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Image contrast enhancement is a crucial computer vision step aiming to improve the quality of the visual information in processed images. In the literature, several proposed methods for image contrast enhancement are Histogram Equalization-based (HE) techniques that use one transformation function and optimize its parameters for mapping the pixels to new gray-intensity values. However, using only one transformation function would leave other enhancement options unexplored. Therefore, the proposed approach generates several transformation functions and selects the one that best improves the image's contrast. This method is based on the Differential Evolution (DE) algorithm, which produces multiple candidate solutions representing transformation functions. The transformation functions map the input pixel values in their enhanced versions to equalize the histogram and improve the image's contrast. Furthermore, a new formulation is proposed as the objective function based on the number of edge pixels, the intensity of the pixels, image entropy, and the number of gray intensity levels. The performance of this approach has been tested on low-contrast dataset images and compared to similar HE techniques, such as AVHEQ, BBHE, RSESIHE, MMBEBHE, and ESIHE. The results demonstrate the proposed algorithm's robustness and high performance in improving the grayscale images' contrast. [ABSTRACT FROM AUTHOR]

Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/Neural Networks (CNN) are commonly utilized in image processing applications such as image edge detection, image encoding and image hole filling. CNN perform well for locating corner features in binary images. However, their use in grayscale images has not been considered due to their design difficulties. In this paper, a corner detector based on CNN for grayscale images is presented. In the approach, the original processing scheme of the CNN is modified to include a nonlinear operation for increasing the contrast of the local information in the image. With this adaptation, the final CNN parameters that allow the appropriate detection of corner points are estimated through the Differential evolution algorithm by using standard training images. Different test images have been used to evaluate the performance of the proposed corner detector. Its results are also compared with popular corner methods from the literature. Computational simulations demonstrate that the proposed CNN approach presents competitive results in comparison with other algorithms in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. [ABSTRACT FROM AUTHOR]

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 a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm. [ABSTRACT FROM AUTHOR]

Abstract: 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 is labeled 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 selection problems; 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 the histogram, 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. [Copyright &y& Elsevier]

Abstract: Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame (macro block, MB) can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) produced by the MB of the current frame over a determined search window from the previous frame. The SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The most straightforward BM method is the full search algorithm (FSA), which finds the most accurate motion vector, exhaustively calculating the SAD values for all the elements of the search window. Over this decade, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the cost of poor accuracy. In this paper, a new algorithm based on differential evolution (DE) is proposed to reduce the number of search locations in the BM process. To avoid computing several search locations, the algorithm estimates the SAD values (fitness) for some locations using the SAD values of previously calculated neighboring positions. As the proposed algorithm does not consider any fixed search pattern or any other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison with other fast BM algorithms, the proposed method deploys more accurate motion vectors, yet delivering competitive time rates. [Copyright &y& Elsevier]

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 for many computer vision algorithms, DE has evolved as a successful heuristic method for solving complex optimization problems, still keeping a simple structure and an easy implementation. It has also shown advantageous convergence properties and remarkable robustness. The detection process is considered similar to a combinational optimization problem. The algorithm uses the combination of three edge points as parameters to determine circle candidates in the scene yielding a reduction of the search space. The objective function determines if some circle candidates are actually present in the image. This paper focuses particularly on one DE-based algorithm known as the discrete differential evolution (DDE), which eventually has shown better results than the original DE in particular for solving combinatorial problems. In the DDE, suitable conversion routines are incorporated into the DE, aiming to operate from integer values to real values and then getting integer values back, following the crossover operation. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images. 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. [ABSTRACT FROM AUTHOR]

Abstract: Threshold selection is a critical preprocessing step for image analysis, pattern recognition and computer vision. On the other hand differential evolution (DE) is a heuristic method for solving complex optimization problems, yielding promising results. DE is easy to use, keeps a simple structure and holds acceptable convergence properties and robustness. In this work, a novel automatic image multi-threshold approach based on differential evolution optimization is proposed. Hereby the segmentation process is considered to be similar to an optimization problem. First, the algorithm fills the 1-D histogram of the image using a mix of Gaussian functions whose parameters are calculated using the differential evolution method. Each Gaussian function approximating the histogram represents a pixel class and therefore a threshold point. The proposed approach is not only computationally efficient but also does not require prior assumptions whatsoever about the image. The method is likely to be most useful for applications considering different and perhaps initially unknown image classes. Experimental results demonstrate the algorithm’s ability to perform automatic threshold selection while preserving main features from the original image. [Copyright &y& Elsevier]

