Relevant bibliographies by topics / Otsu's algorithm

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

Academic literature on the topic 'Otsu's algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Otsu's algorithm"

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Li, Shenghan, and Linlin Ye. "Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer." Mathematical Biosciences and Engineering 20, no. 6 (2023): 9645–69. http://dx.doi.org/10.3934/mbe.2023423.

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<abstract><p>The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and
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Wang, Yanmei, Mingyu Lu, and Pengfei Feng. "Research on Road Extraction Algorithm Based on Improved Otsu’s Thresholding Method." Journal of Physics: Conference Series 2364, no. 1 (2022): 012064. http://dx.doi.org/10.1088/1742-6596/2364/1/012064.

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Abstract In this paper, the widely used method of Otsu's thresholding method is studied. On the basis of the traditional Otsu's thresholding method, according to the distribution characteristics of the target and background in the actual image, a new method based on traditional Otsu's thresholding method is presented.and the road extraction experiment is carried out with GF-2 remote sensing image.The experimental results show that the improved method has the advantages of high segmentation accuracy and strong anti noise ability compared with the traditional Otsu's thresholding method.
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Lee, Youngwoo, and Jin Heon Kim. "A Computational Improvement of Otsu's Algorithm by Estimating Approximate Threshold." Journal of Korea Multimedia Society 20, no. 2 (2017): 163–69. http://dx.doi.org/10.9717/kmms.2017.20.2.163.

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Soundarya, C., A. Kalaiselvi, and J. Surya. "Brain Tumor Detection Using Transfer Learning." Journal of Signal Processing 9, no. 1 (2023): 33–42. http://dx.doi.org/10.46610/josp.2023.v09i01.004.

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The main objective of the proposed work is to encounter the most serious condition of brain tumors. However, if caught early enough, a brain tumor can be cured. MRI scans and CT scans are used to diagnose brain tumors in most cases. It is far too difficult to accurately detect a tumor’s location and size. It is often difficult for doctors and patients to comprehend the outcomes. This paper targets to frame automated segmentation and classification of brain tumors. In this work, around 3000 MRI images (both tumors and non-tumors) are collected. To identify the images with tumors, Otsu's segment
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Chakraborty, Falguni, Provas Kumar Roy, and Debashis Nandi. "Symbiotic Organisms Search Optimization for Multilevel Image Thresholding." International Journal of Swarm Intelligence Research 11, no. 2 (2020): 31–61. http://dx.doi.org/10.4018/ijsir.2020040103.

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Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relatio
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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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Sheng, Dong-Bo, Sang-Bong Kim, Trong-Hai Nguyen, Dae-Hwan Kim, Tian-Shui Gao, and Hak-Kyeong Kim. "Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm." Journal of the Korea Society For Power System Engineering 20, no. 4 (2016): 32–37. http://dx.doi.org/10.9726/kspse.2016.20.4.032.

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Miledi, Mariem, and Souhail Dhouib. "VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation." International Journal of Applied Metaheuristic Computing 12, no. 1 (2021): 94–110. http://dx.doi.org/10.4018/ijamc.2021010106.

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Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functio
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Brahmaiah, Naik J., B. Rajasree, Panchakshari P. Durga, V. Sai, G. Sunny, and J. Raghavendra. "Multilevel thresholding image segmentation using mixed strategy improved convergence based whale optimization algorithm." i-manager’s Journal on Electronics Engineering 15, no. 2 (2025): 1. https://doi.org/10.26634/jele.15.2.21575.

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This paper presents a novel multilevel image segmentation method that leverages an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in solving various optimization problems, its performance can be limited by susceptibility to local optima. To address this challenge, a Mixed-Strategy Improved Convergence WOA (MSICWOA) is proposed, which enhances the algorithm's optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization technique. The MSICWOA is then applied alongside Otsu's crossvariance and Kapur
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Suma K. V. and Bheemsain Rao. "Detection of Rarefaction of Capillaries and Avascular Region in Nailfold Capillary Images." International Journal of Biomedical and Clinical Engineering 5, no. 2 (2016): 73–86. http://dx.doi.org/10.4018/ijbce.2016070106.

