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Journal articles on the topic 'Genetic Algorithm and Image Processing'
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Hassan, Raghad Mahdie, and Luma Salal Hassan. "Assessments Image Segmentation Using Genetic Algorithm." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 352–63. http://dx.doi.org/10.46649/fjiece.v3.2.23a.2.6.2024.
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Image segmentation is a crucial technique for processing images. It is a challenging task to process images, and the quality of the segmentation process affects the following assignments, which include classification, object recognition, feature extraction, and object detection.It's a significant phase of a system for computer vision. Image segmentation is the basic problem in many applications for image processing. Over time, image segmentation has gotten more challenging due to its extensive use in numerous applications. It is the procedure of segmenting the image into various areas by using a specific technique. There are many different ways for image segmentation.A new information parameter with a threshold basis for segmenting images using the genetic algorithm. Due to its ability to calculate the ideal number of segmentation regions, we employed the Genetic Algorithm. In this work, a novel approach built upona genetic algorithm is used to solve the image segmentation problem by utilizing the thresholding concept.The suggested method uses a genetic algorithm to identify the evolutionary best segmented image based on a threshold that is based on new information. We presented the results of our experiments using the suggested method on various grayscale images in the last section. By using parameters used to evaluate image segmentation quality (PSNR,MSE,SC), we notice the results are good.
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Guan, Xiao Wei, and Xia Zhu. "Image Segmentation Research Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 1622–25. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1622.
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As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, genetic algorithm (GA) has proposed to segment the image. GA algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that GA algorithm is very stable, and the fusion result is more satisfactory. Thus, GA can be applied in image segmentation and this algorithm will have good prospects in image processing.
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Riwanto, Yudha, and Enda Putri Atika. "Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets." Internet of Things and Artificial Intelligence Journal 4, no. 3 (2024): 526–31. http://dx.doi.org/10.31763/iota.v4i3.767.
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This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to select the most relevant features from the processed image. The selected features are then used as input for the KNN model in the classification process. The test results show that the proposed method can achieve a classification accuracy of 87,9%. This shows that the combination of image processing in the HSV color space, feature selection using a genetic algorithm, and classification with KNN can improve performance in recognizing leaf types. This research makes significant contributions to the field of image processing and classification and shows the potential of using genetic algorithms for feature selection in pattern recognition applications.
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Agarwal, Ritu, and Mallika Pant. "Image tampering detection using genetic algorithm." MATEC Web of Conferences 277 (2019): 02026. http://dx.doi.org/10.1051/matecconf/201927702026.
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As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.
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A., D. Chitra* Dr. P. Ponmuthuramalingam. "AN ENHANCED HYBRID CUCKOO SEARCH AND GENETIC ALGORITHM USING HAAR-LIKE FEATURE FOR SURVEILLANCE VIDEOS." Global Journal of Engineering Science and Research Management 4, no. 7 (2017): 74–84. https://doi.org/10.5281/zenodo.832647.
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This paper is mainly focused on image processing concept which is used in surveillance videos for security purpose. Image processing technique involves dispensation of image using a mixture of mathematical functions in which image or video is given as input and the extent to which the image is matched is given as output. The main problem found in this paper are edge detection and noise removal in images. Initially this research paper is compared with cuckoo search and genetic algorithm. Then these two algorithms are combined together as an hybrid CS - GA Algorithm and finally the result of this hybrid algorithm is compared with enhanced hybrid CS- GA using Haar-like feature for real time surveillance video. The result is compared in terms of Recognition rate accuracy and time complexity.
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Gorokhovskyi, Semen, and Andrii Moroz. "Image Segmentation Using Genetic Algorithms." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 52–55. http://dx.doi.org/10.18523/2617-3808.2021.4.52-55.
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Image segmentation is a crucial step in the image processing and analysis process. Image segmentation is the process of splitting one image into many segments. Image segmentation divides images into segments that are more representative and easier to examine. Individual surfaces or items can be used as such pieces. The process of image segmentation is used to locate objects and their boundaries.Genetic algorithms are stochastic search methods, the work of which is taken from the genetic laws, natural selection, and evolution of organisms. Their main attractive feature is the ability to solve complex problems of combinatorial search effectively, because the parallel study of solutions, largely eliminates the possibility of staying on the local optimal solution rather than finding a global one.The point of using genetic algorithms is that each pixel is grouped with other pixels using a distance function based on both local and global already calculated segments. Almost every image segmentation algorithm contains parameters that are used to control the segmentation results; the genetic system can dynamically change parameters to achieve the best performance.Similarly to image sequencing, to optimize several parameters in the process, multi-targeted genetic algorithms were used, which enabled finding a diverse collection of solutions with more variables. Multi- targeted Genetic Algorithm (MTGA) is a guided random search method that consists of optimization techniques. It can solve multi-targeted optimization problems and explore different parts of the solution space. As a result, a diversified collection of solutions can be found, with more variables that can be optimized at the same time. In this article several MTGA were used and compared.Genetic algorithms are a good tool for image processing in the absence of a high-quality labeled data set, which is either a result of the long work of many researchers or the contribution of large sums of money to obtain an array of data from external sources.In this article, we will use genetic algorithms to solve the problem of image segmentation.
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Li, Xiao Guang. "Research on the Automatic Multilevel Thresholding for Athletic Image Segmentation Based on Block Sampling and Genetic Algorithm." Advanced Materials Research 791-793 (September 2013): 2007–12. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2007.
