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Journal articles on the topic 'K means method and binarized image'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ali, Aziah, Aini Hussain, and Wan Mimi Diyana Wan Zaki. "Segmenting Retinal Blood Vessels with Gabor Filter and Automatic Binarization." International Journal of Engineering & Technology 7, no. 4.11 (2018): 163. http://dx.doi.org/10.14419/ijet.v7i4.11.20794.

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For timely diagnosis of retinal disease, routine retinal monitoring of people with high risk should be put in place. To assist the ophthalmologists in performing retinal analysis efficiently and accurately, numerous studies have been conducted to propose an automated retinal diagnosis system. One of the crucial steps for such a system is accurate detection of retinal blood vessels from retinal image. In this paper, we investigated the use of automatic binarization methods on pre-processed fundus image to detect retinal blood vessels. Three methods for binarization were investigated in this stu

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Chen, Chaoxiang, Shiping Ye, Zhican Bai, Juan Wang, Alexander Nedzved, and Sergey Ablameyko. "Intelligent Mining of Urban Ventilated Corridor Based on Digital Surface Model under the Guidance of K-Means." ISPRS International Journal of Geo-Information 11, no. 4 (2022): 216. http://dx.doi.org/10.3390/ijgi11040216.

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With the acceleration of urbanization, climate problems affecting human health and safe operation of cities have intensified, such as heat island effect, haze, and acid rain. Using high-resolution remote sensing mapping image data to design scientific and efficient algorithms to excavate and plan urban ventilation corridors and improve urban ventilation environment is an effective way to solve these problems. In this paper, we use unmanned aerial vehicle (UAV) tilt photography technology to obtain high-precision remote sensing image digital elevation model (DEM) and digital surface model (DSM)

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Fan, D. L., B. Wang, Z. L. Chen, and L. Wang. "RESEARCH ON BROKEN ROAD CONNECTION METHOD AFTER ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 387–95. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-387-2020.

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Abstract. Aiming at the problem of disconnection after road classification of remote sensing image, this paper proposes an optimization method for broken road connection considering spatial connectivity. The method extracts the road skeleton based on the binarized image after road extraction, and uses the eight neighborhood detection algorithm to find the road breakpoints after road extraction of high-resolution remote sensing image, and removes the isolated points of the road edge according to mathematical morphology filtering. Secondly, use K-means clustering algorithm to search for road bre

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Zhang, Bolun, and Nurul Hanim Romainoor. "Research on Artificial Intelligence in New Year Prints: The Application of the Generated Pop Art Style Images on Cultural and Creative Products." Applied Sciences 13, no. 2 (2023): 1082. http://dx.doi.org/10.3390/app13021082.

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Chinese New Year prints constitute a significant component of the country’s cultural heritage and folk art. Yangliuqing New Year prints are the most important and widely circulated of all the different kinds of New Year prints. Due to a variety of factors including societal change, industrial structure change, and economic development, New Year prints, which were deeply rooted in agricultural society, have been adversely impacted, and have even reached the brink of disappearance. With the protection and effort from the government and researchers, New Year prints can finally be preserved. Howev

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Yang, Jincheng, Shiwen Chen, Jinpeng Dong, and Xiao Han. "Binarization for time-frequency images of LPI radar signals based on K-means." Journal of Physics: Conference Series 2522, no. 1 (2023): 012011. http://dx.doi.org/10.1088/1742-6596/2522/1/012011.

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Abstract Low probability of intercept radar signal is widely used because it is difficult to be intercepted by non-cooperative receivers in electronic warfare. We need to binarize the time-frequency images when analyzing LPI radar signals based on time-frequency distribution. However, the existing binarization algorithms cannot distinguish noise from the signal frequency at low signal-to-noise ratios. In this paper, we propose to use K-means algorithm to binarize the gray time-frequency images of LPI radar signals. We use F1-score to comprehensively consider the effect of binarization. Based o

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Amrouni, Nadia, Amir Benzaoui, Rafik Bouaouina, Yacine Khaldi, Insaf Adjabi, and Ouahiba Bouglimina. "Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis." Sensors 22, no. 24 (2022): 9814. http://dx.doi.org/10.3390/s22249814.

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In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images’ lighting; (2)

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Adjabi, Insaf, Abdeldjalil Ouahabi, Amir Benzaoui, and Sébastien Jacques. "Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition." Sensors 21, no. 3 (2021): 728. http://dx.doi.org/10.3390/s21030728.