Power-electronics based converters are essential circuits in renewable energy applications such as electricity generated with photovoltaic panels. The research on the field is getting increasing attention due to climate change problems and their possible attenuation with the use of renewable energy. Mathematical models of the converters are being used to optimize several aspects of their operation. This article is dedicated to optimizing (through the mathematical model and an evolutionary algorithm) the operation of a state-of-the-art converter. The converter, which is composed of two parts or phases, is controlled by pulse width modulation with two switching signals (one for each phase). The converter provides by itself low switching ripple in both the output voltage and the input current, which is beneficial for renewable energy applications. In the traditional operation, one of the switching signals has an algebraic dependence on the other one. This article proposes a new way to select the duty cycle for both signals. In the proposed method, duty cycles of both phases are considered independent of each other; this provides an extra degree of freedom; on the other hand, this produce that the possible combinations of duty cycles which produce a certain voltage gain is infinite, it becomes a problem with infinite possible solutions. The proposed method utilizes the a linear success-history based adaptive differential evolution with linear population reduction, also called L-SHADE algorithm for simplicity, to find the two duty cycles that achieve the desired voltage gain and to minimize the converters switching ripple. The obtained results are compared with the former operation of the converter; the proposed operation achieves a lower output voltage ripple while achieving the desired operation (voltage gain). [ABSTRACT FROM AUTHOR]

In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal methodology based on the Differential Evolution algorithm for obtaining the set of duty cycles of a recently proposed power electronics converter with input current ripple cancelation capability. The converter understudy was recently introduced to the state-of-the-art as the interleaved connection of two unequal converters to achieve low input current ripple. A latter contribution proposed a so-called proportional strategy. The strategy can be described as the equations to relate the duty cycles of the unequal power stages. This article proposes a third switching strategy that provides a lower input current ripple than the proportional strategy. This is made by considering duty cycles independently of each other instead of proportionally. The proposed method uses the Differential Evolution algorithm to determine the optimal switching pattern that allows high quality at the input current side, given the reactive components, the switching frequency, and power levels. The mathematical model of the converter is analyzed, and thus, the decision variables and the optimization problem are well set. The proposed methodology is validated through numerical experimentation, which shows that the proposed method achieves lower input current ripples than the proportional strategy. [ABSTRACT FROM AUTHOR]

A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency and convergence. A comprehensive set of experiments are performed to evaluate the MPA-OBL and prove the impact influence of merging OBL strategy with the original MPA in enhancing the quality of the solutions and the acceleration of the convergence speed, using IEEE CEC'2020 benchmark problems as recently complex optimization benchmark. In order to evaluate the performance of the proposed MPA-OBL, the effectiveness of conjunction of OBL with the original MPA and the other counterparts are calculated and compared with LSHADE with semi-parameter adaptation hybrid with CMA-ES (LSHADE_SPACMA-OBL), Restart covariance matrix adaptation ES (CMA_ES-OBL), Differential evolution (DE-OBL), Harris hawk optimization (HHO-OBL), Sine cosine algorithm (SCA-OBL), Salp swarm algorithm (SSA-OBL), and the original MPA. The extensive results and comparisons in terms of optimization metrics have revealed the superiority of the proposed MPA-OBL in solving the IEEE CEC'2020 benchmark problems and improving the convergence speed. Moreover, as a sequel to the proposed MPA-OBL, also, we have conducted experiments using two objective functions of Otsu and Kapur's methods over a variety of benchmark images at different level of thresholds based on three commonly evaluation matrices namely Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM), and Feature similarity (FSIM) indices are analyzed qualitatively and quantitatively. Eventually, the statistical post-hoc analysis reveal that the MPA-OBL obtains highly efficient and reliable results in comparison with the other competitor algorithms. [ABSTRACT FROM AUTHOR]