Multilevel image thresholding using elephant herding optimization algorithm

Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding.

The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution and their speed has led to the use of thresholding approaches. Image thresholding is simple, easy and effective method for image segmentation. Multi-level image thresholding is a key perspective in several real-time pattern recognition and image processing-based applications. It identifies pixels quickly and effectively in different groups indicating multiple regions in an image. Segmentation of images based on thresholding by using various intelligent optimization techniques with fuzzy entropy is widely utilized for defining thresholds in a better way to use them precisely. In this research, a novel technique for multi-level thresholding is proposed by combining Fuzzy Entropy Type II (FE-TII) with recently developed meta-heuristics named Marine Predators Algorithm (MPA). For achieving optimal thresholds of an image, the maximization of entropy is tedious and consumes a lot of time with an increasing number of thresholds. The MPA method presented is analyzed in context with image segmentation, particularly on thresholds with TII-FE. For this reason, proposed methodology is evaluated using several images along with the distribution of histograms. For analyzing the performance efficiency of the proposed methodology, the results are compared and robustness is tested with efficiency of proposed technique to multi-level image segmentation, several images are used randomly from datasets.

Multilevel thresholding using Otsu or Kapur methods is widely used in the context of image segmentation. These methods select optimal thresholds in gray level images by maximizing between-class variance or entropy criterion. These methods become time consuming and less efficient with increasing number of thresholds. To increase the efficiency of the image segmentation using multilevel thresholding based on Kapur and Otsu methods, we developed a hybrid optimization algorithm named BMO-DE based on bird mating optimization (BMO) and differential evolutionary (DE) algorithms. The efficiency of the proposed method was evaluated on eight standard benchmark images. The proposed method achieved better segmentation results in term of solution quality and stability in comparison with other well-known techniques including bacterial foraging (BF), modified bacterial foraging (MBF), particle swarm optimization (PSO), genetic algorithm (GA) and hybrid algorithm named PSO-DE.

Digital image segmentation is often used as a preprocessing step in image analysis where different objects in the image need to be separated. One of the most used techniques for image segmentation is multilevel thresholding. Different criteria are used for determination of optimal thresholds, but in all cases, multilevel thresholding is a hard optimization problem that cannot be solved by deterministic methods. Swarm intelligence stochastic optimization metaheuristics have been proven to be very successful for such problems. In this chapter, we propose adjusted guided fireworks algorithm (GFWA) for multiple optimal threshold determination based on Kapur's entropy, Otsu's criteria and Tsallis' entropy. Comparison with other state-of-the-art techniques shows that the GFWA exhibits superior performance.

Photonic crystal fiber as a new type of optical fiber has been extensively applied because of its unique properties. The effective mode area of optical fiber is an important parameter, which has a great influence on the performance of optical fiber. In this study, digital image processing algorithm was used for preprocessing to improve the accuracy of calculation of mode field area. Then the effective mode field area of optical fiber was calculated using Matlab based Gauss fitting method. Take single-mode fiber G.652 as an example, the effective mode field area was calculated using the traditional algorithm and digital image processing algorithm respectively. It was found that the results obtained using digital image processing algorithm were within the allowed error range, suggesting the effectiveness of the algorithm. Then the calculation of the effective mode area of the triangular lattice photonic crystal fiber further verified the reliability of the algorithm.

Multilevel thresholding is a significant technology for image segmentation. Traditional exhaustive search method to solve the optimal multilevel thresholds is time consuming, color images contain more information are even worse. To address this issue, an improved cuckoo search algorithm (ICS) is proposed for color image segmentation in this paper, and a modified fuzzy entropy is used as its objective function. In ICS, two modifications are adopted to improve the standard cuckoo search algorithm. First, an adaptive control parameter mechanism is utilized to enhance the performance of exploration. Second, a hybrid search strategy is used to boost the local search efficiency. To fully demonstrate the superior performance of the ICS, the experiments are conducted on a series of color benchmark test images, and a total of six optimization algorithms are compared with the proposed algorithm. The experimental results show that the presented ICS algorithm outperforms all the other algorithms in term of objective function value, PSNR, FSIM, convergence speed and parametric statistical test. Compared to other algorithms, the ICS algorithm is an effective method for multilevel color image thresholding segmentation.

