This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following:
– highlighting brightness channels in the Red-Green-Blue color space;
– using a particle swarm method in the image in each channel of brightness of the RGB color space;
– image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel.
Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems.
A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following:
– the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment);
– the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue);
– the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue).
It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %

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Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm

The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems.
The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following:
– highlighting brightness channels in the Red-Green-Blue color space;
– the use of a genetic algorithm in the image in each channel of brightness of the RGB color space;
– image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space.
Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems.
A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way:
– compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind;
– compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind.
The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

https://zenodo.org/record/6861345/files/Devising%20a%20method%20for%20segmenting%20camouflaged%20military%20equipment%20on%20images%20from%20space%20surveillance%20systems%20using%20a%20genetic%20algorithm.pdf

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Segmentation is the technique where an image is partitioned into various regions comprising of similar attributes like intensity, texture, graysclae, etc represented by pixel patterns. Image segmentation has been generating research opportunities in this field for a long period. Various segmentation techniques like Otsu Thresholding, K-means clustering, Watershed Technique, Active Contours and Particle Swarm Optimization have been reviewed under this study. Survey of the mathematical equations along with the carrying-out of above segmentation techniques practically is the desired purpose of this study. The performance is analysed by estimation of the performance measurements i.e, TP, TN, FP and FN in numerical values.

Segmentation Techniques for Complex Image: Review

Neglected TROPICAL diseases related to the skin with similar manifestations in their early phase persist in remote areas and are characterized by the prevailing poverty of populations. If not detected early, they very often lead to severe ulcerations and permanent disabilities. We present an approach to optimize the early detection of neglected tropical skin diseases (NTDs) by automatic identification of skin lesions. As contributions, we propose a web-mobile AI-powered automatic skin lesion recognition system optimized by a new hybrid Whale-Shark optimization algorithm (WOA-SSO-ANN) that can help frontline health workers without state-of-the-art equipment to detect NTDs in their beginning stage. We extract the relevant regions features of the lesions. The dataset resulting from this preprocessing is classified by artificial neural networks optimized by a new hybrid Whale-Shark optimization algorithm to develop an improved artificial neural network in terms of processing time and/or accuracy. The best result was obtained with an overall classification accuracy of 93% and a processing time reduced by almost half compared to other optimizers. The proposed application is able to recognize cases of Buruli ulcer, leprosy, and leishmaniasis in our database (nodule and plaque) and classify new patients, thus reducing the cost of management of these diseases when they are detected late. The AI models implemented in this work have satisfactory accuracy and could be a complementary diagnostic tool, especially in remote areas where medical specialists are scarce. 

Enhancing the Diagnosis of Skin Neglected Tropical Diseases by Artificial Neural Networks Using Evolutionary Algorithms: Implementation on Raspberry Pi

Data clustering and classification technique algorithms often need to possess enough and prominent number of features in the data. Repeating and dominant features are useful in clustering or segmenting the image. The image segmentation method based on k-mean clustering, hierarchical clustering, and expectation maximization derives the optimum cluster centers based on the number of features such as similar intensity region. Deriving such number optimum number of clusters and its centers is an optimization problem. The aim of this paper is to improve the image segmentation using nature inspired techniques. Image segmentation which is complex optimization problem can be solved by this simple nature inspired PSO (Particle swarm optimization) model which is formulated in this paper. PSO model is generic model which is used to solve number of scientific problems. This paper formulates simple PSO model to solve the image segmentation problem. The proposed algorithm randomly assigns the centers to swarm and best value of objective function is initialized best on the color histogram of an image. This is discussed in section 2 and 3 of paper. Section 4 and 5 discusses and results and concluding remarks on results.

Netra M. Lokhande

Papers

Novel Image Segmentation Using Particle Swarm Optimization