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Reduction in the capillary density in the nailfold region is frequently observed in patients suffering from Hypertension (Feng J, 2010). Loss of capillaries results in avascular regions which have been well characterized in many diseases (Mariusz, 2009). Nailfold capillary images need to be pre-processed so that noise can be removed, background can be separated and the useful parameters may be computed using image processing algorithms. Smoothing filters such as Gaussian, Median and Adaptive Median filters are compared using Mean Squared Error and Peak Signal-to-Noise Ratio. Otsu's thresholdin
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Dissertations / Theses on the topic "Otsu's algorithm"

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MITTAL, SHWETA. "OTSU’S MULTI LEVEL THRESHOLDING USING MODIFIED FIREFLY ALGORITHM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15038.

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Segmentation plays a crucial role in most tasks requiring image analysis. Basically segmentation is the process of partitioning an image into multiple segments. Segmentation changes the representation of image into a more meaningful and easier to analyze one. Pixels with the same characteristics are grouped together. Image segmentation can be done using several techniques. Here we are using the threshold selection method. Threshold selection is a significant technique for image segmentation and is broadly applied in many fields like computer vision, character recognition, analysis of me
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Chang, Juei-Feng, and 張瑞芳. "Image Threshold selection based on PSO and 2D-Otsu Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/90250525774143478346.

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碩士<br>國立中央大學<br>通訊工程學系在職專班<br>101<br>Threshold selecting is a significant technique for image segmentation, which is applied broadly in many fields such as character recognition, analysis of biologic images etc. The method mainly includes P-tile method , the maximum entropy method , Otsu and so on. It is essentially a pixels classification problem. Its basic objective is to classify the pixels of a given image into two classes: one is those pertaining to an object and another is those pertaining to the background. While one includes pixels with gray values that are below or equal to a certain
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Chen, Gu-Yang, and 陳谷暘. "A Comparison of Artificial Bee Colony Algorithm and Shuffled Frog Leaping Algorithm for the Numerical Optimization and 3-D Otsu Threshold Selection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/03597785312561050125.

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碩士<br>國立屏東大學<br>資訊科學系碩士班<br>103<br>In the first part of this work, two nature inspired algorithms are used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by Shuffled Frog Leaping Algorithm (SFLA). In the second part of this work, two threshold selection methods are proposed to speed up the original 3-D Otsu thresholding for image segmentation based on SFLA and ABCA, respectively. In order to compare the performance of the proposed ABC algorithm with SFLA, five classical benchmark functions are used for te
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Chen, Ji-ding, and 陳紀鼎. "Design and implementation of Real-Time Object Tracking System Using Gaussian Motion Model and Otsu Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/47763137322935420753.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>97<br>Recently, the technology of image process has been widely applied into various fields such as video-conferencing, computer vision and face tracking due to the rapid growth of computer science. However, the developments of tracking systems are locked because most studies have used the still camera and the pure background. Therefore, a wireless real-time tracking system based on background subtraction, gauss motion model and Otsu algorithm is proposed in this study to identify the moving object and then pursue it. This system consists of (a) the tracking platfo
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Book chapters on the topic "Otsu's algorithm"

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Oliva, Diego, Mohamed Abd Elaziz, and Salvador Hinojosa. "Otsu’s Between Class Variance and the Tree Seed Algorithm." In Metaheuristic Algorithms for Image Segmentation: Theory and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12931-6_7.

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Cao, Lianlian, Sheng Ding, Xiaowei Fu, and Li Chen. "Application and Comparison of Three Intelligent Algorithms in 2D Otsu Segmentation Algorithm." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_26.

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Brüning, Markus, Paul Wunderlich, and Helene Dörksen. "Advanced Feature Extraction Workflow for Few Shot Object Recognition." In Bildverarbeitung in der Automation. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_4.