This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWAATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration and exploitation during the search process. The exploitation is done using good fireworks whereas the bad fireworks are responsible for exploration. FWA shows its efficiency and effectiveness in numerical function optimization over other SI algorithm like particle swarm optimization (PSO). FWA-ATF is a modified version of basic FWA and in this work, it is used in hard clustering technique to segment the image. FWA-ATF is used to find the optimal cluster centroids corresponding to different regions in the image. The proposed clustering technique is applied to segment four benchmark images and the well-known cluster validity index-Dunn's Index is used to measure the performance of the proposed clustering technique quantitatively. The performance of the proposed method is compared with clustering using K-means, PSO and basic FWA. The experimental results demonstrates that the proposed clustering technique with FWA-ATF performs better than other methods in segmentation for most of the images.

Swarm intelligence algorithms have been extensively used in clustering-based applications, e.g., image segmentation, which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization (PSO) is among swarm intelligence algorithms that perform based on population and random search. In this paper, a hybrid algorithm based on PSO, $$k$$ -means, and learning automata is proposed for image segmentation. Each particle in the proposed method has been equipped with a learning automata (LA). In fact, each particle can either update its position by PSO method or select the next position utilizing $$k$$ -means approach in each iteration based on its LA. In other word, the main aim of the hybrid proposed approach was to utilize the efficiency of PSO and $$k$$ -mean methods under supervision of LA. The proposed approach along with other comparative studies has been applied for segmenting standard test images. Efficiency of the proposed method has been compared with that of other methods, and experimental results show the superiority proposed algorithm.

Multi-level image thresholding is a common approach to image segmentation for which population-based metaheuristic algorithms present an interesting alternative to conventional methods that are based on a exhaustive search. In this paper, we propose a novel multi-level image thresholding algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA, and an entropy-based fitness function. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to fitness function value and peak signal-to-noise ratio (PSNR). Experimental results demonstrate excellent thresholding performance and our algorithm to outperform nine other state-of-the-art metaheuristics.

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Real-time image processing is one of the fundamental elements in achieving IR 4.0. The rapid development of digital image processing techniques has enabled various applications in fields such as healthcare, transportation, and manufacturing. People are seeking higher-performance image processing as traditional image processing is no longer fulfilling the demands. FPGA-based digital image processing has become one of the choices for the public due to its parallel pipelining, which enables shorter processing time and better performance. Several digital image processing algorithms have been developed in this project, which are gray level transformation, brightness manipulation, contrast adjustment, thresholding, and inversion. They are the most popular algorithms used in digital image processing. Microsoft Paint is used to convert the format of the color input image to bitmap format, followed by MATLAB to convert it into a hexadecimal file to be read and written in FPGA. Platforms such as ModelSim Altera and Intel Quartus II are used to write Verilog HDL for digital image processing algorithms. As a result, five hexadecimal files are obtained from the simulation. The output hexadecimal files are further processed in MATLAB to generate respective images.

Background/Objectives: The major aim of thework is to propose an efficient multi-level thresholding for gray scale image using Firefly Algorithm (FA). Methods/Statistical Analysis: The multi-level image thresholding is attempted using Otsu's function and Firefly Algorithm (FA) using standard 512 x 512 sized gray scale image dataset. The robustness of the attempted segmentation process is tested by staining the test images with universal noises. The superiority of the FA based segmentation is validated with the heuristic algorithms, such as Bat Algorithm, Bacterial Foraging Optimization and Particle Swarm Optimization existing in the literature. Findings: The simulation result in this work conforms that, FA assisted segmentation offers better result compared to the alternatives. The robustness of the FA and Otsu based segmentation is also superior and offered improvedcost function, SSIM, PSNR value and reduced CPU time compared with the alternatives. Application/Improvements: In future, the proposed technique can be experienced using standard RGB images availablein the literature.

Applications of image compression is important in terms of time and resource management considering factors such as require more time to send according to the size of image over the network and large amount of space is high dimensional data for storing images. In this study, a new approach can be using at image compression process will be introduced. Firstly, image subjected to discrete wavelet transform for extracting feature. Then multi-level threshold values will be find with Shanon entropy in the obtained image. The maximum value of objective function will be obtained with the help of cricket algorithm at the threshold values finding step. This algorithm is a meta-heuristic algorithm that based on population. The threshold values that obtained through algorithm using to compressing the images will be provided. At the end of the study, the image compression ratio, the proposed approach running on a standard test image will be given.

Swarm intelligence algorithms have been extensively used in clustering based applications e.g. image segmentation which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization is amongst swarm intelligence algorithms that performs based on population and random search. In this paper, a hybrid algorithm based on PSO, k-means and learning automata is proposed for image segmentation. In the proposed algorithm, learning automata is responsible for activating and deactivating PSO and k-means methods based on current conditions of the segmentation problem. The proposed approach along with other comparative studies has been applied for segmenting benchmark images. Efficiency of the proposed method has been compared with that of other methods and experimental results show the superiority proposed algorithm.