6 citations · 18 across the 10 of their papers we have counts for
10 papers
UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data
Ezequiel Perez-Zarate, Chunxiao Liu, Oscar Ramos-Soto +2
Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall…
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement
Ezequiel Perez-Zarate, Oscar Ramos-Soto, Chunxiao Liu +2
Low-light image enhancement is an important task in computer vision, essential for improving the visibility and quality of images captured in non-optimal lighting conditions. Inade…
Multi Circle Detection on Images Using Artificial Bee Colony (ABC) Optimization
Erik Cuevas, Felipe Sencion-Echauri, Daniel Zaldivar +1
Hough transform (HT) has been the most common method for circle detection, exhibiting robustness, but adversely demanding considerable computational effort and large memory require…
Circle detection using electro-magnetism optimization
Erik Cuevas, Diego Oliva, Daniel Zaldivar +2
This paper describes a circle detection method based on Electromagnetism-Like Optimization (EMO). Circle detection has received considerable attention over the last years thanks to…
Circle detection using Discrete Differential Evolution Optimization
Erik Cuevas, Daniel Zaldivar, Marco Perez +1
This paper introduces a circle detection method based on Differential Evolution (DE) optimization. Just as circle detection has been lately considered as a fundamental component fo…
Seeking multi-thresholds for image segmentation with Learning Automata
Erik Cuevas, Daniel Zaldivar, Marco Perez
This paper explores the use of the Learning Automata (LA) algorithm to compute threshold selection for image segmentation as it is a critical preprocessing step for image analysis,…
Circle detection by Harmony Search Optimization
Erik Cuevas, Noe Ortega, Daniel Zaldivar +1
Automatic circle detection in digital images has received considerable attention over the last years in computer vision as several efforts have aimed for an optimal circle detector…
A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization
Erik Cuevas, Felipe Sencion, Daniel Zaldivar +2
This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is a heuristic algorithm motivated by the intell…
Opposition Based ElectromagnetismLike for Global Optimization
Erik Cuevas, Diego Oliva, Daniel Zaldivar +2
Electromagnetismlike Optimization (EMO) is a global optimization algorithm, particularly well suited to solve problems featuring nonlinear and multimodal cost functions. EMO employ…
Multi-ellipses detection on images inspired by collective animal behavior
Erik Cuevas, Maurici Gonzalez, Daniel Zaldivar +1
This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave c…