Relevant bibliographies by topics / Multilevel Thresholding

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

Academic literature on the topic 'Multilevel Thresholding'

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

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Journal articles on the topic "Multilevel Thresholding"

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation." International Journal of Applied Metaheuristic Computing 8, no. 4 (2017): 58–83. http://dx.doi.org/10.4018/ijamc.2017100104.

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Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is
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Wang, Yi, and Kangshun Li. "A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–20. http://dx.doi.org/10.4018/ijcini.20211001.oa44.

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Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Ka
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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 91–106. http://dx.doi.org/10.4018/ijamc.2019070105.

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Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quali
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Yin, Peng-Yeng, and Ling-Hwei Chen. "Random-sampling thresholding: A new approach to multilevel thresholding." Signal Processing 34, no. 3 (1993): 311–22. http://dx.doi.org/10.1016/0165-1684(93)90138-z.

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Dash, Prasannajit, and Maya Nayak. "Multilevel Thresholding using PSO Clustering." International Journal of Computer Applications 97, no. 18 (2014): 27–32. http://dx.doi.org/10.5120/17107-7752.

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Cao, L., Z. K. Shi, and E. K. W. Cheng. "Fast automatic multilevel thresholding method." Electronics Letters 38, no. 16 (2002): 868. http://dx.doi.org/10.1049/el:20020594.

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Hertz, Lois, and Ronald W. Schafer. "Multilevel thresholding using edge matching." Computer Vision, Graphics, and Image Processing 44, no. 3 (1988): 279–95. http://dx.doi.org/10.1016/0734-189x(88)90125-9.

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Abualigah, Laith, Ali Diabat, Putra Sumari, and Amir H. Gandomi. "A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images." Processes 9, no. 7 (2021): 1155. http://dx.doi.org/10.3390/pr9071155.

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One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresh
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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation." International Journal of Swarm Intelligence Research 11, no. 4 (2020): 123–39. http://dx.doi.org/10.4018/ijsir.2020100106.

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Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the propos
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S, Anbazhagan. "APPLICATION OF TEACHING LEARNING BASED OPTIMIZATION IN MULTILEVEL IMAGE THRESHOLDING." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2413–22. http://dx.doi.org/10.21917/ijivp.2021.0344.

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This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific,
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Dissertations / Theses on the topic "Multilevel Thresholding"

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Huang, Jian-Min, and 黃健旻. "Multilevel Optimal Thresholding Method for Image Segmentation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/69114026014074061950.

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碩士<br>國立中興大學<br>應用數學系所<br>101<br>In this paper, we propose a new evolutionary algorithm which is combined with lookup table method (LUT). This algorithm can be applied in the selection for optimal multilevel thresholds of image segmentation. The objection function adopted in the algorithm is the same as Otsu’s method (between-class variance of image histogram). In our algorithm, the optimal multilevel thresholds are determined by maximizing the between-class variance. In order to increase computing speed, our algorithm is implemented in the MATLAB by using matrix operation and is combined with
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Hu, Shu-Yu, and 胡書瑜. "Contour-guided Image Multilevel Thresholding by Frequency Filtering." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/05526207205735646628.

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碩士<br>國立屏東大學<br>資訊工程學系碩士班<br>104<br>This thesis proposes the method of adjusting the light distribution in the given grayscale image to improve the effect of multi-level thresholding methods. The proposed method is based on the use of Ultrametric Contour Map (UCM) of the original image to perform two steps of image processing. First, based on the UCM of the given image, the proposed method generate appropriate noise to the background and the contour of the given image to reduce the influence of background information and highlight the object contours. Next, based on the UCM of the given image,
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Kuo, Chin-ming, and 郭晉銘. "Two-Dimensional Multilevel Thresholding Based on Decision Tree Construction." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/51450769861047107372.

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碩士<br>南華大學<br>資訊管理學研究所<br>91<br>In this thesis, we propose a two-dimensional multilevel thresholding based on decision tree construction for image segmentation. In this method, we use unsupervised cluster tree method to discriminate the different objects of the two-dimensional gray level histogram. Our method consists of two steps: cluster tree construction and cluster tree pruning. In the cluster tree construction step, we use the gain criterion to select the appropriate cut in the two-dimensional gray-level histogram until every node’s number of data points or relative density conform to u
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Lin, Yi-Guei, and 林義貴. "Multilevel Image Thresholding and Handwritten Chinese Character Recognition by Support Vector Machines." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/35422744004406021613.

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碩士<br>國立屏東商業技術學院<br>資訊工程系(所)<br>99<br>Recently, support vector machines (SVM) has been adopted in various categorization applications, such as handwritten character recognition, face recognition, and text categorization. The experimental results of these researches confirmed the effectiveness of SVM. The mathematical theory used in SVM is related to the subfield of nonlinear programming, quadratic programming, which has been developed by many researches in the machine learning area to forther improve the performance of SVM. The research first proposed a SVM-based bi-level image threshoding me
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Chen, SONG-XIN, and 陳松鑫. "A Watermarking Scheme for Tamper Detection and Recovery of Color Images by Using Arnold's Cat Map and Multilevel Thresholding." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/74185423426858065056.

