Dr. Carlos Brito Loeza

 

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Facultad de Matemáticas, Laboratorio de Aprendizaje Automático y Visión, Edificio G

Tel. y Fax: (999) 942-31-40 al 49 ext. 67129

 

Curriculum vitae:      Download

 


Education and Awards

  • PhD Mathematics, University of Liverpool, United Kingdom.
  • MSc. Mathematics, Universidad Autónoma de Yucatán, México.
  • BSc. Eletronic Engineering, Instituto Tecnológico de Mérida, México.
  • Membership to the National System of Researchers since 2010, currently Level I.

Research interests

I work on the mathematical modeling of interesting problems belonging to the fields of image processing, artificial intelligence, and the analysis of digital data. My preferred tools for this purpose are techniques based on the calculus of variations, partial differential equations and optimization. Lately, I have put some effort on using these methods on the mathematical modeling and understanding of convolutional neural networks to tackle challenging problems from Health sciences.

Some keywords that somehow may describe my interests are:

  • Variational methods; Nonlinear PDE's; Ill-posed inverse problems.
  • Numerical methods and optimization; Multigrid methods.
  • Convolutional neural networks, image processing and data analysis.
  • C/C++, Python and Matlab programming.

Books

 


Journal publications

  1. Carlos Brito-Pacheco, Carlos Brito-Loeza, Anabel Martin-Gonzalez, "A regularized logistic regression based model for supervised learning", Journal of Algorithms and Computational Technology, 14:1-9, July 2020.
  2. Ricardo Legarda-Saenz, Carlos Brito-Loeza, "Augmented Lagrangian Method for a total-variation based model for demodulating phase discontinuities", Journal of Algorithms and Computational Technology, 14:1-8, November 2020.
  3. Carlos Brito-Loeza, Ricardo Legarda-Sáenz, Anabel Martin-Gonzalez, "A fast algorithm for a total variation based phase demodulation model", Numerical Methods for Partial Differential Equations, accepted for publication, DOI:10.1002/num.22444, October 2019.
  4. Legarda-Saenz, A Téllez Quiñones, C Brito-Loeza, A Espinosa-Romero, "Variational phase recovering without phase unwrapping in phase-shifting interferometry", International Journal of Computer Mathematics 96 (6), 1217-1229.
  5. Carlos Brito-Loeza, Ricardo Legarda-Sáenz, Arturo Espinosa-Romero, Anabel Martin-Gonzalez, "A Mean Curvature Regularized Based Model for Demodulating Phase Maps from Fringe Patterns", Journal Communications in Computational Physics, 24 (1), 27-43, 2018.
  6. José L Medina-Catzin, Anabel Martin-Gonzalez, Carlos Brito-Loeza, Victor Uc-Cetina, "Body gestures recognition system to control a service robot", Journal Int J Inf Tech Comput Sci, Volume 9, Pages 69-76, 2017.
  7. Brito‐Loeza, Carlos, Ke Chen, and Victor Uc‐Cetina, “Image denoising using the Gaussian curvature of the image surface”, Numerical Methods for Partial Differential Equations, 32(3):1066-1089, 2015.
  8. Ibrahim, Mazlinda, Ke Chen, and Carlos Brito-Loeza, “A novel variational model for image registration using Gaussian curvature”, Geometry, Imaging and Computing, 1(4):417-446, 2014.
  9. Víctor Uc-Cetina, Carlos Brito-Loeza, and Hugo Ruiz-Piña, “Chagas Parasite Detection in Blood Images Using AdaBoost”, Computational and Mathematical Methods in Medicine, Article ID 139681, 13 pages, doi:10.1155/2015/139681, 2015.
  10. César Cobos-May, Víctor Uc-Cetina, Carlos Brito-Loeza, and Anabel Martin-Gonzalez, “A Convex Set Based Algorithm to Automatically Generate Haar-Like Features”, Journal Computer Science, 2(2):64-70, 2014.
  11. Ricardo Legarda-Saenz, Carlos Brito-Loeza and Arturo Espinosa-Romero, “Variational method for integrating radial gradient field”, Optics and Lasers in Engineering, 63:53-57, 2014.
  12. Ricardo Legarda-Saenz, Carlos Brito-Loeza, and Arturo Espinosa-Romero, “Total variation regularization cost function for demodulating phase discontinuities”, Applied optics 53(11): 2297-2301, 2014.
  13. Martha Varguez-Moo, Victor Uc-Cetina, and Carlos Brito-Loeza, “Clasificación de documentos usando Máquinas de Vectores de Apoyo”, Abstraction and Application Magazine, 2014.
  14. Víctor Uc-Cetina, Carlos Brito-Loeza, and Hugo Ruiz-Piña, “Chagas parasites detection through Gaussian discriminant analysis”, Abstraction and Application Magazine 8, 2014.
  15. Roger Soberanis-Mukul, Víctor Uc-Cetina, Carlos Brito-Loeza, Hugo Ruiz-Piña, “An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images”, Computer methods and programs in biomedicine 112(3):633-639, 2013.
  16. Carlos Brito-Loeza and Ke Chen, “Fast iterative algorithms for solving the minimization of curvature-related functionals in surface fairing”, International Journal of Computer Mathematics, 90(1):92-108, 2013.
  17. Noppadol Chumchob, Ke Chen and Carlos Brito-Loeza, “A new variational model for removal of combined additive and multiplicative noise and a fast algorithm for its numerical approximation”, 90(1):140-161, 2013.
  18. Noppadol Chumchob, Ke Chen and Carlos Brito-Loeza, “A Fourth Order variational Image Registration Model and Its Fast Multigrid Algorithm”, SIAM Multiscale Modeling and Simulation, 9(1):89-128, 2011.
  19. Carlos Brito-Loeza and Ke Chen, “Fast Numerical Algorithms for the Euler’s Elastica Inpainting Model”, International Journal of Modern Mathematics, 5(2):157-182, 2010.
  20. Carlos Brito-Loeza and Ke Chen, “Multigrid Algorithm for High-Order Denoising”, SIAM Journal on Imaging Sciences, 3(3):363-389, 2010.
  21. Carlos Brito-Loeza and Ke Chen, “On High Order Denoising Models and Fast Algorithms for Vector-valued Images”, Image Processing, IEEE Transactions on, 19(6):1518-1527, 2010.
  22. Carlos Brito-Loeza and Ke Chen, “Multigrid Method for a Modified Curvature Driven Diffusion Model for Image Inpainting”, Journal of Computational Mathematics, 26(6): 856-875, 2008.