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The research on new image segmentation algorithm is a very meaningful work in the processing of image. In the process, it will produce large amount of data redundancy. The efficient algorithm not only can greatly improve the quality of image treatment but also can greatly reduce the time and cost of the treatment. In this context, the paper analyzes several image processing algorithms commonly used in recent years and presents a new computer image processing algorithm--AMT-GA algorithm. In order to verify the effectiveness of AMT-GA algorithm, this paper takes the process of athletics image for example and compares the consistency and time of image segmentation with other literature results and ultimately finds that the consistency of AMT-GA algorithm reaches 0.99. The time in the algorithm execution is only 0.81 which not only achieves effective segmentation of the image but also saves the cost of computing. It also provides a theoretical reference for the research of computer graphics technology.
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J. Hemalatha, R., Dr V. Vijaybaskar, A. Josephin Arockia Dhivya, and . "Early detection of joint abnormalities from ultrasound images." International Journal of Engineering & Technology 7, no. 2.25 (2018): 105. http://dx.doi.org/10.14419/ijet.v7i2.25.16569.
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Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly
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He, Yun, Yan Gang Wu, Jia Lin Tian, and Wen Xu. "Image Restoration of Depth of Field Extension Imaging System Based on Genetic Algorithm." Applied Mechanics and Materials 539 (July 2014): 131–35. http://dx.doi.org/10.4028/www.scientific.net/amm.539.131.
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Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm and improves the arithmetic speed of the algorithm, which achieves better image restoration effect. And the paper compares the image restoration quality of traditional algorithm, standard genetic algorithm and improved genetic algorithm to prove the feasibility of applying genetic algorithm to image restoration.
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Wei, Wei, Liang Liu, Zhong Qin Hu, and Yu Jing Zhou. "Rigid Medical Image Registration Based on Genetic Algorithms and Mutual Information." Applied Mechanics and Materials 665 (October 2014): 712–17. http://dx.doi.org/10.4028/www.scientific.net/amm.665.712.
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With the variety of medical imaging equipment’s application in the medical process,medical image registration becomes particularly important in the field of medical image processing,which has important clinical diagnostic and therapeutic value. This article describes the matrix conversion method of the rigid registration model, the basic concepts and principles of the mutual information algorithm ,the basic idea of genetic algorithms and algorithm’s flow , and the application of the improved genetic algorithms in practice. The rigid registration of two CT brain bones images uses mutual information as a similarity measure, genetic algorithm as the search strategy and matlab as programming environment. Using the three-point crossover technique to exchange the three parameters in the rigid transformation repeectively to produce new individuals, the genetic algorithm’s local search ability enhanced and the prematurity phenomenon can be reduced through the depth study of the basic genetic algorithm. The experiments show that the registration has high stability and accuracy.
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Kawamoto, Kazuhiko. "Special Issue on Advances on Intelligent Multimedia Processing." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 2 (2010): 121. http://dx.doi.org/10.20965/jaciii.2010.p0121.
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As computer and sensor technology advances make increasing amounts of multimedia data available, multimedia processing methodologies are needed for multimodal data fusion, efficient data processing, information extraction, and added-value data generation. This special issue introduces the following latest practical developments in image processing, acoustical signal processing, pattern recognition, data mining, and visualization: S. Kobashi et al. propose a robust algorithm for reconstructing total knee arthroplasty implants from X-ray cone-beam images. Y. Hatakeyama et al. propose an algorithm for classifying ultrasonic abdominal images with the help of reports in text format. T. Miyazaki et al. analyze breathy and rough speech by the elderly. M. Nii et al. present a genetic algorithm for classifying nursing-care text. These papers provide useful insights into medical diagnosis and nursing care in the aging society coming to dominate 21st century. T. Toyota et al. visualize Japanese law networks based on granular computing. K. Sawase et al. present a management system for large databases of tagged images. These graphical user interface techniques will be helpful to those who are not computer experts. H. Kawano et al. propose a classification algorithm for segmenting range data into multiple quadric surfaces. Y. Arai et al. propose a nearest-neighbor method for personal authentication. H. Kawano et al. present an algorithm for extracting the structure of decorative characters based on graph spectral decomposition. These classification and discriminant algorithms provide a basis for multimedia data processing. G. Tanaka et al. propose a color transfer algorithm. H. Orii et al. present an image completion algorithm. G. Tanaka et al. present an image enhancement algorithm to noisy images. M. Mizumachi et al. propose a stochastic algorithm for estimating sound source direction. These image and acoustical processing algorithms improve the quality of digital data and will provide new applications in these areas. We thank the authors and referees whose invaluable work and kind comments have made this special issue possible and improved overall paper quality.
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Mondal, Probir, Debranjan Pal, Krishnendu Basuli, and Pratyay Banerjee. "Visualizing Genetic Patterns: A Comparative Analysis of DNA Sequences Through Image Processing." International Journal of Microsystems and IoT 2, no. 2 (2024): 586–90. https://doi.org/10.5281/zenodo.10803111.
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A comparative analysis of DNA sequence is investigated through Image Processing. The underlying algorithm transforms, in a novel way, genetic data into images. The information is encoded by using the pixel intensities representing the four constituent nucleotide bases viz. A, T, C and G. These sequences are then employed to generate visual representations, facilitating an intuitive understanding of complex genetic information. Our study integrates machine learning techniques to compare and cluster these DNA sequence-based images, offering a powerful tool for classification. By leveraging machine learning algorithms, we enable the automated recognition of genetic similarities/dissimilarities within genomes which, in turn, streamline the time-consuming process of traditional sequence comparison.
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Mutar, Jinan Redha. "Study of Methodologyof Optimizing Image Segmentation and Processing Using Genetic Algorithm." International Journal of Advances in Scientific Research and Engineering 08, no. 12 (2022): 15–21. http://dx.doi.org/10.31695/ijasre.2022.8.12.3.
