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.
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.
Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions.
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.
In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony.
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.
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).
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.
This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation.
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.
The detection process is considered as a multi-modal optimization problem, allowing the detection of multiple circular shapes through only one optimization procedure.
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.
Electromagnetismlike Optimization (EMO) is a global optimization algorithm, particularly well suited to solve problems featuring nonlinear and multimodal cost functions.