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Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF m

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Belhaouari, Samir Brahim, Shahnawaz Ahmed, and Samer Mansour. "Optimized K-Means Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/506480.

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The localization of the region of interest (ROI), which contains the face, is the first step in any automatic recognition system, which is a special case of the face detection. However, face localization from input image is a challenging task due to possible variations in location, scale, pose, occlusion, illumination, facial expressions, and clutter background. In this paper we introduce a new optimized k-means algorithm that finds the optimal centers for each cluster which corresponds to the global minimum of the k-means cluster. This method was tested to locate the faces in the input image

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Alqadi, Prof Ziad, Dr Ghazi M. Qaryouti, and Prof Mohammad Abuzalata. "Enhancing Color Image Clustering using K-Means Method." IJARCCE 9, no. 1 (2020): 78–84. http://dx.doi.org/10.17148/ijarcce.2020.9115.

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Nagato, Keisuke, Hirotaka Oya, Akihisa Tanaka, Gen Inoue, and Takuya Tsujiguchi. "Autonomous Exploration of Catalyst Layer Drying Process of PEMFC." ECS Meeting Abstracts MA2024-02, no. 44 (2024): 2984. https://doi.org/10.1149/ma2024-02442984mtgabs.

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Recent Materials Informatics (MI) has been drastically extended to experiment-based materials exploration methods, especially applied to catalytic materials[1], organic materials[2] and inorganic materials[3]. “Process”, which is located between the materials and actual products, is also important to generate “shape-having” materials. However, the number of candidates in process exploration is much greater than those of material because the process is downstream of the material. Powder-film-formation processes are widely used to produce functional devices such as fuel cells and batteries, and

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OMRAN, M., A. P. ENGELBRECHT, and A. SALMAN. "PARTICLE SWARM OPTIMIZATION METHOD FOR IMAGE CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 297–321. http://dx.doi.org/10.1142/s0218001405004083.

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An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that the PSO image classifier performs better than state-of-the-art image classifiers (namely, K-means, Fuzzy C-means, K-Harmonic means and Genetic Algorithms) in all measured criteria. The influence of

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Chi, Dongxiang. "Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map." ISRN Signal Processing 2011 (June 7, 2011): 1–18. http://dx.doi.org/10.5402/2011/393891.

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Natural image segmentation is an important topic in digital image processing, and it could be solved by clustering methods. We present in this paper an SOM-based k-means method (SOM-K) and a further saliency map-enhanced SOM-K method (SOM-KS). In SOM-K, pixel features of intensity and L∗u∗v∗ color space are trained with SOM and followed by a k-means method to cluster the prototype vectors, which are filtered with hits map. A variant of the proposed method, SOM-KS, adds a modified saliency map to improve the segmentation performance. Both SOM-K and SOM-KS segment the image with the guidance of

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Wang, Fangzheng. "Method of Fruit Image Segmentation by Improved K-Means." Advance Journal of Food Science and Technology 10, no. 11 (2016): 838–40. http://dx.doi.org/10.19026/ajfst.10.2271.

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Ali, Mariam A. "IMAGE GENERATION BY USING K-MEANS CLUSTERING TECHNIQUE." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 03 (2019): 3092–96. https://doi.org/10.5281/zenodo.14823018.

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This paper presents a new approach for image generation by applying k-means algorithm. The K-means clustering algorithm is one of the most widely used algorithm in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means. Our research proposes a color-based image generation method that uses K-means clustering technique which is an iterative technique used to partition an image into k clusters. At first the pixels of source image are clustered into k partitions based on their color, where the clustering process is accomplished. Then the c

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Thylashri, S., Udutha Mahesh Yadav, and T. Danush Chowdary. "Image Segmentation Using K- Means Clustering Method for Brain Tumour Detection." International Journal of Engineering & Technology 7, no. 2.19 (2018): 97. http://dx.doi.org/10.14419/ijet.v7i2.19.15058.

