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Journal articles on the topic 'Global image thresholding'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

KHASHMAN, ADNAN, and BORAN SEKEROGLU. "DOCUMENT IMAGE BINARISATION USING A SUPERVISED NEURAL NETWORK." International Journal of Neural Systems 18, no. 05 (2008): 405–18. http://dx.doi.org/10.1142/s0129065708001671.

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Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel m

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Aboura, Khalid. "Pseudo Bayesian and Linear Regression Global Thresholding." International Journal of Electronics and Telecommunications 56, no. 1 (2010): 63–72. http://dx.doi.org/10.2478/v10177-010-0008-1.

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Pseudo Bayesian and Linear Regression Global ThresholdingClassification is an important task in image analysis. Simply recognizing an object in an image can be a daunting step for a computer algorithm. The methodologies are often simple but rely heavily on the thresholding of the image. The operation of turning a color or gray-scale image into a black and white image is a determining step in the effectiveness of a solution. Thresholding methods perform differently in various problems where they are often used locally. Global thresholding is a difficult task in most problems. We highlight a pse

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Senthilkumaran, N1 and Vaithegi S2. "TOP 1 CITED PAPER - COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ)." COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL (CSEIJ) 6, no. 1 (2019): 3. https://doi.org/10.5281/zenodo.3386005.

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Image binarization is the process of separation of pixel values into two groups, black as background and white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This paper describes a locally adaptive thresholding technique that removes background by using local mean and standard deviation. Most common and simplest approach to segment an image is using thresholding. In this work we present an efficient implementation for threshoding and give a detailed comparison of Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding

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Gill, Tarnjot Kaur, and Aman Arora. "Evaluating the performance of different image binarization techniques." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 11 (2014): 1294–99. https://doi.org/10.5281/zenodo.14768316.

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Image binarization is the methodology of separating of pixel values into dual collections, dark as frontal area and white as background. Thresholding has discovered to be a well-known procedure utilized for binarization of document images. Thresholding is further divided into global and local thresholding technique. In document with contrast delivery of background and foreground, global thresholding is discovered to be best technique. In corrupted documents, where extensive background noise or difference in contrast and brightness exists i.e. there exists numerous pixels that cannot be effortl

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Reining, Lars C., and Thomas S. A. Wallis. "A psychophysical evaluation of techniques for Mooney image generation." PeerJ 12 (September 27, 2024): e18059. http://dx.doi.org/10.7717/peerj.18059.

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Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually, using subjective criteria for generation. This manual process could eventually be avoided by using automatic generation techniques. The field of computer image processing offers numerous techniques for image thresholding, but these are only rarely used to create Mooney images. Fu

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T. A. A., Enow, Ngalle H. B., and Ngonkeu M. E. L. "Automated Estimation of Plant Leaf Disease Severity Using Classical Image Segmentation Techniques." Biotechnology Journal International 29, no. 2 (2025): 59–76. https://doi.org/10.9734/bji/2025/v29i2772.

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Aim: This study aimed to propose a computationally cost-effective method for automated estimation of plant leaf disease severity in resource-limited settings. Study Design: The performance of four image segmentation algorithms—global thresholding, adaptive thresholding, Otsu thresholding, and edge detection—was evaluated using nine curated images of disease-affected leaves from tomato, bell pepper, and potato plants. Each image was segmented into healthy and diseased regions, and quantitative metrics—including diseased pixel counts, percentage of affected area, healthy-to-diseased ratios, and

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Mohamad Roslan, Muhammad Ammar, Aimi Salihah Abdul Nasir, Marni Azira Markom, Allan Melvin Andrew, and Edy Victor Haryanto. "COVID-19 Chest X-Ray Lung Segmentation by Locally Adaptive Thresholding." Journal of Advanced Research in Applied Sciences and Engineering Technology 64, no. 3 (2025): 69–83. https://doi.org/10.37934/araset.64.3.6983.