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In the literature, several worksfocal point on the description of contrast metrics and standards that allow to count the overall an image processing algorithm performance. These assessment standards can be investigated to outline new photo processing algorithms by way of optimizing them. In this work, we suggest a universal scheme to phasephotographswith the aid of a genetic algorithm. The developed approach makes use of an estimation standard which counts the first-class of a photo segmentation outcome. The suggested type segmentationapproach can combine a local ground reality when it is accessible in order to set the preferred degree of the last result accuracy. A genetic algorithm(G-A) is then investigated in sort to decide the first-rate mixture of statistics excerpted with the aidof the chosen standard. Then, we exhibit that this method can either be utilized for gray-levels or multicomponent snap shots in a supervised context or in an unsupervised one. Last, we exhibit the efficiency of the suggested approachviaseveral experimental outcomes on a number of(levels of gray) and multi-components images.
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Wu, Long, Jie Chen, Shuyu Chen, et al. "Hybrid Dark Channel Prior for Image Dehazing Based on Transmittance Estimation by Variant Genetic Algorithm." Applied Sciences 13, no. 8 (2023): 4825. http://dx.doi.org/10.3390/app13084825.
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Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free images with high quality, a hybrid dark channel prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The variant genetic algorithm (VGA) is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the modified dark channel prior algorithm is used to obtain the dehazed image. Compared with three other modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows superior objective indexes in the mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and information entropy (E) for different haze degrees.
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K., Pavithra. "Analytical Method of Multi-Objective Genetic Algorithm with Multi-Objective Messy Genetic Algorithm in Satellite Image Segmentation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 3, no. 3 (2018): 168–73. https://doi.org/10.5281/zenodo.4301122.
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Image can be dividing into different Segmentation. In image processing , the important task is Segmentation process methods. This method involves such as K-means clustering, watershed segmentation, Fuzzy c-Means, Iterative Self Organizing Data. Clustering methods depends powerfully on the selection of the primary spectral signatures which represents initial cluster centers. Normally, this is either done physically or erratically based on statistical operations. In this case the outcome is random and sometime inaccurate. In base paper an unsupervised method based on Multi-Objective Genetic Algorithm (MO-GA) for the selection of spectral signature from satellite images is implemented. The goal is to make greatest cluster centers as an initial population for any segmentation technique. Experimental results are conducted using high-resolution SPOT V satellite image and the verification of the segmentation results is based on a very elevated resolution satellite image of kind Quickbird. The spectral signatures method to Fuzzy c-means and K-means by MO-GA method increased the speed of the clustering algorithm to approximately4 times the speed of the random based selection of signatures. In this paper unsupervised method is comparative with Multi-Objective Messy Genetic Algorithm(MOMGA) with existing MO-GA methods for the selection of spectral signature using satellite images.
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Li, Mingfeng. "Comprehensive Review of Backpropagation Neural Networks." Academic Journal of Science and Technology 9, no. 1 (2024): 150–54. http://dx.doi.org/10.54097/51y16r47.
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The Backpropagation Neural Network (BPNN) is a deep learning model inspired by the biological neural network. Introduced in the 1980s, the BPNN quickly became a focal point in neural network research due to its outstanding learning capability and adaptability. The network structure consists of input, hidden, and output layers, and it optimizes weights through the backpropagation algorithm, widely applied in image recognition, speech processing, natural language processing, and more. The mathematical model of neurons describes the relationship between input and output, and the training process involves adjusting weights and biases using optimization algorithms like gradient descent. In applications, BPNN excels in image recognition, speech processing, natural language processing, and financial forecasting. Researchers continuously experiment with optimization algorithms, including the Grey Wolf Algorithm, Genetic Algorithm, Particle Swarm Algorithm, Simulated Annealing Algorithm, as well as comprehensive strategies and improved gradient descent algorithms. In the future, with the ongoing development of deep learning, BPNN is poised to play a crucial role in tasks such as image recognition and speech processing.
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Tang, Jinyu. "An Optimized Digital Image Processing Algorithm for Digital Oil Painting." Mobile Information Systems 2022 (May 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/4956839.
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Human civilization’s accomplishments have grown with the passage of time and the advancement of society. With the fast growth of computer networks and information technology, the conventional method of information transmission based on words cannot fulfill the demands of people in the current era. As a result, in this age of extensive information and image processing techniques, images as a means of information sharing are becoming increasingly popular. As we know, digital image processing knowledge has a far-reaching impact in the field of artistic creation, among which the creation of oil painting is facing severe challenges. Aiming at the problem that the effect of digital oil painting is not ideal, this paper aims to study digital oil painting by using digital image processing technology. This paper first uses the image edge recognition based on the improved Canny algorithm to detect the edge of the oil painting image, then uses the nonlinear image enhancement algorithm to enhance the effect of the oil painting image, then uses the improved genetic algorithm to segment the image, and finally enlarges the oil painting image to calibrate the color of the oil painting image. Experiments reveal that the proposed approach outperforms existing algorithms in terms of edge detection data integrity, high-quality coefficient index of image enhancement, picture segmentation running time, and the ability to successfully increase the visual effect of oil painting.
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Tahir KASIM, Ghada Mohammad, Zahraa Mazin ALKATTAN, and Nadia Maan MOHAMMED. "Hybrid System for Image Restoration." International Research Journal of Innovations in Engineering and Technology 08, no. 01 (2024): 168–77. http://dx.doi.org/10.47001/irjiet/2024.801020.
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The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results.
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WANG, Li, and Wei WANG. "Hyperspectral image compressed processing: Evolutionary multi-objective optimization sparse decomposition." PLOS ONE 17, no. 4 (2022): e0267754. http://dx.doi.org/10.1371/journal.pone.0267754.
