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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
In today’s digital image processing field, image segmentation is an important and complex task. The goal of image segmentation is to divide an image into several different regions so that the pixels in each region have similar characteristics. This task plays a key role in many applications such as computer vision, medical image processing, object recognition, etc.
In image segmentation algorithms, threshold segmentation is a commonly used method. It classifies pixels in an image into different categories based on their gray value. However, traditional threshold segmentation methods often can only handle the case of a single threshold, and the effect is not ideal for complex images.
To solve this problem, researchers have proposed many improved image segmentation algorithms. One such method is multi-threshold image segmentation based on optimization algorithms. This method utilizes an optimization algorithm to automatically select the best threshold to obtain better segmentation results.
The Northern Goshawk Optimization Algorithm (NGOA) is an optimization algorithm based on bionics principles. It simulates the behavior of northern goshawks during the hunting process and finds the optimal solution through iterative search. The NGOA algorithm has the characteristics of fast convergence, high efficiency and strong global search ability, so it is widely used in the field of image processing.
The multi-threshold image segmentation algorithm process based on the improved northern goshawk optimization algorithm is as follows:
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Initialization parameters: including the gray level of the image, population size, maximum number of iterations, etc.
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Generate initial population: Randomly generate a set of initial thresholds based on the set population size.
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Calculate the fitness function: apply each individual threshold to the image segmentation and calculate the fitness of the segmentation results.
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Selection operation: According to the value of the fitness function, select a part of outstanding individuals as the next generation population.
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Crossover operation: perform crossover operation on the selected individuals to generate new individuals.
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Mutation operation: perform mutation operation on newly generated individuals to increase the diversity of the population.
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Update population: Add newly generated individuals to the current population.
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Determine the termination condition: Determine whether the maximum number of iterations is reached or the preset fitness threshold is reached.
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End algorithm: If the termination condition is met, output the optimal solution, otherwise return to step 4.
The multi-threshold image segmentation algorithm based on the improved northern goshawk optimization algorithm automatically adjusts the threshold through continuous iterative search to obtain better segmentation results. It makes full use of the global search capability of the Northern Goshawk optimization algorithm and is able to handle complex image segmentation tasks.
In summary, the multi-threshold image segmentation algorithm based on the improved northern goshawk optimization algorithm is an efficient and effective image processing method. It can automatically select the best threshold and provide better segmentation results. With the continuous development of digital image processing technology, it is believed that this algorithm will play an important role in more fields.
Part of the code
%Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar y Marco Perez-Cisneros</code><code>%Multilevel Thresholding Segmentation Based on Harmony Search Optimization</code><code>%Universidad Complutense de Madrid / Universidad de Guadalajara</code><code>?</code><code>%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%</code><code>%The algorithm was published in:</code><code>%Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, and Marco Perez-Cisneros, </code><code>%ultilevel Thresholding Segmentation Based on Harmony Search Optimization,?</code><code>%Journal of Applied Mathematics, vol. 2013, </code><code>%Article ID 575414, 24 pages, 2013. doi:10.1155/2013/575414</code><code>%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%</code><code>?</code><code>%Program for Peak Signal to Noise Ratio Calculation</code><code>?</code><code>function PSNRV = PSNR(origImg, distImg)</code><code>?</code><code>origImg = double(origImg);</code><code>distImg = double(distImg);</code><code>?</code> <code>[M N] = size(origImg);</code><code>error = origImg - distImg;</code><code>MSE = sum(sum(error .* error)) / (M * N) ;</code><code>?</code><code>if(MSE > 0)</code><code> PSNRV = 10*log(255*255/MSE) / log(10);</code><code>else</code><code> PSNRV = 99;</code><code>end
Operation results
References
[1] Wu Liang, Du Lingbin, Tang Xianfeng. Multi-threshold image segmentation based on improved butterfly optimization algorithm [J]. Chinese Science and Technology Papers, 2021, 016(011):1174-1180.
[2] Wu Liang, Du Lingbin, Tang Xianfeng. Multi-threshold image segmentation based on improved butterfly optimization algorithm [J]. Chinese Science and Technology Papers, 2021, 16(11):7.
[3] Yang Zhenlun, Min Huaqing, Luo Ronghua. Multi-threshold image segmentation algorithm based on improved quantum particle swarm optimization [J]. Journal of South China University of Technology: Natural Science Edition, 2015(5):7.DOI:10.3969/j.issn. 1000-565X.2015.05.020.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV application
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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