C
Cynthia B. Perez
Researcher at Sonora Institute of Technology
Publications - 32
Citations - 216
Cynthia B. Perez is an academic researcher from Sonora Institute of Technology. The author has contributed to research in topics: Genetic programming & Pair programming. The author has an hindex of 7, co-authored 30 publications receiving 190 citations. Previous affiliations of Cynthia B. Perez include Ensenada Center for Scientific Research and Higher Education.
Papers
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Proceedings ArticleDOI
Evolutionary learning of local descriptor operators for object recognition
Cynthia B. Perez,Gustavo Olague +1 more
TL;DR: Evidence that genetic programming is able to design new features that enhance the overall performance of the best available local descriptor algorithm is provided.
Journal ArticleDOI
The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Correspondence
TL;DR: This work presents a new bio-inspired approach applied to a problem of stereo image matching based on an artificial epidemic process, which it is called the infection algorithm, based on a set of distributed rules.
Proceedings ArticleDOI
Learning invariant region descriptor operators with genetic programming and the F-measure
Cynthia B. Perez,Gustavo Olague +1 more
TL;DR: Experimental results show that the evolved descriptor¿s operator can enhance significantly the overall performance of the SIFT descriptor and surpass other state-of-the-art algorithms.
Journal ArticleDOI
Genetic programming as strategy for learning image descriptor operators
Cynthia B. Perez,Gustavo Olague +1 more
TL;DR: A new approach for learning invariant region descriptor operators through genetic programming and another optimization method based on a hill-climbing algorithm with multiple re-starts is introduced.
Journal ArticleDOI
Estudio Cualitativo sobre el Comportamiento del Consumidor en las Compras en Línea
TL;DR: In this paper, a qualitative study based on the methodology of fundamental theory is used to identify and categorize variables about online purchases based on relevant factors such as purchase motivation, preferences, consumption habits and purchasing patterns.