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In the compressed processing of hyperspectral images, orthogonal matching pursuit algorithm (OMP) can be used to obtain sparse decomposition results. Aimed at the time-complex and difficulty in applying real-time processing, an evolutionary multi-objective optimization sparse decomposition algorithm for hyperspectral images is proposed. Instead of using OMP for the matching process to search optimal atoms, the proposed algorithm explores the idea of reference point non-dominated sorting genetic algorithm (NSGA) to optimize the matching process of OMP. Take two objective function to establish the multi-objective sparse decomposition optimization model, including the largest inner product of matching atoms and image residuals, and the smallest correlation between atoms. Utilize NSGA-III with advantage of high accuracy to solve the optimization model, and the implementation process of NSGA-III-OMP is presented. The experimental results and analysis carried on four hyperspectral datasets demonstrate that, compared with the state-of-the-art algorithms, the proposed NSGA-III-OMP algorithm has effectively improved the sparse decomposition performance and efficiency, and can effectively solve the sparse decomposition optimization problem of hyperspectral images.
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Chen, Shou-Ming, and Jun-Hui Zhang. "Genetic Algorithm in Data Mining of Colorectal Images." Computational and Mathematical Methods in Medicine 2021 (October 15, 2021): 1–8. http://dx.doi.org/10.1155/2021/3854518.
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There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research.
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Liu, Hai Yan. "Application of Genetic Algorithm to Image Segmentation." Applied Mechanics and Materials 685 (October 2014): 642–45. http://dx.doi.org/10.4028/www.scientific.net/amm.685.642.
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Image segmentation is very important in image analysis that needs to separate the related area for the general target distinguishing and analyzing an image, and it can make further use of the target, such as characteristic pick-up and measure on the basis of image processing disposal. In this dissertation, image segmentation based on genetic algorithm will be described. The selection of threshold and the process about image segmentation based on genetic algorithm are described. Finally, image segmentation based on genetic algorithm is used on a picture by Matlab, the result can be accepted. Therefore, it is significant to make analysis on image segmentation based on genetic algorithm.
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Gdeeb, Rasha Talib. "Using Genetic Algorithms to Segment Images: A Review." Al-Kitab Journal for Pure Sciences 7, no. 2 (2023): 1–15. http://dx.doi.org/10.32441/kjps.07.02.p1.
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The genetic algorithm plays a pivotal role in image processing, particularly in the critical stage of image segmentation. The process of segmenting photographs is an essential method in the field. Identifying objects, extracting features for object recognition, and classifying are integral components of image processing. However, the effectiveness of these activities relies on the quality of the operations performed. The work at hand in the domain of image processing is notably arduous and intricate. The segmentation of photos cannot be consistently achieved through the utilization of a singular approach. Nevertheless, it is not possible to consistently classify photos into extensive categories. The complexity inherent in the image segmentation task necessitates careful consideration when determining a suitable set of parameters to employ. The arduous task of selecting picture parameters the picture segmentation problem encompasses various factors that contribute to the complexity of the selection process. An optimization problem is employed to efficiently locate the global maximum inside a given search space, with the problem being formulated as a Genetic Algorithm. Subsequently, the task of determining the most suitable segmentation criteria for an image is successfully overcome. The primary objective of this study was to investigate the viability of employing genetic algorithms within the domain of image segmentation.
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Hajira Be, A. B. "Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing." Journal of Computer Allied Intelligence 2, no. 3 (2024): 55–66. http://dx.doi.org/10.69996/jcai.2024015.
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This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonstrates superior performance in medical image classification with an accuracy of 92.5%, showcasing its ability to achieve high precision and recall rates critical for medical diagnostics. GA proves effective for satellite imagery with an accuracy of 87.3%, while the CNN + Feature Fusion Network excels in MRI scans with 89.8% accuracy. Lasso Regression, though slightly less accurate at 85.6%, efficiently selects features for microscopy images within a shorter computational time. These findings highlight the strengths and trade-offs of each algorithm across different image processing domains, providing insights for selecting appropriate feature selection methods tailored to specific imaging applications.
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Et. al., Vigneshwari K,. "Genetic Algorithm Based Fuzzy Local Informationc-Means (Gaflicm) Clustering Algorithm And Hybrid Kernel Convolution Neural Network (Hkcnn) With Distributed Processing Framework For Brain Mri Images." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 5639–56. http://dx.doi.org/10.17762/turcomat.v12i10.5375.
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Cancer is the large important origin of casualty in today world. Among many cancers, brain cancer has been becomes one of the lowest living rate. It is formed based on the brain tumor. However brain tumors are able to have diverse categories based on their shape, texture, and position. Appropriate identification and retrieval of the tumor types create potential the doctor to compose the right cure option and assist keep the patient's life. Image processing has gained wide attention in medical analysis and health in recently. In general image processing methods, brain Magnetic Resonance Imaging (MRI) image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs. Hence, parallel computing and distributed system has been performed increasingly for brain MRI images in recently. In this paper, a novel Medical Image Cloud Processing (MICP) based distributed processing framework is proposed for brain MRI images by lesser computational time. In this work, image preprocessing is done by using the Adaptive Median Filtering (AMF) and image enhancement by Histogram Equalization (HE). The proposed MICP framework includes of Static Medical Image Cloud Processing (SMICP) and Dynamic Medical Image Cloud Processing (DMICP). In MICP framework, SMICP consists of two methods called Pure-Image and Big-Image. These methods are integrated to Genetic Algorithm based Fuzzy Local Information C-Means-MapReduce (GAFLICM-MR) algorithm to attain more optimized design and higher effectiveness. The core design of GAFLICM-MR framework is to make use of the rich computing resources given by means of the distributed system consequently as to apply efficient parallel processing. GAFLICM algorithm is also used as brain tumor segmentation in MRI images. In MICP framework, DMICP is developed via a parallel processing process of the distributed system. For retrieval and detection of brain Magnetic MRIimages into normal and tumor, Hybrid Kernel Convolution Neural Network (HKCNN) is developed in this work. Finally the results of the HKCNN classifier are compared to other previous works like precision, recall, f-measure, accuracy, time and memory.
