Laboratorio de Visión, Robótica e Inteligencia Artificial


Computer Vision

The main developments in computer vision from LaViRIA are:

  • The analysis of visual characteristics such as color and texture in order to automate tasks that use images as the key information.
  • The classification and segmentation of images, in which the computer automatically determines the component elements of a scene.
  • The recognition of patterns and shapes, where visual models are searched autonomously in an image.
  • Object tracking and visual servo control that allow computers to perform dynamic vision tasks based on mathematical and computational models

Mobile Robotics

Our work in mobile robotics uses the XidooBot robot as the main experimental platform. Our goal is to provide artificial intelligence to this robot so that it can execute high-level robotic tasks. Developments to date include:

  • Robotic navigation systems, which consist of making the robot execute displacement tasks between two points that can be specified both geometrically, from sensory references or even using topological references.
  • Scenario modeling tasks, so that the robot generates useful representations to complete the tasks entrusted to it.
  • Reactive navigation methods, which allow the robot to avoid obstacles present in its operating environment and that prevent the efficient execution of the task in progress.

Computational Intelligence

At LaViRIA, the main approach used to develop intelligent systems is based on flexible computing. Flexible computing is understood as a set of techniques and methods (among which are fuzzy systems, genetic algorithms and neural networks) that aims to solve engineering problems, synergistically integrating the benefits of the methods in a collaborative way.

We have applied this experience in real-time systems for image processing, computer vision and mobile robotics applications.


The scientific work of the LaViRIA members produces research articles that are published in different national and international conferences as well as in peer-reviewed and indexed journals.

Articles in indexed journals

  • Contreras-Cruz, M. A., Novo-Torres, L., Villarreal, D. J., & Ramirez-Paredes, J. P. (2023). Convolutional neural network and sensor fusion for obstacle classification in the context of powered prosthetic leg applications. Computers and Electrical Engineering, 108, 108656.
  • Contreras-Cruz, M. A., Correa-Tome, F. E., Lopez-Padilla, R., & Ramirez-Paredes, J. P. (2023). Generative Adversarial Networks for anomaly detection in aerial images. Computers and Electrical Engineering106, 108470.
  • Septien-Hernandez, J. A., Arellano-Vazquez, M., Contreras-Cruz, M. A., & Ramirez-Paredes, J. P. (2022). A Comparative study of post-quantum cryptosystems for Internet-of-Things applications. Sensors22(2), 489.
  • Torres, F. J., Ramírez-Paredes, J. P., García-Murillo, M. A., Martínez-Ramírez, I., Capilla-González, G., & Ramírez, V. A. (2021). A tracking control of a flexible-robot including the dynamics of the induction motor as actuator. IEEE Access9, 82373-82379.
  • Zamora-Garcia, I., Correa-Tome, F. E., Hernandez-Belmonte, U. H., Ayala-Ramirez, V., & Ramirez-Paredes, J. P. (2021). Mobile digital colorimetry for the determination of ammonia in aquaculture applications. Computers and Electronics in Agriculture181, 105960.
  • Lopez-Alanis, A., Lizarraga-Morales, R. A., Contreras-Cruz, M. A., Ayala-Ramirez, V., Sanchez-Yanez, R. E., & Trujillo-Romero, F. (2020). Rule-based aggregation driven by similar images for visual saliency detection. Applied Intelligence50(6), 1745-1762.
  • Ramirez-Paredes, J. P., & Hernandez-Belmonte, U. H. (2020). Visual quality assessment of malting barley using color, shape and texture descriptors. Computers and Electronics in Agriculture168, 105110.
  • Contreras-Cruz, M. A., Ramirez-Paredes, J. P., Hernandez-Belmonte, U. H., & Ayala-Ramirez, V. (2019). Vision-based novelty detection using deep features and evolved novelty filters for specific robotic exploration and inspection tasks. Sensors19(13), 2965.
  • Lopez-Alanis, A., Lizarraga-Morales, R. A., Sanchez-Yanez, R. E., Martinez-Rodriguez, D. E., & Contreras-Cruz, M. A. (2019). Visual saliency detection using a rule-based aggregation approach. Applied Sciences9(10), 2015.
  • Lizarraga-Morales, R. A., Correa-Tome, F. E., Sanchez-Yanez, R. E., & Cepeda-Negrete, J. (2019). On the use of binary features in a rule-based approach for defect detection on patterned textiles. IEEE Access7, 18042-18049.
  • Lopez-Perez, J. J., Hernandez-Belmonte, U. H., Ramirez-Paredes, J. P., Contreras-Cruz, M. A., & Ayala-Ramirez, V. (2018). Distributed multirobot exploration based on scene partitioning and frontier selection. Mathematical Problems in Engineering2018.
  • Ramirez-Paredes, J. P., Doucette, E. A., Curtis, J. W., & Ayala-Ramirez, V. (2018). Sensor compromise detection in multiple-target tracking systems. Sensors18(2), 638.
  • Cepeda-Negrete, J., Sanchez-Yanez, R. E., Correa-Tome, F. E., & Lizarraga-Morales, R. A. (2017). Dark image enhancement using perceptual color transfer. IEEE Access6, 14935-14945.

Articles in conference proceedings

  • Contreras-Cruz, M. A., Ochoa, G., & Ramirez-Paredes, J. P. (2020, November). Synthetic vs. real-world continuous landscapes: A local optima networks view. In International Conference on Bioinspired Methods and Their Applications (pp. 3-16). Cham: Springer International Publishing.
  • Ayala-Alfaro, V., Torres-Del Carmen, F., & Ramirez-Paredes, J. P. (2020, September). Wind field estimation by small UAVs for rapid response to contaminant leaks. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1546-1552). IEEE.
  • Novo-Torres, L., Ramirez-Paredes, J. P., & Villarreal, D. J. (2019, July). Obstacle recognition using computer vision and convolutional neural networks for powered prosthetic leg applications. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3360-3363). IEEE.

Articles in outreach publications

  • Andrade, M. R., Vázquez, L. G., Camacho, E. A., Carmona, M. E. C. M. E., Villegas, A. T., & Paredes, J. R. P. J. R. (2023). Evasión de Obstáculos con un Robot Móvil Terrestre usando Aprendizaje por Refuerzo. JÓVENES EN LA CIENCIA21, 1-8.
  • Ledesma, H. E. Q., Uribe, H. A. G., Gutiérrez, F. A. C., Torres, A. D. H., Beltrán, L. G. L., Vázquez, M. G. M., … & Belmonte, U. H. H. (2022). Arte y tecnología: Implementación de la máquina estética de Manuel Felguérez en un lenguaje moderno. JÓVENES EN LA CIENCIA16, 1-9.
  • Pérez, G. P., Andrade, M. A. R., Jiménez, U. M., Roa, M. O., González, X. B. G., Zamora-García, I., & Ramírez-Paredes, J. P. (2022). Desarrollo de una aplicación móvil para el monitoreo de parámetros en la acuicultura. JÓVENES EN LA CIENCIA16.
  • Rivillas, A. C. C., & Paredes, J. P. I. R. (2018). MODELADO MATEMÁTICO Y CONTROL DE UN SISTEMA DE DESPEGUE Y ATERRIZAJE VERTICAL A ESCALA. Jovenes en la ciencia4(1), 2480-2485.