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The novel coronavirus disease 2019 (COVID-19), first identified in December 2019 in Wuhan, China, rapidly escalated into a global pandemic. Effective and reliable solutions for automated detection and large-scale screening are crucial to monitor and control the spread of COVID-19. However, distinguishing between COVID-19 and pneumonia in chest X-ray (CXR) scans remains a challenge for radiologists due to overlapping image features. Additionally, modern diagnostic methods such as reverse transcription polymerase chain reaction (RT-PCR) are expensive, complex, and time-consuming. This study aims

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Houssein, Essam H., Gaber M. Mohamed, Nagwan Abdel Samee, Reem Alkanhel, Ibrahim A. Ibrahim, and Yaser M. Wazery. "An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation." Diagnostics 13, no. 8 (2023): 1422. http://dx.doi.org/10.3390/diagnostics13081422.

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Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level th

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Tarbă, Nicolae, Costin-Anton Boiangiu, and Mihai-Lucian Voncilă. "From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning." Applied Sciences 15, no. 14 (2025): 8096. https://doi.org/10.3390/app15148096.

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Image binarization is an important process in many computer-vision applications. This transforms the color space of the original image into black and white. Global thresholding is a quick and reliable way to achieve binarization, but it is inherently limited by image noise and uneven lighting. This paper introduces a global thresholding method that uses the results of classical global thresholding algorithms and other global image features to train a regression model via machine learning. We prove through nested cross-validation that the model can predict the best possible global threshold wit

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Abdul Halim, Nor Aqlina, and Aqilah Baseri Huddin. "Segmentation Methods for MRI Human Spine Images using Thresholding Approaches." Jurnal Kejuruteraan 34, no. 4 (2022): 591–97. http://dx.doi.org/10.17576/jkukm-2022-34(4)-07.

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Computer-Aided Diagnosis (CAD) in MRI image processing can assist experts in detecting abnormality in human spine image efficiently. The manual process of detecting abnormality is tedious, hence the use of CAD in this field is helpful to increase the diagnosis efficiency. The segmentation method is an important and critical process in CAD that could affect the accuracy of the MRI spine image’s overall diagnosis. There are various segmentation methods commonly used in CAD. One of the methods is segmentation using thresholding. Thresholding approaches divide the area of interest by identifying t

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Dhatchinamoorthy, Vinoth, and Ezhilmaran Devarasan. "An Analysis of Global and Adaptive Thresholding for Biometric Images Based on Neutrosophic Overset and Underset Approaches." Symmetry 15, no. 5 (2023): 1102. http://dx.doi.org/10.3390/sym15051102.

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The study introduces a new threshold method based on a neutrosophic set. The proposal applies the neutrosophic overset and underset concepts for thresholding the image. The global threshold method and the adaptive threshold method were used as the two types of thresholding methods in this article. Images could be symmetrical or asymmetrical in professional disciplines; the government maintains facial image databases as symmetrical. General-purpose images do not need to be symmetrical. Therefore, it is essential to know how thresholding functions in both scenarios. Since the article focuses on

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Song, Guo-Zhang Michael, David Doley, David Yates, Kuo-Jung Chao, and Chang-Fu Hsieh. "Improving accuracy of canopy hemispherical photography by a constant threshold value derived from an unobscured overcast sky." Canadian Journal of Forest Research 44, no. 1 (2014): 17–27. http://dx.doi.org/10.1139/cjfr-2013-0082.

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High image discrimination threshold values tend to be given to canopy hemispherical photographs (CHPs) with high exposure (resulting in bright images), but the effects of exposure on image threshold have been overlooked. A model canopy system was developed to precisely manipulate exposure (in relation to reference exposure measured from an unobscured overcast model sky), canopy openness, gap fragmentation and sky illumination of CHPs. We showed that there was a numerical trade-off relationship between exposure and image threshold of CHPs, whereas the last three factors had negligible effects o

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Karapet, Bohdan, Roman Savitskyi, and Tetiana Vakaliuk. "Method of comparing and transforming images obtained using UAV." Radioelectronic and Computer Systems 2024, no. 1 (2024): 99–115. http://dx.doi.org/10.32620/reks.2024.1.09.

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The subject matter of this article involves reviewing and developing methods for the comparison and transformation of images obtained using UAV via Computer Vision tools. The goal is to improve methods for image comparison and transformation. Various image-processing methods were employed to achieve the goal of this study,thereby contributing to the development of practical algorithms and approaches for image analysis and comparison. The tasks can be described as follows: 1) development of image comparison methods: design tools for the comparison of images from UAV that efficiently detect diff

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Rosin, Paul L., and Efstathios Ioannidis. "Evaluation of global image thresholding for change detection." Pattern Recognition Letters 24, no. 14 (2003): 2345–56. http://dx.doi.org/10.1016/s0167-8655(03)00060-6.