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Xu, Taotao, Lijian Yao, Lijun Xu, Qinhan Chen, and Zidong Yang. "Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm." Sustainability 15, no. 4 (2023): 3089. http://dx.doi.org/10.3390/su15043089.
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To solve the problems of the low target-positioning accuracy and weak algorithm robustness of target-dosing robots in greenhouse environments, an image segmentation method for cucumber seedlings based on a genetic algorithm was proposed. Firstly, images of cucumber seedlings in the greenhouse were collected under different light conditions, and grayscale histograms were used to evaluate the quality of target and background sample images. Secondly, the genetic algorithm was used to determine the optimal coefficient of the graying operator to further expand the difference between the grayscale of the target and background in the grayscale images. Then, the Otsu algorithm was used to perform the fast threshold segmentation of grayscale images to obtain a binary image after coarse segmentation. Finally, morphological processing and noise reduction methods based on area threshold were used to remove the holes and noise from the image, and a binary image with good segmentation was obtained. The proposed method was used to segment 60 sample images, and the experimental results show that under different lighting conditions, the average F1 score of the obtained binary images was over 94.4%, while the average false positive rate remained at about 1.1%, and the image segmentation showed strong robustness. This method can provide new approaches for the accurate identification and positioning of targets as performed by target-dosing robots in a greenhouse environment.
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Khudov, Hennadii, Oleksandr Makoveichuk, Temir Kalimulin, Vladyslav Khudov, and Nazar Shamrai. "THE METHOD FOR APPROXIMATING THE EDGE DETECTION CONVOLUTIONAL OPERATOR USING A GENETIC ALGORITHM FOR SEGMENTATION OF COMPLEX-STRUCTURED IMAGES." Advanced Information Systems 8, no. 4 (2024): 5–12. http://dx.doi.org/10.20998/2522-9052.2024.4.01.
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The subject matter of the study in the article is the method for approximating the convolutional operator for edge detection using a genetic algorithm for segmentation of complex-structured images. The goal is to develop a method for approximating the convolutional operator for edge detection using a genetic algorithm for the segmentation of complex-structured images. The tasks are: analysis of known methods of segmentation of optoelectronic images, development of a method for approximating the edge detection convolutional operator using a genetic algorithm for segmenting complex-structured images, practical validation of the method for approximating the edge detection convolutional operator using a genetic algorithm for segmenting complex-structured images. The methods used are: digital image processing methods, data clustering techniques, matrix theory mathematics, swarm intelligence methods, the genetic algorithm, mathematical modelling techniques, optimization theory methods, as well as analytical and empirical methods for image comparison. The following results are obtained. The advantages and disadvantages of the main known methods for segmenting optoelectronic images have been identified. It has been established that the most effective segmentation methods for images from space-based optoelectronic observation systems (complex-structured images) are those based on swarm intelligence and genetic algorithms. An important case of segmentation – binarization (segmentation into two classes), has been considered. The task of binarization has been formalized, and the concepts of structural and amplitude predicates have been introduced. The method for segmenting complex-structured images has been improved, in which, unlike existing methods, a genetic algorithm is used for approximating the edge detection convolutional operator, facilitating segmentation of images at various scales with later integration of the results. A visual assessment of the quality of the segmented image has been conducted using the improved method. Conclusions. The method for segmenting complex-structured images has been improved, in which, unlike existing methods, a genetic algorithm is employed to approximate the edge detection convolutional operator, easing segmentation of images at various scales with later integration of the results. A visual assessment of the quality of the segmented image using the improved method shows a significant reduction in the number of noise objects present in the segmented image.
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Wu, Yong Zhuo, Zhen Tu, and Lei Liu. "Local Statistic Enhancement of Digital Image Based on Genetic Algorithm." Applied Mechanics and Materials 427-429 (September 2013): 1836–40. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1836.
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Iamge repair using the digital image processing technology has become a new research point in computer application. A novel method of local statistic enhancement based on genetic algorithm is proposed in this paper for the image enhancement. The modified amplified function are used as the jugement criterion, and the optimal paremeters are searched by the genetic algorithm. Experimental results show that the quality of images is improved dramatically by using this method.
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Preetam, Singh* Mr Jatinder Sharma. "A REVIEW PAPER ON IMAGE DENOISING BY LOW RANK MATRIX DECOMPOSITION ANDGENETIC ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 166–681. https://doi.org/10.5281/zenodo.56923.
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A great challenge in the field of image processing nowadays is image denoising. Although, there have beenproposed various methods and algorithms for the same, but,most of them have not attained the desirable results. Theperformance does not match with the assumed one.In the field of image denoising, nonlocal image denoising algorithm is a nonlinear, space average denoising algorithm, it will not cause boundary blurred, and it is an effective denoising algorithm. But its application still has limitations because of it taking much longer time, in this paper, the method was improved, image signal can be divided into high frequency and low frequency part using low rank matrix decomposition, nonlocal denoising algorithm is used in low-frequency approximate signal, for high frequency detail signals using wavelet filtering method for denoising. Then, noise and aliasing artifacts are removed from the structured matrix by applying low rank matrix decomposition method with Genetic Algorithm(GA). We use Genetic Algorithm for unwanted features reduction from the high and low frequency signals. The Denoising of image is implemented using Image Processing Toolbox. This work test and found suitable for its purpose.For the implementation of this proposed work we use the Matlabsoftware.
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Si, Lipeng, Xiuhua Hu, and Baolong Liu. "Image Matching Algorithm Based on the Pattern Recognition Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (March 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/7760437.
