25,464 research outputs found
Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms
As an alternative to
classical techniques, the problem of image
segmentation has also been handled through
evolutionary methods. Recently, several
algorithms based on evolutionary principles have
been successfully applied to image segmentation
with interesting performances. However, most of
them maintain two important limitations: (1)
they frequently obtain suboptimal results
(misclassifications) as a consequence of an
inappropriate balance between exploration and
exploitation in their search strategies; (2) the
number of classes is fixed and known in advance.
This paper presents an algorithm for the
automatic selection of pixel classes for image
segmentation. The proposed method combines a
novel evolutionary method with the definition of
a new objective function that appropriately
evaluates the segmentation quality with respect
to the number of classes. The new evolutionary
algorithm, called Locust Search (LS), is based
on the behavior of swarms of locusts. Different
to the most of existent evolutionary algorithms,
it explicitly avoids the concentration of
individuals in the best positions, avoiding
critical flaws such as the premature convergence
to suboptimal solutions and the limited
exploration-exploitation balance. Experimental
tests over several benchmark functions and
images validate the efficiency of the proposed
technique with regard to accuracy and
robustness
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing
Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99
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