| Multiclass data segmentation using diffuse interface methods on graphs C Garcia-Cardona, E Merkurjev, AL Bertozzi, A Flenner, AG Percus IEEE transactions on pattern analysis and machine intelligence 36 (8), 1600-1613, 2014 | 113 | 2014 |
| An MBO scheme on graphs for classification and image processing E Merkurjev, T Kostic, AL Bertozzi SIAM Journal on Imaging Sciences 6 (4), 1903-1930, 2013 | 108 | 2013 |
| Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video E Merkurjev, J Sunu, AL Bertozzi 2014 IEEE International Conference on Image Processing (ICIP), 689-693, 2014 | 54 | 2014 |
| Diffuse interface methods for multiclass segmentation of high-dimensional data E Merkurjev, C Garcia-Cardona, AL Bertozzi, A Flenner, AG Percus Applied Mathematics Letters 33, 29-34, 2014 | 40 | 2014 |
| Global binary optimization on graphs for classification of high-dimensional data E Merkurjev, E Bae, AL Bertozzi, XC Tai Journal of Mathematical Imaging and Vision 52 (3), 414-435, 2015 | 37 | 2015 |
| Detection and tracking of gas plumes in LWIR hyperspectral video sequence data T Gerhart, J Sunu, L Lieu, E Merkurjev, JM Chang, J Gilles, AL Bertozzi Algorithms and Technologies for Multispectral, Hyperspectral, and …, 2013 | 35 | 2013 |
| Hyperspectral Image Classification Using Graph Clustering Methods Z Meng, E Merkurjev, A Koniges, AL Bertozzi | 30* | |
| Auction Dynamics: A Volume Constrained MBO Scheme M Jacobs, E Merkurjev, S Esedoglu | 29* | |
| Convex variational methods for multiclass data segmentation on graphs E Bae, E Merkurjev | 21* | |
| A Semi-Supervised Heat Kernel Pagerank MBO Algorithm for Data Classification E Merkurjev, AL Bertozzi, Chung, F. | 19* | |
| Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds E Bae, E Merkurjev | 16* | |
| Automated parameter selection in the-TV model for removing Gaussian plus impulse noise A Langer Inverse Problems 33 (7), 074002, 2017 | 15 | 2017 |
| Modified Cheeger and ratio cut methods using the Ginzburg–Landau functional for classification of high-dimensional data E Merkurjev, A Bertozzi, X Yan, K Lerman Inverse Problems 33 (7), 074003, 2017 | 12 | 2017 |
| Hyperspectral video analysis using graph clustering methods Z Meng, E Merkurjev, A Koniges, AL Bertozzi Image Processing On Line, submitted, 2017 | 8 | 2017 |
| An MBO scheme on graphs for segmentation and image processing E Merkurjev, T Kostic, A Bertozzi UCLA CAM Report, 12-46, 2012 | 8 | 2012 |
| Relative Performance of Lambert Solvers 1: 0-Revolution Methods GE Peterson, ET Campbell, J Balbas, S Ivy, E Merkurjev, Rodriguez, P. Adv Astronaut Sci. 136 (1), 1495-1510, 2010 | 8* | 2010 |
| Variational and PDE-based methods for big data analysis, classification and image processing using graphs E Merkurjev CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS, 2015 | 3 | 2015 |
| A fast graph-based data classification method with applications to 3D sensory data in the form of point clouds E Merkurjev Pattern Recognition Letters 136, 154-160, 2020 | 1 | 2020 |
| On a modified auction dynamics technique for semi-supervised multiclass data classification E Merkurjev Intelligent Data Analysis 25 (4), 879-906, 2021 | | 2021 |
| Graph-based optimization approaches for machine learning, uncertainty quantification and networks AL Bertozzi, E Merkurjev Handbook of Numerical Analysis 20, 503-531, 2019 | | 2019 |
Andrea BertozziProf. of Math. and Mech. & Aero. Eng., Betsy Wood Knapp Chair for Innovation and Creativity, UCLADirección de correo verificada de math.ucla.edu
