Dr Abel Pacheco-Ortega is a computer vision and machine learning researcher and engineer with experience spanning academic research, industrial R&D, software development and higher education. He is currently a Knowledge Transfer Partnership (KTP) Associate working with MSeis Ltd and the Â鶹´«Ã½Ó³» on the development of AI-driven solutions for marine environmental monitoring.
His work focuses on integrating computer vision, machine learning and multimodal sensing into practical systems operating in challenging real-world environments. His current project investigates the use of thermal and optical imaging to support the detection and classification of marine mammals and floating objects.
Qualifications
Qualifications
- PhD in Computer Science, University of Bristol, United Kingdom.
- Master's degree in Engineering (Computing), Universidad Nacional Autónoma de México (UNAM), Mexico.
- Bachelor's degree in Computer Engineering, Universidad Nacional Autónoma de México (UNAM), Mexico.
Professional background
Abel's professional background spans computer vision, machine learning, robotics, software engineering, and academic teaching. His experience includes research at the University of Bristol, industrial research and development in computer vision and robotics, and software development leadership.
His technical expertise includes 3D computer vision, visual recognition, deep learning, image processing, camera calibration, and robotic perception. His current KTP role focuses on transferring AI and computer vision capabilities into marine monitoring systems through collaboration between industry and academia.
Abel's professional background spans computer vision, machine learning, robotics, software engineering, and academic teaching. His experience includes research at the University of Bristol, industrial research and development in computer vision and robotics, and software development leadership.
His technical expertise includes 3D computer vision, visual recognition, deep learning, image processing, camera calibration, and robotic perception. His current KTP role focuses on transferring AI and computer vision capabilities into marine monitoring systems through collaboration between industry and academia.
Teaching
Teaching interests
Abel's teaching interests include computer vision, image processing, machine learning, robotics, and programming. He is particularly interested in connecting theoretical concepts with practical implementation through programming exercises, experimental projects, and real-world applications.
His broader educational interests include problem-solving, critical thinking, software development, and communicating complex technical concepts clearly and accessibly. His teaching experience includes undergraduate programming and image-processing modules at the University of Bristol and postgraduate computer vision teaching at the Universidad Nacional Autónoma de México.
His broader educational interests include problem-solving, critical thinking, software development, and communicating complex technical concepts clearly and accessibly. His teaching experience includes undergraduate programming and image-processing modules at the University of Bristol and postgraduate computer vision teaching at the Universidad Nacional Autónoma de México.
Research
Research interests
Abel's research interests lie at the intersection of computer vision, machine learning, and robotic perception, with a focus on developing robust methods for interpreting visual data in real-world environments.
His specific research interests include:
His specific research interests include:
- Computer vision and visual perception: object detection, recognition, scene understanding, visual affordances, and 3D vision.
- Machine learning and deep learning: visual recognition, data-driven perception, and the development and evaluation of learning-based methods.
- Multimodal sensing and sensor fusion: combining thermal and optical imagery to improve perception and environmental monitoring.
- Robotics and autonomous systems: visual SLAM, camera calibration, localisation, and perception for robotic applications.
- AI for environmental monitoring: computer vision methods for detecting and classifying marine mammals, floating objects, and other environmental anomalies.
A central theme of his work is bridging academic research and industrial applications, translating advances in AI and computer vision into reliable and practical systems.
Publications
- Pacheco-Ortega, A., & Mayol-Cuevas, W. (2023). AROS: Affordance recognition with one-shot human stances. Frontiers in Robotics and AI, 10, 1076780.
- Sarmiento, C., Savage, J., Juarez, A., Contreras, L., Pacheco, A., & Matamoros, M. (2019, April). Feature detection using Hidden Markov Models for 3D-visual recognition. In 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (pp. 1–6). IEEE.
- Pacheco, A., Estrada, H., Vázquez, E., Martell, R., Hernández, J., Cruz, J., Silva, E., Savage, J., & Contreras, L. (2018). Intelligent flat-and-textureless object manipulation in service robots. In IROS 2018 Workshop: Towards Robots that Exhibit Manipulation Intelligence, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.
- Savage, J., Contreras, L., Figueroa, I., Pacheco, A., Bermudez, A., Negrete, M., & Rivera, C. (2016). Construction of roadmaps for mobile robots’ navigation using RGB-D cameras. In Intelligent Autonomous Systems 13: Proceedings of the 13th International Conference IAS-13 (pp. 217–229). Springer International Publishing.
- Contreras, L. A., Pacheco-Ortega, A., Figueroa, J. I., Mayol-Cuevas, W. W., & Savage, J. (2013, November). Object detection via receptive field co-occurrence and spatial cloud-point data. In 2013 16th International Conference on Advanced Robotics (ICAR) (pp. 1–8). IEEE.
- Figueroa, J., Contreras, L., Pacheco, A., & Savage, J. (2012). Development of an object recognition and location system using the Microsoft Kinect™ sensor. In T. Röfer, N. M. Mayer, J. Savage, & U. Saranlı (Eds.), RoboCup 2011: Robot Soccer World Cup XV (Lecture Notes in Computer Science, Vol. 7416, pp. 321–328). Springer.