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Image matching is an important topic in image processing. Matching technology plays an important role in and is the basis for image understanding. In order to solve the shortcomings of slow image matching and low matching accuracy, a matching method based on improved genetic algorithm is proposed. The main improvement of the algorithm is the use of self-identifying crossover operators for crossover operations to avoid premature population maturity. According to the characteristics of the image data, new intersection and mutation operators are defined by the new coding method. The sampling method is used to initialize the population method, introduce an evolution strategy, reduce the number of iterations, and effectively reduce the amount of calculation. The experimental results show that the algorithm can guarantee the matching accuracy and that the calculation time is much shorter than that of the original algorithm. In addition, the image matching calculation time per frame of the algorithm is basically unchanged, which is convenient for engineering applications.
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ZHENG, Guang, and Yiran WANG. "Adaptive Genetic Algorithm Based on Chaotic Intelligent Algorithm to Image Restoration Research." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 7 (2016): 305–14. http://dx.doi.org/10.14257/ijsip.2016.9.7.27.
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C, Nithyasree, Stanley D, and Subalakshmi K. "Brain Tumor Detection using Image Processing." International Journal on Cybernetics & Informatics 10, no. 2 (2021): 319–25. http://dx.doi.org/10.5121/ijci.2021.100235.
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Brain tumor extraction and its analysis are challenging tasks in medical image processing because brain image is complicated .Segmentation plays a very important role in the medical image processing.In that way MRI (magnetic resonance imaging )has become a useful medical diagnostic tool or the diagnosis o brain & other medical images.In this project, we are presenting a comparative study of three segmentation methods implemented or tumor detection .The method includes kmeans clustering using watershed algorithm . Optimized k-means and optimized c-means using genetic algorithm.
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Raihan, Md, Kh Mohaimenul, and Md Mehedi. "Data Security Throws Image processing by Blowfish Algorithm, Genetic Algorithm and LSB." International Journal of Computer Applications 148, no. 1 (2016): 1–3. http://dx.doi.org/10.5120/ijca2016909946.
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Mishra, Shashwati, and Mrutyunjaya Panda. "Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions." International Journal of Fuzzy System Applications 8, no. 4 (2019): 39–59. http://dx.doi.org/10.4018/ijfsa.2019100103.
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Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.
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Kulkarni, Dr Jyoti S. "Genetic Algorithm Approach for Image Fusion: A Simple Method and Block Method." International Journal of Innovative Technology and Exploring Engineering 11, no. 6 (2022): 16–21. http://dx.doi.org/10.35940/ijitee.f9895.0511622.
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The sensors available nowadays are not generating images of all objects in a scene with the same clarity at various distances. The progress in sensor technology improved the quality of images over recent years. However, the target data generated by a single image is limited. For merging information from multiple input images, image fusion is used. The basis of image fusion is on the image acquisition as well as on the level of processing and under this many image fusion techniques are available. Several input image acquisition techniques are available such as multisensor, multifocus, and multitemporal. Also, image fusion is performed in four different stages. These levels are the level of the signal, pixel level, level of feature, and level of decision-making. Further, the fusion methods are divided into two domains i.e spatial and frequency domains. The fusion in spatial domain images uses inputs directly to work on pixels, while the transition refers to frequency domain image fusion on input images before fusion. The limitation of spatial domain image fusion is spectral degradation. To overcome this limitation, the fusion of transform domain images is preferred which uses several transforms. The results generated by transform methods are superior to spatial domain methods. But there is a scope to improve the results or to find the optimized results. Optimization can be achieved by using evolutionary approaches. The evolutionary computation approach is an effective way of finding the required solution for a complex problem. An evolutionary algorithm is a guided random search used for optimization. The biological model of evolution and natural selection inspires it. The different types of evolutionary computing algorithms include Genetic algorithm, Genetic Programming, Evolutionary programming, Learning Classifier System, Ant Colony Optimization, Artificial Bee Colony Optimization, Particle Swarm Optimization, Evolution strategy, Swarm intelligence, Tabu Search, Cuckoo Search, etc. Three genetic algorithm-based image fusion techniques are proposed: a genetic algorithm with one population, a genetic algorithm with separate populations, and a block method. In the block method, an array of numbers in one chromosome is generated. The result obtained by the proposed techniques are compared with existing methods and observed that the results are improved. The graphical representation of performance parameters reflects that the block method is better.
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Dr., Jyoti S. Kulkarni. "Genetic Algorithm Approach for Image Fusion: A Simple Method and Block Method." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 6 (2022): 16–21. https://doi.org/10.35940/ijitee.F9895.0511622.
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<strong>Abstract:</strong> The sensors available nowadays are not generating images of all objects in a scene with the same clarity at various distances. The progress in sensor technology improved the quality of images over recent years. However, the target data generated by a single image is limited. For merging information from multiple input images, image fusion is used. The basis of image fusion is on the image acquisition as well as on the level of processing and under this many image fusion techniques are available. Several input image acquisition techniques are available such as multisensor, multifocus, and multitemporal. Also, image fusion is performed in four different stages. These levels are the level of the signal, pixel level, level of feature, and level of decision-making. Further, the fusion methods are divided into two domains i.e spatial and frequency domains. The fusion in spatial domain images uses inputs directly to work on pixels, while the transition refers to frequency domain image fusion on input images before fusion. The limitation of spatial domain image fusion is spectral degradation. To overcome this limitation, the fusion of transform domain images is preferred which uses several transforms. The results generated by transform methods are superior to spatial domain methods. But there is a scope to improve the results or to find the optimized results. Optimization can be achieved by using evolutionary approaches. The evolutionary computation approach is an effective way of finding the required solution for a complex problem. An evolutionary algorithm is a guided random search used for optimization. The biological model of evolution and natural selection inspires it. The different types of evolutionary computing algorithms include Genetic algorithm, Genetic Programming, Evolutionary programming, Learning Classifier System, Ant Colony Optimization, Artificial Bee Colony Optimization, Particle Swarm Optimization, Evolution strategy, Swarm intelligence, Tabu Search, Cuckoo Search, etc. Three genetic algorithm-based image fusion techniques are proposed: a genetic algorithm with one population, a genetic algorithm with separate populations, and a block method. In the block method, an array of numbers in one chromosome is generated. The result obtained by the proposed techniques are compared with existing methods and observed that the results are improved. The graphical representation of performance parameters reflects that the block method is better.