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Brain tumour is an irregular development by cells imitating among them in an unstoppable way. Specific identification of size and area of Brain tumour assumes a fundamental part in the analysis of tumour. Image processing is a dynamic research territory in which processing of image in medical field is an exceedingly difficult field. Segmentation of image assumes a critical part in handling of image as it helps in the finding of suspicious districts from the restorative image. In this paper a proficient algorithm is proposed for detection of tumour based on segmentation of brain by means of clu

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Glowacz, Adam. "Ventilation Diagnosis of Angle Grinder Using Thermal Imaging." Sensors 21, no. 8 (2021): 2853. http://dx.doi.org/10.3390/s21082853.

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The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: health

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Hena, Chauhan, and Pravinchandra Barot Mehul. "Clustering Principles with K-Means." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 405–9. https://doi.org/10.5281/zenodo.7655874.

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K-means clustering is a method of unsupervised learning that is used to partition a dataset into a specific number of clusters (k) to identify patterns and underlying structures within the data. It is particularly useful for identifying patterns and structures in large datasets and is often used as a preprocessing step for other machine learning algorithms. It has been used in a wide variety of fields, including data mining, machine learning, pattern recognition, and image processing. In this paper, we will discuss some of the advantages and disadvantages of using the method

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Long, JianWu, ZeRan Yan, HongFa Chen, and XinLei Song. "Spectrum decomposition in Gaussian scale space for uneven illumination image binarization." PLOS ONE 16, no. 4 (2021): e0251014. http://dx.doi.org/10.1371/journal.pone.0251014.

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Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distin

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Mr., Mohan Raj C. S., and Srikanth V. Dr. "K-Means and Fuzzy C-Means Algorithm for Mammogramy Image Segmentation." Sangrathan Journal, UGC Care Listed Journal 4, no. 1 (2024): 203–15. https://doi.org/10.5281/zenodo.11000974.

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One of the foremost challenges in image analysis is image segmentation. The majority of medical applications often involve trained operators extracting images from targeted regions that may be physically distinct but statistically indistinguishable. Also, Image segmentation is time-consuming and has poor reproducibility often subjected to manual errors and biases. Identification of clusters in given data is another challenge during clustering. K-means is a widely used clustering technique that divides the data into K different clusters. In this strategy, clusters are specified in advance, whic

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Wang, Xiao Bo, and Chuan Sheng Wang. "A Method of Detecting Carton Black in Rubber Based on Optimized Rubber Image." Advanced Materials Research 781-784 (September 2013): 487–90. http://dx.doi.org/10.4028/www.scientific.net/amr.781-784.487.

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Detecting carton black is the basis of evaluating carton black’s dispersion, which is important for researching mixing process. Considering the difference of gray between rubber and carton black, K-Means algorithm was adopted to recognize carton black. With the consideration of some deviations where using K-Means algorithm to recognize carton black with small size, rubber image was optimized on the basis of inflection point. Application of optimizing rubber image and K-Means algorithm improves the accuracy of detecting carton black, which supplies support for evaluating carton black’s dispersi

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Park, Dayoung, and Youngbae Hwang. "Efficient Image Retrieval Using Hierarchical K-Means Clustering." Sensors 24, no. 8 (2024): 2401. http://dx.doi.org/10.3390/s24082401.

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The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set

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Nahari, Rosida Vivin, Achmad Jauhari, Rachmad Hidayat, and Riza Alfita. "Image Segmentation of Cows using Thresholding and K-Means Method." International Journal of Advanced Engineering, Management and Science 3, no. 9 (2017): 913–18. http://dx.doi.org/10.24001/ijaems.3.9.2.

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Kim, Ga-On, Gang-Seong Lee, and Sang-Hun Lee. "An Edge Extraction Method Using K-means Clustering In Image." Journal of Digital Convergence 12, no. 11 (2014): 281–88. http://dx.doi.org/10.14400/jdc.2014.12.11.281.

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Ruban, Igor, Hennadii Khudov, Oleksandr Makoveichuk, et al. "Methods of UAVs images segmentation based on k-means and a genetic algorithm." Eastern-European Journal of Enterprise Technologies 4, no. 9(118) (2022): 30–40. http://dx.doi.org/10.15587/1729-4061.2022.263387.

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The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones

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Mohammad, Elham Jasim. "Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique." Al-Mustansiriyah Journal of Science 29, no. 3 (2019): 150. http://dx.doi.org/10.23851/mjs.v29i3.635.

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Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image.

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Frackiewicz, Mariusz, Aron Mandrella, and Henryk Palus. "Fast Color Quantization by K-Means Clustering Combined with Image Sampling." Symmetry 11, no. 8 (2019): 963. http://dx.doi.org/10.3390/sym11080963.