 

Selected talks

  1. A Variational Model for Binary Classification in the Supervised Learning Context, The Fourth International Workshop on Image Processing Techniques and Applications, 22-23 July 2019, CMIT, University of Liverpool, UK.
  2. Una visión diferente de los procesos de segmentación y registro de imágenes médicas, XLI Congreso Nacional de Ingeniería Biomédica CNIB2018 18 al 20 de octubre del 2018 León, Guanajuato, México.
  3. Variational methods for imaging and supervised learning, Zhejiang University of Science and Technology, China, May 12, 2018 - May 20, 2018.
  4. A one week course on mathematical modeling and solutions of different imaging and supervised learning problems using variational techniques,  Nanchang University, China, April 27, 2018 – May 12, 2018.
  5. A Gaussian Curvature Based Denoising Model for Non-Gaussian Noise, SIAM Conference on Imaging Science, May 23-26, Hotel Albuquerque at Old Town, Albuquerque, New Mexico, USA, 2016.
  6. Fringe Analysis Using Curvature Models, SIAM Conference on Imaging Science, May 23-26, Hotel Albuquerque at Old Town, Albuquerque, New Mexico, USA, 2016.
  7. Image Denoising Using the Gaussian Curvature of the Image Surface, SIAM Conference on Imaging Science, May 12-14, Hong Kong Baptist University, Hong Kong, 2014.
  8. High order models and fast algorithms for fairing variational implicit surfaces, SIAM Conference on Imaging Science, May 20-22, Philadelphia, Pennsylvania, USA, 2012.
  9. On numerical algorithms for level set and curvature based models for surface fairing. 24th. Biennial Conference on Numerical Analysis, University of Strathclyde, Glasgow, UK, 2011.
  10. On high order variational models for blind image deblurring, International Workshop on Image Processing Techniques and Applications, 22-23 June, CMIT, Liverpool, UK, 2011.
  11. High-Order Vector-Valued Models for Image Restoration, SIAM Conference on Imaging Science, April 12th - 14th, Chicago, Illinois, USA, 2010.
  12. Multigrid Algorithms for High Order Variational Models with Applications to Digital Image Denoising and Inpainting, 23rd. Biennial Conference on Numerical Analysis, University of Strathclyde, Glasgow, UK, 2009.

 


 

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Research Projects Dr Carlos

Slide 1
Machine Lerning
Learning problems
Slide 2
Machine Learning Projects
Chagas Parasite Segmentation
Slide 3
Machine Learning Projects
Comet Assay
Slide 14
Machine Learning Projects
Pollen grains detection of four plant species of Yucatan using
deep learning
Slide 10
Computer Vision Projects
Unmanned Aerial System for Remote Data Acquisition and Photogrammetric Sensing
Slide 11
Computer Vision Projects
Fringe pattern analysis
Slide 12
Computer Vision Projects
Imaging problems
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