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AbstractObject recognition is well known to have a high importance in various fields. Example applications are anomaly detection and object sorting. Common methods for object recognition in images divide into neural and non-neural approaches: Neural-based concepts, e.g. using deep learning techniques, require a lot of training data and involve a resource intensive learning process. Additionally, when working with a small number of images, the development effort increases. Common non-neural feature detection approaches, such as SIFT, SURF or AKAZE, do not require these steps for preparation. Th
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Jyotika Pruthi and Gaurav Gupta. "Image Segmentation Using Genetic Algorithm and OTSU." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0451-3_43.

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Gao, Kanglin, Mei Dong, Liqin Zhu, and Mingjun Gao. "Image Segmentation Method Based Upon Otsu ACO Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19853-3_85.

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Yi-bing, Li, Meng Ting, and Li Ao. "Two-Dimensional Otsu Image Segmentation Algorithm Based on the Particle Swarm Optimization Algorithm." In Future Control and Automation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31006-5_14.

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Suresh, K., and U. Sakthi. "Robust Multi-thresholding in Noisy Grayscale Images Using Otsu’s Function and Harmony Search Optimization Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4765-7_52.

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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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Sucharitha, M., Sunitha Tappari, B. Jyothi, and Arunkumar Madupu. "Lung Cancer Detection Using Hybrid Methods of Otsu-Based PSO Algorithm Combined with ACO Algorithm." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8628-6_49.

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Gao, Jian, Zhiliang Wang, Yanyun Liu, Chuanxia Jian, and Xin Chen. "Development of OLED Panel Defect Detection System through Improved Otsu Algorithm." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_13.

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Conference papers on the topic "Otsu's algorithm"

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Chen, Yong, Caixia Wang, and Haibing Fang. "Feature Extraction Algorithm Based on Otsu's Method and Hybrid Hierarchical Grid-Quadtree Partitioning." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11084144.

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Deng, Xiaolian, Jiao Deng, and Zengyu Deng. "Adaptive Corner Detection Algorithm Based on OTSU Threshold Segmentation." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719234.

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Wang, Jingjing, Shiming Lin, and Kang Zhang. "An Edge Detection Algorithm of Noisy Image Based on OTSU Adaptive Threshold Segmentation." In 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2024. http://dx.doi.org/10.1109/ipec61310.2024.00099.

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ghaffari, Babak Vazifehkhah, Marzuki Bin Khalid, and Mojgan Kouhnavard. "New Algorithm for License plate Localization with Otsu's Method." In 3rd Annual International Conference on Advances in Distributed and Parallel Computing. Global Science Technology Forum, 2012. http://dx.doi.org/10.5176/2251-1652_adpc12.10.

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Messina, M., M. Greco, L. Fabbrini, and G. Pinelli. "Modified Otsu's algorithm: A new computationally efficient ship detection algorithm for SAR images." In 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS 2012). IEEE, 2012. http://dx.doi.org/10.1109/tywrrs.2012.6381140.

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Wang, Qingping, Hongyu Zhao, Weiwei Wu, and Naichang Yuan. "Algorithm for segmentation based on an improved three-dimensional Otsu's thresholding." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6526256.

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Wang, Xuanjing, and Yonglin Xue. "Fast HEVC intra coding algorithm based on Otsu's method and gradient." In 2016 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2016. http://dx.doi.org/10.1109/bmsb.2016.7521964.

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Pandey, J. G., A. Karmakar, C. Shekhar, and S. Gurunarayanan. "A Novel Architecture for FPGA Implementation of Otsu's Global Automatic Image Thresholding Algorithm." In 2014 27th International Conference on VLSI Design. IEEE, 2014. http://dx.doi.org/10.1109/vlsid.2014.58.

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Singh, Rahul, Prateek Agarwal, Manish Kashyap, and Mahua Bhattacharya. "Kapur's and Otsu's based optimal multilevel image thresholding using social spider and firefly algorithm." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754088.

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Shah-Hosseini, Hamed. "Otsu's criterion-based multilevel thresholding by a nature-inspired metaheuristic called Galaxy-based Search Algorithm." In 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2011. http://dx.doi.org/10.1109/nabic.2011.6089621.

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