Valentín Osuna-Enciso

Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the al...

In this article is introduced an innovative segmentation methodology that is based on a two-dimensional (2D) histogram that permits to increase the quality of the segmented images. The 2D histogram is constructed using the Lateral Inhibition (LI) that helps maintain and remark different image features. To segment the image in the proposed approach,...

In Metaheuristic Algorithms (MA), the balance between exploration and exploitation is a common issue considered an open research problem in the MA community. Besides its particular parameters, another way to control the Exploration-Exploitation Balance in MA is by using a diversity metric (DM) as a guide. However, this procedure has two drawbacks:...

Convolutional Neural Networks (CNN) are novel techniques with significant performance in object detection and classification. An open research problem on CNN is the automatic finding of adequate architectures, which is usually done by hand. Metaheuristic algorithms are techniques that find optimal solutions in heuristic manner to problems where the...

Nowadays, resource-optimizing techniques are required in many engineering areas to obtain the most appropriate solutions for complex problems. For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore...

Clustering is one of the most popular methods of machine learning. The process of clustering involves the division of a set of abstract objects into a certain number of groups which integrated with objects of similar characteristics. Therefore, a cluster integrates objects which are similar to them, but dissimilar to the elements that belong to the...

Segmentation is an important task in image processing because it could affect the performance of other steps in image analysis. One of the most used methods for segmentation is thresholding which can be formulated as an optimization problem, and evolutionary algorithms (EAs) are alternatives commonly applied to solve it. Estimation of Distribution...

The design of Two-Dimensional Infinite Input Response Filters (2D IIR) is an important task in the field of signal processing. These filters are widely used in several areas of engineering as an important tool to eliminate undesired frequencies in high-noised signals. However, 2D IIR filters have parameters that need to be calibrated in order to ob...

Pipe design is one of the most significant research lines in the area of parabolic semi-cylindrical solar collectors. The main idea behind pipe design is to increase the capillarity angle by expanding the total area being heated, therefore boosting the work capacity of the device. Such capillarity depends on several factors, whose numerical calcula...

This work is focused on understanding the interaction of native galena minerals and xanthate as well as their influence on the process of mineral separation by flotation and leaching techniques. To obtain basic and fundamental information about this interaction, a quantitative analysis of an electrochemical impedance study based on a mechanistic ap...

The segmentation of digital images is one of the most important steps in an image processing or computer vision system. It helps to classify the pixels in different regions according to their intensity level. Several segmentation techniques have been proposed, and some of them use complex operators. The techniques based on thresholding are the easi...

The Wind Farm Layout Problem (WFLP) consists in the placement of eolic generators (either in a grid, or at any position) into a delimited terrain. Several factors are taken into account to solve the WFLP, which include produced energy, costs - environmental, installation, maintenance, etc-, average useful life of turbines, among other. Likewise, op...

Template matching (TM) plays an important role in several image processing applications. In a TM approach, it is sought the point in which it is presented the best possible resemblance between a sub-image known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy....

Computer vision process frequently include an modeling step whose parameters, obtained from a data set, are not easy to calculate due mainly to the presence of a high proportion of outliers. The most known method to overcome this problem is the random sampling consensus (RANSAC). Such technique, in combination with Harmony Search (HS), are used in...

Hough transform (HT) represents the most common method for circle detection, exhibiting robustness and parallel processing. However, HT adversely demands a considerable computational load and large storage. Alternative approaches may include heuristic methods with iterative optimization procedures for detecting multiple circles. In this chapter a n...

Segmentation is used to divide an image into separate regions, which in fact correspond to different real-world objects. One interesting functional criterion for segmentation is the Tsallis entropy (TE), which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding (MT), its evaluation becomes comput...

The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most of the industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induct...

Motion estimation is a major problem for video-coding applications. Among several other motion estimation approaches, block matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity at their software and hardware implementation. The BM approach assumes that the pixel movement inside a given region of the curren...

As a research area, there are several problems in medical imaging that continue unresolved; one of those is the automatic detection of white blood cells (WBC) in smear images. The study of this kind of images has engaged researchers from fields of medicine and computer vision alike. Several studies have been done to try to approximate this cells wi...

In order to improve the performance of solar energy systems, accurate modeling of current versus voltage (I–V) characteristics of solar cells has attracted the attention of various researches. The main drawback in accurate modeling is the lack of information about the precise parameter values which indeed characterize the solar cell. Since such par...

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physica...

In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal re...

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques hav...

Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations o...

Photovoltaic systems, wind turbines and battery storage systems are all commonly present in energy self-sufficiency and independent producers schemes, since they support distributed generation designs. For both cases, single phase inverters provide power conditioning in order to inject micro-sources power to the grid, demanding multiple tasks such...

In image analysis, segmentation is considered one of the most important steps. Segmentation by searching threshold values assumes that objects in a digital image can be modeled through distinct gray level distributions. In this chapter it is proposed the use of a bio-inspired algorithm, called Allostatic Optimisation (AO), to solve the multi thresh...

Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which ex...

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to...

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex ta...

Segmentation is one of the most important tasks in image processing. It consists of classifying the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for Multilevel Thresholding is c...

PhD thesis defended on 2nd December, 2013. In general, the purpose of Global Optimization (GO) is to find the global optimum of an objective function defined inside a search space, and it has applications in many areas of science, engineering, economics, among other, where mathematical modeling is used. GO algorithms are divided into two groups: d...

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the...

Acute lymphoblastic leukemia is a blood cancer that can be cured if it is detected at early stages; however, the analysis of smear blood by a human expert is tired and subject to errors. In such a sense, diagnostic of the disease is costly and time consuming. Considering that situation, several automatic segmentation methods have been proposed, som...

Hough transform has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimisation procedures for detecting multiple circles under the inconvenience that only one circle can be mark...

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. T...

The Physic–inspired computation is becoming popular and has been acknowledged by the scientific community. This emerging area has developed a wide range of techniques and methods for dealing with complex problems. On the other hand, automatic circle detection in digital images has been considered as an important and complex task for the computer vi...

The Physic-inspired computation is becoming popular and has been acknowledged by the scientific community. This emerging area has developed a wide range of techniques and methods for dealing with complex problems. On the other hand, automatic circle detection in digital images has been considered as an important and complex task for the computer vi...

White blood cells also known as leukocytes play a significant role in the diagnosis of different diseases. Digital image processing techniques have successfully contributed to analyze the cells, leading to more accurate and reliable systems for disease diagnosis. However, a high variability on cell shape, size, edge and localization complicates the...

Threshold selection is a critical step in computer vision. Immune systems, has inspired optimization algorithms known as Artificial Immune Optimization (AIO). AIO have been successfully applied to solve optimization problems. The Clonal Selection algorithm (CSA) is the most applied AIO method. It generates a response after an antigenic pattern is i...