Dr. Victor Uc Cetina

 

 

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Computational Learning and Imaging Research Lab, Facultad de Matemáticas, Universidad Autónoma de Yucatán.

Tel. y Fax: (999) 942-31-40 al 49 ext. 1131.


Education and awards

  • Ph.D. Computer Science (Dr. rer. nat.), Humboldt-Universität zu Berlin, Germany.
  • M.Sc. Intelligent Systems, Instituto Tecnológico y de Estudios Superiores de Monterrey, México.
  • B.Sc. Computer Systems, Instituto Tecnológico de Mérida, México.
  • Membership to the National System of Researchers since 2016, currently Level I.

Research interests

  • Artificial intelligence.
  • Reinforcement learning.
  • Machine learning.

Completed Theses

  • Red Neuronal con Convolución Dilatada para Estimación del Flujo Óptico Denso, [Neural network with dilated convolution to estimate dense optical flow], Iván Martínez, 2019
  • Detección del árbol de Dzildzilché usando imágenes aéreas multiespectrales y redes neuronales convolucionales, [Dzildzilché tree detection using multispectral aerial images and convolutional neural networks], Juan José Negrón Granados, 2019

Personal Website

https://sites.google.com/view/victoruccetina/

 

Research Projects


 

Traffic Signal Recognition

The recognition of traffic signals is one of the fundamental tasks in the advanced driving assistance systems, since most of the actions the vehicle must take to maintin a safe and convenient driving fall on them. The project implements a deep learning algorithm (YOLO) to classify and detect traffic signals from the Yucatán state. 

 


 

Learning problems

Historically, the problem of supervised classification has been tackled using the neural network approach or with algorithms of a statistical nature. In particular, neural networks, when correctly designed, are capable of delivering efficient solutions to the classification problem. However the analysis of the solution and its properties is very complex and this is why they are commonly referred to as "black box" solutions. In recent years, mathematical models have been proposed based on the calculus of variations for the problem of data classification. This different approach allows us to use powerful mathematical tools such as functional analysis and differential geometry for model analysis. We are interested in developing models and fast algorithms and to tackle the many challenges that appear in doing so.