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Rasika, Joat, A. P. Thakare Dr., Ketaki Kalele Dr., and Viashali Thakare Dr. "Genetic Programming Approach for Oral Cancer Detection and its Image Restoration." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2422–26. https://doi.org/10.31142/ijtsrd12787.
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Cancer is one of the leading causes of death in developing countries. Cancers are of different types like breast cancer, lung cancer, skin cancer and so on. Oral Cancer is one of the types of cancers. Oral cancer is a very common type of cancer. This Oral Cancer is observed in both males as well as females. It is a big challenge to detect Oral Cancer. This is a time consuming process in medical image processing. Detection and prevention of oral cancer at early stage is critical. But it increases the chances of survival. This work presents the detection of oral cancers using Image Processing. Clinical images which are RGB images and microscopic images are used as the input image for detection of cancer. At first, Gabor filter is used to remove noise from the images. This is used for image enhancement in image preprocessing step. Genetic Algorithm is used to extract the features of tumors from the enhanced image. GA is used for segmentation of image. The proposed algorithm provides better segmentation. Genetic Programming GP , is a computation technique that can evolve better solutions for image classification problems. Rasika Joat | Dr. A. P. Thakare | Dr. Ketaki Kalele | Dr. Viashali Thakare "Genetic Programming Approach for Oral Cancer Detection and its Image Restoration" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd12787.pdf
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Roy, Abhisek, Pranab Kanti Roy, Anirban Mitra, et al. "Artificial bee colony-based nonrigid demons registration." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3951. http://dx.doi.org/10.11591/ijece.v14i4.pp3951-3961.
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The artificial bee colony (ABC) algorithm has gained popularity in recent years for its ability to solve optimization problems. The accuracy and resilience of ABC-based image processing techniques have demonstrated encouraging outcomes. The ABC method is an excellent solution for image processing issues since it has the ability to swiftly and effectively explore the search space. The current research intends to address image registration issues by refining the existing image registration strategy using ABC algorithm. The process of nonrigid demons registration is frequently employed in the processing of medical images. The combination of these two techniques is referred to as the ABC-based nonrigid demons registration method. The proposed method has shown superior performance in registration accuracy and efficiency compared to other existing methods. Applications in medical image analysis and computer-assisted diagnosis are highly promising for the ABC-based nonrigid demons registration. Particle swarm optimization (PSO) and frameworks based on genetic algorithms (GA) have been compared with the suggested framework. The observed results showed improved accuracy and faster convergence in ABC-based demons registration.
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Xi, Erhui, and Jiali Zhang. "Research on Image Deblurring Processing Technology Based on Genetic Algorithm." Journal of Physics: Conference Series 1852, no. 2 (2021): 022042. http://dx.doi.org/10.1088/1742-6596/1852/2/022042.
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Marwan, Ali Shnan, H. Rassem Taha, and Saradatul Akmar Zulkifli Nor. "Facial image retrieval on semantic features using adaptive mean genetic algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 882–96. https://doi.org/10.12928/TELKOMNIKA.v17i2.3774.
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The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
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Liu, Xiao Jun. "Fire Detection Technology Based on Infrared Image Processing." Applied Mechanics and Materials 347-350 (August 2013): 3426–30. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3426.
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This paper proposes a method to detect fire by processing the images captured by an CCD camera with infrared filter. First, the flame objects are detected by using two consecutive frames difference and background difference. Using genetic algorithm to optimize the threshold, the image is segmented by using Ostu. The boundary chain code is acquired on the basis of extracting flame contour. Lastly, shape feature, change feature and edge jitter feature are used to judge whether the fire exists. This method suppresses visible light interference. The experiment results show that the algorithm has higher reorganization rate in different environment.
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Song, Xiao Ling. "A Novel Modified Algorithm for Local Statistic Enhancement." Advanced Materials Research 926-930 (May 2014): 2910–13. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2910.
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Image repair using the digital image processing technology has become a new hot point in the cultural relic protection. To study of ancient fresco restoration techniques, A novel algorithm of local statistic enhancement image is proposed in this paper for the reparation of ancient fresco. The modified amplified function and the rubber band conversion algorithm are used as the jugement criterion, and the optimal paremeters are searched by the genetic algorithm (GA). Experimental results show that the quality of images is improved compared with the traditional.
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K., Beulah Suganthy. "Identification of Disease in Leaves using Genetic Algorithm." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1264–67. https://doi.org/10.31142/ijtsrd22901.
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Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy "Identification of Disease in Leaves using Genetic Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
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Zhao, Jing. "Art Visual Image Transmission Method Based on Cartesian Genetic Programming." Scientific Programming 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/4628563.
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Because most of the traditional artistic visual image communication methods use the form of modeling and calculation, there are some problems such as long image processing time, low success rate of image visual communication, and poor visual effect. An artistic visual image communication method based on Cartesian genetic programming is proposed. The visual expression sensitivity difference method is introduced to process the image data, the neural network is used to identify the characteristics of the artistic visual image, the midpoint displacement method is used to remove the folds of the artistic visual image, and the processed image is formed under the above three links. The Cartesian genetic programming algorithm is used to encode the preprocessed image, improve the fitness function, select the algorithm to improve the operation, design the image rendering platform, input the processed image to the platform, and complete the artistic visual image transmission. The analysis of the experimental results shows that the image processing time of this method is short, the success rate of visual communication is high, and the image visual effect is good, which can obtain the image processing results satisfactory to users.
