Luminita Vese

Affiliations: 
Mathematics University of California, Los Angeles, Los Angeles, CA 
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"Luminita Vese"

Children

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Triet M. Le grad student 2006 UCLA
Linh H. Lieu grad student 2006 UCLA
Ginmo Chung grad student 2007 UCLA
Yunho Kim grad student 2009 UCLA
Pascal T. Getreuer grad student 2010 UCLA
Mi Y. Jung grad student 2010 UCLA
Tungyou Lin grad student 2010 UCLA
Carl D. Lederman grad student 2012 UCLA
Melissa Y. Tong grad student 2012 UCLA
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Publications

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Tari S, Vese L, Rangarajan A. (2016) Guest Editorial: Shape Analysis Beyond the Eikonal Equation Journal of Mathematical Imaging and Vision. 55: 151-152
Yanovsky I, Lambrigtsen BH, Tanner AB, et al. (2015) Efficient Deconvolution and Super-Resolution Methods in Microwave Imagery Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Lederman C, Joshi A, Dinov I, et al. (2015) A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures Journal of Mathematical Imaging and Vision
Bar L, Chan TF, Chung G, et al. (2015) Mumford and shah model and its applications to image segmentation and image restoration Handbook of Mathematical Methods in Imaging: Volume 1, Second Edition. 1539-1597
Tong M, Kim Y, Zhan L, et al. (2014) A vectorial total variation model for denoising high angular resolution diffusion images corrupted by Rician noise Methods and Applications of Analysis. 21: 151-176
Schaeffer H, Duggan N, Guyader CL, et al. (2014) Topology preserving active contours Communications in Mathematical Sciences. 12: 1329-1342
Kim Y, Garnett JB, Vese LA. (2014) Image restoration using one-dimensional sobolev norm profiles of noise and texture Siam Journal On Imaging Sciences. 7: 366-390
Duggan N, Bae E, Jones E, et al. (2014) A simple boundary reinforcement technique for segmentation without prior Pattern Recognition Letters. 46: 27-35
Schaeffer H, Vese L. (2014) Active contours with free endpoints Journal of Mathematical Imaging and Vision. 49: 20-36
Schaeffer H, Garnett J, Vese L. (2013) A texture model based on a concentration of measure Inverse Problems and Imaging. 7: 927-946
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