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碩士<br>國立高雄第一科技大學<br>電腦與通訊工程系碩士班<br>105<br>With the progress and development of the Internet, many people share their own pictures or daily life photos on the Internet. It is not only easy to download with the browser, but also easy to modify or edit through the variety of software, and then re-share to the Internet. In order to detect the truthfulness of the image and to avoid the image being tampered, we propose a novel method to embed the recovery message and authentication data in color images. When an image is tampered with or destroyed, this method can extract the authentication data for
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Book chapters on the topic "Multilevel Thresholding"

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Jena, Bibekananda, Manoj Kumar Naik, Aneesh Wunnava, and Rutuparna Panda. "Multilevel Thresholding Using Black Widow Optimization." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0695-3_55.

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Ali, Layak. "Multilevel Thresholding in Image Segmentation Using Swarm Algorithms." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13731-5_23.

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Rueda, Luis, and Iman Rezaeian. "Applications of Multilevel Thresholding Algorithms to Transcriptomics Data." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_3.

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Oliva, Diego, Noe Ortega-Sánchez, Salvador Hinojosa, and Marco Pérez-Cisneros. "Multilevel Thresholding by Using Manta Ray Foraging Optimization." In Modern Metaheuristics in Image Processing. CRC Press, 2022. http://dx.doi.org/10.1201/9781003183501-4.

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Ramos-Frutos, Jorge, Israel Miguel-Andrés, Teresa Alonso-Rasgado, Noé Ortega-Sánchez, Gonzalo Pájares, and Juan Carlos Barragán-Barajas. "Multilevel Thresholding Color Image Segmentation Solved with Metaheuristics." In Studies in Computational Intelligence. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78440-8_1.

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Nyathi, Thambo. "Automated Design of Multilevel Thresholding Using Differential Evolution." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84356-3_6.

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Liu, Suping, and Yi Wang. "Multilevel Image Thresholding Using Bat Algorithm Based on Otsu." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5577-0_32.

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Bruzzese, D., and U. Giani. "Automatic Multilevel Thresholding Based on a Fuzzy Entropy Measure." In Classification and Multivariate Analysis for Complex Data Structures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13312-1_12.

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Ahmed, Saifuddin, and Anupam Biswas. "A Survey on Multilevel Thresholding-Based Image Segmentation Techniques." In Futuristic Trends in Networks and Computing Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5037-7_59.

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Siva Sathya, S., and Jonti Deuri. "Multilevel Thresholding for Image Segmentation Using Cricket Chirping Algorithm." In Bio-Inspired Computing for Image and Video Processing. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315153797-2.

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Conference papers on the topic "Multilevel Thresholding"

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Sushir, R. D., and Sarita S. Bhutada. "Fhelo-MTH: Floret Heliotropic Optimized Multilevel Thresholding-based Image Segmentation." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760673.

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Ramos-Frutos, Jorge, Oscar Ramos-Soto, Angel Casas-Ordaz, et al. "A multi-objective decomposition estimation of distribution algorithm for multilevel thresholding image segmentation." In 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion). IEEE, 2025. https://doi.org/10.1109/cismcompanion65074.2025.11032660.

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Zhang, Yunfei, Zhan Zhang, Ri Xu, and Ping Xiong. "Multilevel Thresholding for Image Partitioning Using Median Grayscale-Based 2D Entropy in 3X3 Neighborhoods." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085875.

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Tuba, Milan, Nebojsa Bacanin, and Adis Alihodzic. "Multilevel image thresholding by fireworks algorithm." In 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA. IEEE, 2015. http://dx.doi.org/10.1109/radioelek.2015.7129057.

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Ma, Junyong, Desheng Wen, Shaodong Yang, Liang Wang, and Jianming Zhan. "Hierarchical segmentation based on a multilevel thresholding." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648275.

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Banimelhem, Omar, Moad Mowafi, and Oduy Alzoubi. "Multilevel thresholding image segmentation using memetic algorithm." In 2015 6th International Conference on Information and Communication Systems (ICICS). IEEE, 2015. http://dx.doi.org/10.1109/iacs.2015.7103213.

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Cao, Yongfeng, Hong Sun, and Xin Xu. "A novel and efficient multilevel thresholding method." In Third International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Hanqing Lu and Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.538942.

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Virmajoki, Olli, and Pasi Franti. "Multilevel thresholding by fast PNN-based algorithm." In Electronic Imaging 2003, edited by Edward R. Dougherty, Jaakko T. Astola, and Karen O. Egiazarian. SPIE, 2003. http://dx.doi.org/10.1117/12.477741.

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He, Yaqiong, Guangbin Zhang, and Xiaofeng Zhang. "Multilevel Thresholding Based on Fuzzy Masi Entropy." In 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). IEEE, 2021. http://dx.doi.org/10.1109/icpeca51329.2021.9362561.

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Adollah, R., E. U. Francis, M. Y. Mashor, and N. H. Harun. "Bone marrow image segmentation based on multilevel thresholding." In 2012 International Conference on Biomedical Engineering (ICoBE). IEEE, 2012. http://dx.doi.org/10.1109/icobe.2012.6179058.

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