Search Results for author: Daniel Zaldivar

Found 15 papers, 1 papers with code

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

no code implementations30 Jun 2014 Erik Cuevas, Daniel Zaldivar, Marco Perez, Humberto Sossa, Valentin Osuna

Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space.

Motion Estimation

An improved computer vision method for detecting white blood cells

no code implementations26 Jun 2014 Erik Cuevas, Margarita Diaz, Miguel Manzanares, Daniel Zaldivar, Marco Perez

This paper presents an algorithm for the automatic detection of WBC embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem.

Multi Circle Detection on Images Using Artificial Bee Colony (ABC) Optimization

no code implementations25 Jun 2014 Erik Cuevas, Felipe Sencion-Echauri, Daniel Zaldivar, Marco Perez Cisneros

Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions.

A multilevel thresholding algorithm using Electromagnetism Optimization

no code implementations24 Jun 2014 Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, Valentin Osuna

Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu or Kapur method.

Semantic Segmentation

A swarm optimization algorithm inspired in the behavior of the social-spider

1 code implementation12 Jun 2014 Erik Cuevas, Miguel Cienfuegos, Daniel Zaldivar, Marco Perez

In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony.

Circle detection using electro-magnetism optimization

no code implementations30 May 2014 Erik Cuevas, Diego Oliva, Daniel Zaldivar, Marco Perez-Cisneros, Humberto Sossa

The EMO algorithm is used to find the circle candidate that is better related with the real circle present in the image according to the objective function.

Circle detection by Harmony Search Optimization

no code implementations28 May 2014 Erik Cuevas, Noe Ortega, Daniel Zaldivar, Marco Perez

Guided by the values of this objective function, the set of encoded candidate circles are evolved using the HSA so that they can fit to the actual circles on the edge map of the image (optimal harmony).

Circle detection using Discrete Differential Evolution Optimization

no code implementations28 May 2014 Erik Cuevas, Daniel Zaldivar, Marco Perez, Marte Ramirez

Experimental results on several synthetic and natural images with varying range of complexity validate the efficiency of the proposed technique considering accuracy, speed, and robustness.

A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization

no code implementations28 May 2014 Erik Cuevas, Felipe Sencion, Daniel Zaldivar, Marco Perez, Humberto Sossa

This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation.

Semantic Segmentation

Seeking multi-thresholds for image segmentation with Learning Automata

no code implementations28 May 2014 Erik Cuevas, Daniel Zaldivar, Marco Perez

In this approach, one 1D histogram of a given image is approximated through a Gaussian mixture model whose parameters are calculated using the LA algorithm.

Semantic Segmentation

Robust Fuzzy corner detector

no code implementations21 May 2014 Erik Cuevas, Daniel Zaldivar, Marco Perez, Edgar Sanchez, Marte Ramirez

Reliable corner detection is an important task in determining the shape of different regions within an image.

Fast algorithm for Multiple-Circle detection on images using Learning Automata

no code implementations21 May 2014 Erik Cuevas, Fernando Wario, Valentin Osuna, Daniel Zaldivar, Marco Perez

The detection process is considered as a multi-modal optimization problem, allowing the detection of multiple circular shapes through only one optimization procedure.

Circle detection on images using Learning Automata

no code implementations21 May 2014 Erik Cuevas, Fernando Wario, Daniel Zaldivar, Marco Perez

Guided by the values of such reinforcement signal, the probability set of the encoded candidate circles is modified through the LA algorithm so that they can fit to the actual circles on the edge map.

Multi-ellipses detection on images inspired by collective animal behavior

no code implementations20 May 2014 Erik Cuevas, Maurici Gonzalez, Daniel Zaldivar, Marco Perez

This paper presents a novel and effective technique for extracting multiple ellipses from an image.

Opposition Based ElectromagnetismLike for Global Optimization

no code implementations20 May 2014 Erik Cuevas, Diego Oliva, Daniel Zaldivar, Marco Perez, Gonzalo Pajares

Electromagnetismlike Optimization (EMO) is a global optimization algorithm, particularly well suited to solve problems featuring nonlinear and multimodal cost functions.

Global Optimization

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