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Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorith

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Sukafona, I. Made, and Emmy Febriani Thalib. "CONTENT BASED IMAGE RETRIEVAL DENGAN METODE COLOR MOMENT DAN K-MEANS." Jurnal RESISTOR (Rekayasa Sistem Komputer) 1, no. 2 (2018): 73–78. http://dx.doi.org/10.31598/jurnalresistor.v1i2.322.

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Content Based Image Retrieval (CBIR) is a research cluster that is very important to overcome problems related to the image search process. The development of internet technology and data communication has caused the number of multimedia images currently circulating to be very high. This study took the Color Moment method to carry out the feature extraction process. Before the feature extraction process, a segmentation process was carried out to separate the background image and the foreground image. Next, each background and front image is stored in the database. Method performance measuremen

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Dong, Zhenfen, Yuheng Men, Zhengming Li, Zhenzhen Liu, and Jianwei Ji. "Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering." Transactions of the ASABE 64, no. 1 (2021): 13–22. http://dx.doi.org/10.13031/trans.13212.

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HighlightsChlorophyll fluorescence imaging can be used to evaluate chilling injury.Chilling injury area heterogeneity in the L*a*b* color space is significant.Improved k-means++ clustering has a good segmentation effect on chilling injury.Abstract. The application of fluorescence imaging in the detection of tomato chilling injury was investigated. With the segmentation of the chilling injury area serving as the experimental target, an algorithm based on chlorophyll fluorescence image analysis and improved k-means++ clustering was proposed. First, the extraction of lateral heterogeneity values

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Igor, Ruban, Khudov Hennadii, Makoveichuk Oleksandr, et al. "Methods of UAVs images segmentation based on k-means and a genetic algorithm." Eastern-European Journal of Enterprise Technologies 4, no. 9 (118) (2022): 30–40. https://doi.org/10.15587/1729-4061.2022.263387.

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The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones

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Arasy, Muhammad Hariz, Suyanto Suyanto, and Kurniawan Nur Ramadhani. "Aerial Image Segmentation with Clustering Using Fireworks Algorithm." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (2019): 19. http://dx.doi.org/10.21108/indojc.2019.4.1.245.

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Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are co

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Nidhi, Patel* Asst. Prof. Pratik Kumar Soni. "A REVIEW WAVELET TRANSFORM AND FUZZY K-MEANS BASED IMAGE DE-NOISING METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 72–74. https://doi.org/10.5281/zenodo.822974.

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The research area of image processing technique using fuzzy k-means and wavelet transform. The enormous amount of data necessary for images is a main reason for the growth of many areas within the research field of computer imaging such as image processing and compression. In order to get this in requisites of the concerned research work, wavelet transforms and k-means clustering is applied. This can be done in order to discover more possible combinations that may lead to the finest de-noising technique. In this review paper we have tried to review the maximum aspects regarding to image de-noi

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Zhang, Pengpeng, Wei Wu, Yu Li, and Yukun Wang. "Metal Abrasive Image Segmentation Algorithm Based on K-means Clustering." Academic Journal of Science and Technology 7, no. 2 (2023): 227–31. http://dx.doi.org/10.54097/ajst.v7i2.12328.

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Metal abrasive image segmentation is one of the important image processing tasks in the industrial field. However, due to the complex color and texture characteristics of metal abrasive images, as well as difficult factors such as noise and lighting changes, traditional image segmentation methods often fail to achieve high accuracy and stability. In order to solve this problem, a metal abrasive image segmentation algorithm based on K-means clustering is proposed. The algorithm applies the K-means clustering algorithm to the image segmentation of metal abrasive particles, and realizes the separ

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Ma, Guo Qiang, and Xiao Juan Wang. "An Efficient Algorithm Optimization of CT Images Segmentation Based on K-Means Clustering." Applied Mechanics and Materials 530-531 (February 2014): 386–89. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.386.