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Yang, Zeyin. "Application and Development of Digital Enhancement of Traditional Sculpture Art." Scientific Programming 2022 (February 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/9095577.
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Sculpture art, as an important carrier of spiritual civilization, also portrays a prosperous scene as an industry with urban and cultural development. Three-dimensional technology offers a new platform for sculpture creation, allowing for the digitization of sculpture works via electronic information technology, and the display of sculpture works in front of people via displays, facilitating the exchange and dissemination of information and promoting the growth and progress of the entire sculpture creation industry. We plan to use digital enhancement technology to conduct small-scale creation experiments on traditional sculpture works, discuss the method of GA (Genetic Algorithm) in image restoration processing, investigate the method of image segmentation processing based on the genetic algorithm, and propose the method of image segmentation processing based on the fuzzy membership surface genetic algorithm, in order to verify and solve the creation difficulties of traditional sculpture works.
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Kong, Luoyi, Mohan Huang, Lingfeng Zhang, and Lawrence Wing Chi Chan. "Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation." Bioengineering 11, no. 3 (2024): 270. http://dx.doi.org/10.3390/bioengineering11030270.
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Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.
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Feng Shou, Zhang, Dong Fang, Liu Jian Ting, and Meng Xin. "An improved method of microscopic image segmentation." MATEC Web of Conferences 176 (2018): 01041. http://dx.doi.org/10.1051/matecconf/201817601041.
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In order to improve the effectiveness and accuracy of image processing in modern medical inspection, a segmentation image optimization algorithm of improved two-dimensional maximum entropy threshold based on genetic algorithm combined with mathematical morphology is proposed, in view of the microscopic cell images characteristic and the shortcomings of the traditional segmentation algorithm. Through theoretical analysis and contrast test, the segmentation method proposed is superior to the traditional threshold segmentation method in microscopic cell images, and the average segmentation time of the improved algorithm is 73% and 44% higher than the traditional two-dimensional maximum entropy threshold and the improved two-dimensional maximum entropy threshold.
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Mo, Shu, and Xiao Dong Yang. "A Color Image Enhancement Based on Improved Genetic Algorithm." Applied Mechanics and Materials 475-476 (December 2013): 342–46. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.342.
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Image enhancement plays a fundamental role in image processing. Because of the influence of internal and external factors such as the sensor precision,light,random noise.It has a certain difficulty for image recognition directly.To solve such problems, we proposed a novel and efficient enhancement method based on improved genetic algorithm. This paper combined median filter and improved genetic algorithm to enhance the visual effect of color image. Simulation results proved that the proposed method was stronger than traditional method in terms of details and contrast enhancement and natural visual effect.
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Ali, Alzahrani, and Al-Amin Bhuiyan Md. "Feature selection for urban land cover classification employing genetic algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 793–802. https://doi.org/10.11591/eei.v11i2.3399.
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Feature selection has attained substantial research interest in image processing, computer vision, pattern recognition and so on due to tremendous dimensional reduction in image analysis. This research addresses a genetic algorithm based feature selection strategy for urban land cover classification. The principal purpose of this research is to monitor the land cover alterations in satellite imagery for urban planning. The method is based on object based classification by detecting the object area of a given image with the knowledge of visual information of the object from remote sensing images. The classification system is organized through a multilayer perceptron with genetic algorithm (MLPGA). Experimental results explicitly indicate that this MLPGA based hybrid feature selection procedure performs classification with sensitivity 94%, specificity 90% and precision 89%, respectively. This MLPGA centered hybrid feature selection scheme attains better performance than the counterpart methods in terms of classification accuracy.
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Roy, Abhisek, Roy Pranab Kanti, Anirban Mitra, et al. "Artificial bee colony-based nonrigid demons registration." Artificial bee colony-based nonrigid demons registration 14, no. 4 (2024): 3951–61. https://doi.org/10.11591/ijece.v14i4.pp3951-3961.
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The artificial bee colony (ABC) algorithm has gained popularity in recent years for its ability to solve optimization problems. The accuracy and resilience of ABC-based image processing techniques have demonstrated encouraging outcomes. The ABC method is an excellent solution for image processing issues since it has the ability to swiftly and effectively explore the search space. The current research intends to address image registration issues by refining the existing image registration strategy using ABC algorithm. The process of nonrigid demons registration is frequently employed in the processing of medical images. The combination of these two techniques is referred to as the ABC-based nonrigid demons registration method. The proposed method has shown superior performance in registration accuracy and efficiency compared to other existing methods. Applications in medical image analysis and computer-assisted diagnosis are highly promising for the ABC-based nonrigid demons registration. Particle swarm optimization (PSO) and frameworks based on genetic algorithms (GA) have been compared with the suggested framework. The observed results showed improved accuracy and faster convergence in ABC-based demons registration.
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A, Mrs Nandhini, and Muthu Sahin S H. "Lung Cancer Detection Using SVM Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 09 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem37556.
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In this study investigates the formation of Lung cancer detection using the techniques image processing. The oncologist considered for blood test result. The system formed can take any of medical image within the three choices consisting of CT, MRI and Ultrasound image. This study explores the application of Support Vector Machine (SVM) classification algorithms for the detection of lung cancer, this improves its ability to handle high-dimensional data and provide robust classification result. The model proposed here is developed using PSO, genetic optimization and SVM algorithm used for feature selection and classification. This paper uses image processing to detect lung cancer and separates the lung image into its parts(segmentation)Find important features in the image (features extraction). Choose the most important features to use (features selection). The computer format accepts any clinical image within three choices of MRI, CT and Ultrasound image as input. SVM tool works very well for early detection and treatment of this lung cancer. Keywords: Support Vector Machine, Lung Cancer, Image processing, Early Detection.