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Elen, Abdullah, and Emrah Dönmez. "Histogram-based global thresholding method for image binarization." Optik 306 (July 2024): 171814. http://dx.doi.org/10.1016/j.ijleo.2024.171814.

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Jasim, Wala’a, and Rana Mohammed. "A Survey on Segmentation Techniques for Image Processing." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (2021): 73–93. http://dx.doi.org/10.37917/ijeee.17.2.10.

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The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image

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Wang, Xiangluo, Chunlei Yang, Guo-Sen Xie, and Zhonghua Liu. "Image Thresholding Segmentation on Quantum State Space." Entropy 20, no. 10 (2018): 728. http://dx.doi.org/10.3390/e20100728.

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Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searchi

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Kausar, Shireen. "A Focused Study on Otsus Thresholding for Segmenting Images of Paralysis-Affected Individuals." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1894–900. https://doi.org/10.22214/ijraset.2025.72482.

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A crucial step in the diagnosis and treatment of neurological conditions like paralysis is medical picture segmentation. This study examines the application of Otsu's thresholding technique for segmenting medical images of patients with paralysis. The automatic global thresholding technique developed by Otsu is used to maximize the inter-class variance between foreground and background pixels in order to extract regions of interest. In computer vision and digital image processing, where the main goal is to divide a picture into meaningful structures, image segmentation is essential. The simpli

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Zhang, Chuang, Yue-Han Pei, Xiao-Xue Wang, Hong-Yu Hou, and Li-Hua Fu. "Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm." PLOS ONE 18, no. 6 (2023): e0287573. http://dx.doi.org/10.1371/journal.pone.0287573.

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To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergenc

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Gaur, S., A. M. Khan, D. L. Suthar, and Avnish Bora. "Image Edge Detection by Global Thresholding Using Riemann–Liouville Fractional Integral Operator." Mathematical Problems in Engineering 2024 (March 19, 2024): 1–7. http://dx.doi.org/10.1155/2024/9266585.

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It is difficult to give a fractional global threshold (FGT) that works well on all images as the image contents are totally different. This paper describes an interesting use of fractional calculus in the field of digital image processing. In the proposed method, the fractional global threshold-based edge detector (FGTED) is established using the Riemann–Liouville fractional integral operator. FGTED is used to find the microedges in minimum time for any input digital images. The results demonstrate that the FGTED outperforms conventional techniques for detecting microtype edges. The image with

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Bogiatzis, Athanasios, and Basil Papadopoulos. "Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures." Symmetry 11, no. 2 (2019): 286. http://dx.doi.org/10.3390/sym11020286.

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Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures whi

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Richert, Claudia, Yijuan Wu, Murilo Hablitzel, Erica T. Lilleodden, and Norbert Huber. "Image segmentation and analysis for densification mapping of nanoporous gold after nanoindentation." MRS Advances 6, no. 20 (2021): 519–23. http://dx.doi.org/10.1557/s43580-021-00099-w.

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AbstractSegmentation of scanning electron microscopy (SEM) images of focused ion beam (FIB) cross-sections through indented regions in nanoporous gold (np-Au) is carried out. A key challenge for image analysis of open porous materials is the appropriate binarization of the pore and gold ligament regions while excluding material lying below the cross-sectional plane. Here, a manual approach to thresholding is compared to global and local approaches. The global thresholding resulted in excessive deviations from the nominal solid fraction, due to a strong gray-scale gradient caused by the tilt an

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Sari, Toufik, Abderrahmane Kefali, and Halima Bahi. "Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques." Advances in Multimedia 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/934656.

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This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified intoforegroundorbackground. In the second stage,

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Sudeep, D. Thepade, R. Chaudhari Piyush, and Das Rik. "Identifying Land Usage from Aerial Image using Feature Fusion of Thepade's Sorted n-ary Block Truncation Coding and Bernsen Thresholding with Ensemble Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2612–21. https://doi.org/10.35940/ijeat.C5556.029320.

