Relevant bibliographies by topics / K means method and binarized image

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'K means method and binarized image'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "K means method and binarized image"

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Wen-Feng, Wu, and 吳文鳳. "A Study of Data Hiding Method in Color Image using Grouping Palette Index by Particle Swarm Optimization with K-means Clustering." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/09396474013896816616.

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碩士<br>玄奘大學<br>資訊管理學系碩士班<br>99<br>We propose a data hiding method in color image with its image palette. Many authors usually embed data into the palette or into the index table of the palette directly. Those data hiding methods embedded the secret data into palette itself, the palette will be changed to a different one. It becomes more difficultly to reveal the embedded information. We apply the particle swarm optimization method with K-means clustering to divide the color image palette into several groups. The largest numbers of pixels of a palette group has, the more data may be embedded in
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Book chapters on the topic "K means method and binarized image"

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Van, Thanh The, Nguyen Van Thinh, and Thanh Manh Le. "The Method Proposal of Image Retrieval Based on K-Means Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_45.

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Jin, Ran, Chunhai Kou, Ruijuan Liu, and Yefeng Li. "A Color Image Segmentation Method Based on Improved K-Means Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4850-0_63.

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Liu, Yijiangze, Jishen Peng, Xinping Song, and Boyu Cheng. "Insulator Image Segmentation Method Based on IHPO-GH Optimized K-means Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1852-1_57.

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Dinh, Nguyen Thi, Thanh Manh Le, and Thanh The Van. "An Improvement Method of Kd-Tree Using k-Means and k-NN for Semantic-Based Image Retrieval System." In Information Systems and Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04819-7_19.

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Zhao, Xiangwei, Xin Li, Hao Chang, Jun Yao, and Beibei Weng. "Extraction Method of Power’s Corridor Centre Based on Grid Inspection Image and K-Means Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9423-3_45.

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Mishra, Bikram Keshari, and Amiya Kumar Rath. "An Enhanced Clustering Method for Image Segmentation." In Exploring Critical Approaches of Evolutionary Computation. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5832-3.ch016.

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The findings of image segmentation reflect 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 chapter, the authors have evaluated the performances of three different clustering algorithms used in image segmentation: the classical k-means, its modified k-means++, and proposed en
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Shodhan, Abhishek, and R Manimozhi. "DETECTING BRAIN TUMORS USING DIGITAL IMAGE PROCESSING." In INFORMATION TECHNOLOGY & BIOINFORMATICS: INTERNATIONAL CONFERENCE ON ADVANCE IT, ENGINEERING AND MANAGEMENT - SACAIM-2022 (VOL 1). REDSHINE India, 2020. http://dx.doi.org/10.25215/8119070682.15.

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Processing of MRI is one of the components of image processing in the medical profession. Finding the tumor is frequently the first stage. CNN, ANN, SVM classifiers, Edge Detection (Using Sobel operator), Filtering, Thresholding, shrinking operation on image, Watershed method, bilateral segmentation, Active contour method, discrete wavelet transformation, Clustering, Fuzzy C-Means, K-Means and The Naive Bayes classifier are some of the algorithms used to detect brain tumor.
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Ramadevi, Potharla, Raja Das, Lakshmi M., Balakrishnama Manohar, and Smita Sharma. "An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification." In Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis. Elsevier, 2025. https://doi.org/10.1016/b978-0-443-26765-9.00006-8.

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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." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch075.

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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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De, Sourav, and Firoj Haque. "Multilevel Image Segmentation Using Modified Particle Swarm Optimization." In Intelligent Analysis of Multimedia Information. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0498-6.ch004.

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Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned
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Conference papers on the topic "K means method and binarized image"

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Zhao, Xia, Linfang Zhang, Yaohui Ling, Zhiran Zhang, Yizhuo He, and Fuyan Wang. "Research on Image Classification Enhancement Method Based on K-means++ Clustering and CNN." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10828045.

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Deng, GuanNan, Lu Mu, Zhe Ma, and Mei Zhang. "Image retrieval method based on k-means block." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), edited by Paulo Batista and Ram Bilas Pachori. SPIE, 2023. http://dx.doi.org/10.1117/12.2680938.

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Kich, Ismail, El Bachir Ameur, and Abdelghani Souhar. "New Image Steganography Method Based on K-means Clustering." In BDCA'17: 2nd international Conference on Big Data, Cloud and Applications. ACM, 2017. http://dx.doi.org/10.1145/3090354.3090432.

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Pillai, Bhagya, Mundra Mounika, Pooja J. Rao, and Padmamala Sriram. "Image steganography method using K-means clustering and encryption techniques." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732209.

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Yang, Yuan-feng, Jian Wu, Jing Fang, and Zhi-ming Cui. "Parallel Hierarchical K-means Clustering-Based Image Index Construction Method." In 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science. IEEE, 2012. http://dx.doi.org/10.1109/dcabes.2012.35.

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Premana, Agyztia, Akhmad Pandhu Wijaya, and Moch Arief Soeleman. "Image segmentation using Gabor filter and K-means clustering method." In 2017 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2017. http://dx.doi.org/10.1109/isemantic.2017.8251850.

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Dallali, Adel, Salim El Khediri, Amel Slimen, and Abdennaceur Kachouri. "Breast tumors segmentation using Otsu method and K-means." In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2018. http://dx.doi.org/10.1109/atsip.2018.8364469.

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Song, Yan, Anan Liu, Lin Pang, Shouxun Lin, Yongdong Zhang, and Sheng Tang. "A Novel Image Text Extraction Method Based on K-Means Clustering." In Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). IEEE, 2008. http://dx.doi.org/10.1109/icis.2008.31.

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Zheng, Jian, Zhanzhong Cui, Anfei Liu, and Yu Jia. "A K-Means Remote Sensing Image Classification Method Based On AdaBoost." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.903.

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Song, Xuanzhang, Jianqiang Liu, and Qiongyan Li. "Soil Erosion Image Segmentation Based on Improved K-means clustering method." In 2016 5th International Conference on Sustainable Energy and Environment Engineering (ICSEEE 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/icseee-16.2016.163.

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