38,6 (2016): 1170-84. doi: https://doi.org/10.1109/TPAMI.2015.2481390
Inoue N, Shinoda K. Fast Coding of Feature Vectors Using Neighbor-to-Neighbor Search. IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1170-84. doi: 10.1109/TPAMI.2015.2481390. Epub 2015 Sep 23. PMID: 26415151.
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Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms.

IEEE transactions on pattern analysis and machine intelligence

Saeidi R, Astudillo RF, Kolossa D.
PMID: 26415158
IEEE Trans Pattern Anal Mach Intell. 2016 Jul;38(7):1479-88. doi: 10.1109/TPAMI.2015.2481420. Epub 2015 Sep 23.

Linear discriminant analysis (LDA) is a powerful technique in pattern recognition to reduce the dimensionality of data vectors. It maximizes discriminability by retaining only those directions that minimize the ratio of within-class and between-class variance. In this paper, using...

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Saeidi R, Astudillo RF, Kolossa D. Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms. IEEE Trans Pattern Anal Mach Intell. 2015;38(7):1479-88doi: 10.1109/TPAMI.2015.2481420.
Saeidi, R., Astudillo, R. F., & Kolossa, D. (2016). Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms. IEEE transactions on pattern analysis and machine intelligence, 38(7), 1479-88. https://doi.org/10.1109/TPAMI.2015.2481420
Saeidi, Rahim, et al. "Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms." IEEE transactions on pattern analysis and machine intelligence vol. 38,7 (2016): 1479-88. doi: https://doi.org/10.1109/TPAMI.2015.2481420
Saeidi R, Astudillo RF, Kolossa D. Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms. IEEE Trans Pattern Anal Mach Intell. 2016 Jul;38(7):1479-88. doi: 10.1109/TPAMI.2015.2481420. Epub 2015 Sep 23. PMID: 26415158.
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Online Metric-Weighted Linear Representations for Robust Visual Tracking.

IEEE transactions on pattern analysis and machine intelligence

Li X, Shen C, Dick A, Zhang ZM, Zhuang Y.
PMID: 26390446
IEEE Trans Pattern Anal Mach Intell. 2016 May;38(5):931-50. doi: 10.1109/TPAMI.2015.2469276. Epub 2015 Aug 17.

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and...

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Li X, Shen C, Dick A, et al. Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Trans Pattern Anal Mach Intell. 2015;38(5):931-50doi: 10.1109/TPAMI.2015.2469276.
Li, X., Shen, C., Dick, A., Zhang, Z. M., & Zhuang, Y. (2016). Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE transactions on pattern analysis and machine intelligence, 38(5), 931-50. https://doi.org/10.1109/TPAMI.2015.2469276
Li, Xi, et al. "Online Metric-Weighted Linear Representations for Robust Visual Tracking." IEEE transactions on pattern analysis and machine intelligence vol. 38,5 (2016): 931-50. doi: https://doi.org/10.1109/TPAMI.2015.2469276
Li X, Shen C, Dick A, Zhang ZM, Zhuang Y. Online Metric-Weighted Linear Representations for Robust Visual Tracking. IEEE Trans Pattern Anal Mach Intell. 2016 May;38(5):931-50. doi: 10.1109/TPAMI.2015.2469276. Epub 2015 Aug 17. PMID: 26390446.
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Local Feature Selection for Data Classification.

IEEE transactions on pattern analysis and machine intelligence

Armanfard N, Reilly JP, Komeili M.
PMID: 26390448
IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1217-27. doi: 10.1109/TPAMI.2015.2478471. Epub 2015 Sep 14.

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the...

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Armanfard N, Reilly JP, Komeili M. Local Feature Selection for Data Classification. IEEE Trans Pattern Anal Mach Intell. 2015;38(6):1217-27doi: 10.1109/TPAMI.2015.2478471.
Armanfard, N., Reilly, J. P., & Komeili, M. (2016). Local Feature Selection for Data Classification. IEEE transactions on pattern analysis and machine intelligence, 38(6), 1217-27. https://doi.org/10.1109/TPAMI.2015.2478471
Armanfard, Narges, et al. "Local Feature Selection for Data Classification." IEEE transactions on pattern analysis and machine intelligence vol. 38,6 (2016): 1217-27. doi: https://doi.org/10.1109/TPAMI.2015.2478471
Armanfard N, Reilly JP, Komeili M. Local Feature Selection for Data Classification. IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1217-27. doi: 10.1109/TPAMI.2015.2478471. Epub 2015 Sep 14. PMID: 26390448.
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Showing 1 to 12 of 3461 entries