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Computer tomography image (CT Image) segmentation algorithms have a number of advantages. However, most of these image segmentation algorithms suffer from long computation time because the number of pixels and the encoding parameters is large. We optimized the k-means clustering program with MATLAB language in order to improve the efficiency and stability of k-clustering algorithm in CT image segmentation. One hundred CT images are used to test the proposed method code and compare with the k-means function of the MATLAB R2012a Statistics Toolbox. We analyzed the difference of the two kinds pro

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Dorrer, M. G., and A. E. Alekhina. "Data normalization for training and analysis of the MaskRCNN model using the k-means method for computer vision of smart refrigerator." Informatization and communication 4 (April 2021): 74–80. http://dx.doi.org/10.34219/2078-8320-2021-12-4-74-80.

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This paper proposes using the k-means method for the controlled adjustment of the training sample for semantic image segmentation in the artificial vision of a smart refrigerator. To solve this problem, a new two-stage architecture for computer vision is proposed. In the proposed architecture, various sets of settings for optimizing the contrast of images are used to classify pixels according to their belonging to fragments of the studied image. Extensive experimental evaluation shows that the proposed method has critical advantages over existing work. Firstly, the obtained pixel classes can b

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Krishan, Kewal. "Color Image Segmentation Using Improved Region Growing and K-Means Method." IOSR Journal of Engineering 4, no. 5 (2014): 43–46. http://dx.doi.org/10.9790/3021-04544346.

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Wang, T. N., T. J. Li, G. F. Shao, and S. X. Wu. "An improved K-means clustering method for cDNA microarray image segmentation." Genetics and Molecular Research 14, no. 3 (2015): 7771–81. http://dx.doi.org/10.4238/2015.july.14.3.

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Masa, Amin Padmo Azam, and Hamdani Hamdani. "Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1292. http://dx.doi.org/10.30865/mib.v5i4.3246.

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Batik has several motifs and patterns so it is necessary to identify certain objects in an image, one of which is the recognition of the image of Yogyakarta batik using the Convolutional Neural Network (CNN) method which is already popular in the use of image data classification. The introduction of batik imagery aims to contribute to the digitization of batik image data and at the same time provide information on types of batik to the public. The batik image recognition process using CNN in this study combines the image segmentation process and the enhancement process with median filters and

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Chen, Wei, Cenyu He, Chunlin Ji, Meiying Zhang, and Siyu Chen. "An improved K-means algorithm for underwater image background segmentation." Multimedia Tools and Applications 80, no. 14 (2021): 21059–83. http://dx.doi.org/10.1007/s11042-021-10693-7.

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AbstractConventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed for underwater image background segmentation to address the issue of improper K value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional K-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The K

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Nayak, Nihar Ranjan, Bikram Keshari Mishra, Amiya Kumar Rath, and Sagarika Swain. "Improving the Efficiency of Color Image Segmentation using an Enhanced Clustering Methodology." International Journal of Applied Evolutionary Computation 6, no. 2 (2015): 50–62. http://dx.doi.org/10.4018/ijaec.2015040104.

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The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their

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Ikasari, Ines Hediani, Resti Amalia, and Perani Rosyani. "Segmentasi Citra Bunga Menggunakan Blob Analisis." Building of Informatics, Technology and Science (BITS) 3, no. 3 (2021): 228–34. http://dx.doi.org/10.47065/bits.v3i3.1050.

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Content Based Image Retrieval (CBIR) for image segmentation is a concern this year, especially in the development of computer vision. The object discussed in this study is about interest, which uses a dataset from ImageCLEF2017 by taking 8 flower samples. Image of flowers in the dataset is still a lot of noise such as the initial background behind objects such as leaves, tree trunks or others. So we need a method to eliminate the noise, this method for cleaning noise is done by color clusters using the K-means method. By color clustering using K-Means and using color clusters k=2,3,4,and5. Aft

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Yu, Miao, and Xiaojie Liu. "Computer Image Content Retrieval considering K-Means Clustering Algorithm." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7914842.

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The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, patte

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Liu, Bengang, Wenjiang Wu, Yunpeng Zhang, Zeguang Dong, and Baode Li. "Tool wear image segmentation algorithm based on K-means clustering." Journal of Physics: Conference Series 2787, no. 1 (2024): 012061. http://dx.doi.org/10.1088/1742-6596/2787/1/012061.

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Abstract The existing tool wear region segmentation and detection methods make it difficult to achieve accurate detection of tool wear images under the conditions of noise interference. To further improve the detection efficiency and detection accuracy, combined with the characteristics of tool wear, this paper proposes a tool wear image segmentation algorithm based on K-means clustering to achieve accurate detection of tool wear images. Firstly, the acquired image is pre-processed to reduce the image processing computation and optimize the image, filter out the noise, and reduce the interfere

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Zhao, Linqi, Zhenya Wang, Yaxue Zuo, and Danyang Hu. "Comprehensive Evaluation Method of Ethnic Costume Color Based on K-Means Clustering Method." Symmetry 13, no. 10 (2021): 1822. http://dx.doi.org/10.3390/sym13101822.

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Color is the external manifestation of ethnic minority culture, and the costume of each ethnic group has its objective color matching rules. In the color design of minority costumes, there is often a lack of scientific evaluation methods. Aiming at this problem, this article proposed a comprehensive evaluation method, based on the K-Means clustering method, for evaluating color matching schemes of minority costumes. We used the K-Means clustering method to analyze the objective laws of minority costume colors, and based on the objective laws found, we extracted the objective evaluation indicat

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Yu, Wen Ting, Jing Ling Wang, and Long Ye. "An Improved Normalized Cut Image Segmentation Algorithm with k-Means Cluster." Applied Mechanics and Materials 548-549 (April 2014): 1179–84. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1179.

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Image segmentation with low computational burden has been highly regarded as important goal for researchers. One of the popular image segmentation methods is normalized cut algorithm. But it is unfavorable for high resolution image segmentation because the amount of segmentation computation is very huge [1]. To solve this problem, we propose a novel approach for high resolution image segmentation based on the Normalized Cuts. The proposed method preprocesses an image by using the normalized cut algorithm to form segmented regions, and then use k-Means clustering on the regions. The experimenta

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Xu, Er Jing, Zhen Hong Jia, Lie Jun Wang, Ying Jie Hu, and Jie Yang. "A Novel Level Set Method for Remote Sensing Image." Applied Mechanics and Materials 610 (August 2014): 457–63. http://dx.doi.org/10.4028/www.scientific.net/amm.610.457.

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Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection .Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process .For the reason of picking up the contour’s convergent speed and enable the avoidanc

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Pham, D. T., S. S. Dimov, and C. D. Nguyen. "An Incremental K-means algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 7 (2004): 783–95. http://dx.doi.org/10.1243/0954406041319509.

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Data clustering is an important data exploration technique with many applications in engineering, including parts family formation in group technology and segmentation in image processing. One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency. The main problem with this clustering method is its tendency to coverge at a local minimum. In this paper, the cause of this problem is explained and an existing solution involving a cluster centre jumping operation is examined. The jumping technique alleviates the problem with local

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Zhu, Ling Li, and Lan Wang. "Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation." Advanced Materials Research 1022 (August 2014): 269–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1022.269.

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Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population f

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Zhong, Haidong, Xianyi Chen, and Qinglong Tian. "An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching." Information 10, no. 1 (2019): 17. http://dx.doi.org/10.3390/info10010017.

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Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method f

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Benrais, Lamine, and Nadia Baha. "Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram." International Journal of Information Systems in the Service Sector 8, no. 2 (2016): 57–69. http://dx.doi.org/10.4018/ijisss.2016040105.

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The K-means is a popular clustering algorithm known for its simplicity and efficiency. However the elapsed computation time is one of its main weaknesses. In this paper, the authors use the K-means algorithm to segment grayscale images. Their aim is to reduce the computation time elapsed in the K-means algorithm by using a grayscale histogram without loss of accuracy in calculating the clusters centers. The main idea consists of calculating the histogram of the original image, applying the K-means on the histogram until the equilibrium state is reached, and computing the clusters centers then

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Waasilah, Hadiyyatan, Riries Rulaningtyas, Winarno Winarno, and Anny Setijo Rahaju. "Tubule Formation Segmentation Of Histopathological Image Of Breast Cancer By Using Clustering Method." Indonesian Applied Physics Letters 1, no. 1 (2020): 29. http://dx.doi.org/10.20473/iapl.v1i1.21338.

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Histopathological assessment is one of the examinations that allows the classification of breast cancer based on its level. Histopathological assessment factors are based on tubule formation, nuclear pleomorphism, and the mitotic count. This study only focused on tubule formation. The tubule formation was represented by a lumen surrounded a nucleus. The segmentation of tubule histopathology of breast cancer method was using a combination of k-means clustering and graph cut. The image data used in this study were 15 images of breast cancer histopathology preparations using 5 variations in the n

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