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Automatic Land Usage Identification is one of the most demanded research areas in Remote Sensing. One of the primitive sources for Land Usage Identification is Aerial images. Automatic Land Usage Identification is implemented by exploring different feature extraction methods whereas, these features are categorized into local and global content description of image. Fusion of local and global features may be a potential approach for land usage identification. Accordingly, the major contribution of work presented here is fusion of global color features extracted using TSBTC n-ary method (applied

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Abdusalomov, Akmalbek, Mukhriddin Mukhiddinov, Oybek Djuraev, Utkir Khamdamov, and Taeg Keun Whangbo. "Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics." Applied Sciences 10, no. 10 (2020): 3350. http://dx.doi.org/10.3390/app10103350.

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Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used

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Pambudi, Elindra Ambar, and Muhammad Ivan Nurhidayat. "Impact of Wolf Thresholding on Background Subtraction for Human Motion Detection." Compiler 13, no. 1 (2024): 39. http://dx.doi.org/10.28989/compiler.v13i1.2116.

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Series of motion detection based on background subtraction there is an image segmentation stage. Thresholding is a common technique used for the segmentation process. There are two types that can be used in thresholding techniques namely local and global. This research intends to implement local adaptive wolf thresholding as the threshold value of the background subtraction method to detect motion objects. The proposed method consists of the reading frame, background and foreground initialization of each frame, preprocessing, background subtraction, wolf thresholding, providing a bounding box,

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Danev, Angel, Atanaska Bosakova-Ardenska, and Miroslav Apostolov. "Application of thresholding algorithms for brown bread porosity evaluation." Food Science and Applied Biotechnology 2, no. 2 (2019): 99. http://dx.doi.org/10.30721/fsab2019.v2.i2.51.

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The bread is one of the most popular foods in Bulgaria. It’s quality is regulated by approved standards. This paper presents a computer based approach for evaluation of bread porosity which is one of physicochemical parameters of bread quality. The proposed approach includes image processing techniques. A Java program is developed to binarize images of bread and calculate ratio of white pixels to all (coefficient of diversity). This coefficient corresponds with physicochemical parameter- bread porosity. It is used an open-source plugin Auto_Threshold for image binarization. This plugin impleme

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Michalak, Hubert, and Krzysztof Okarma. "Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes." Entropy 21, no. 6 (2019): 562. http://dx.doi.org/10.3390/e21060562.

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Automatic text recognition from the natural images acquired in uncontrolled lighting conditions is a challenging task due to the presence of shadows hindering the shape analysis and classification of individual characters. Since the optical character recognition methods require prior image binarization, the application of classical global thresholding methods in such case makes it impossible to preserve the visibility of all characters. Nevertheless, the use of adaptive binarization does not always lead to satisfactory results for heavily unevenly illuminated document images. In this paper, th

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Charansiriphaisan, Kanjana, Sirapat Chiewchanwattana, and Khamron Sunat. "A Global Multilevel Thresholding Using Differential Evolution Approach." Mathematical Problems in Engineering 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/974024.

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Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images a

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Seifedine, Kadry, Rajinikanth Venkatesan, Koo Jamin, and Kang Byeong-Gwon. "Image multi-level-thresholding with Mayfly optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5420–29. https://doi.org/10.11591/ijece.v11i6.pp5420-5429.

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Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every May

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Lin, Shanying, Heming Jia, Laith Abualigah, and Maryam Altalhi. "Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures." Entropy 23, no. 12 (2021): 1700. http://dx.doi.org/10.3390/e23121700.

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Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ES

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Vlăsceanu, Giorgiana Violeta, and Nicolae Tarbă. "Harnessing Neural Networks for Enhancing Image Binarization Through Threshold Combination." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 2 (2023): 59–75. http://dx.doi.org/10.18662/brain/14.2/444.

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Threshold-based methods are prevalent across numerous domains, with specific relevance to image binarization, which traditionally employs global and local threshold algorithms. This paper presents a novel approach to image binarization, where the capacity of neural networks is utilized not just for determining optimal thresholds, but also for combining multiple global thresholds sourced from existing binarization techniques. The primary objective of our method is to develop a robust binarization strategy capable of managing a wide array of image conditions. By integrating the strengths of vari

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Giorgiana, Violeta Vlăsceanu, and Tarbă Nicolae. "Harnessing Neural Networks for Enhancing Image Binarization Through Threshold Combination." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 2 (2024): 59–75. https://doi.org/10.18662/brain/14.2/444.

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<em>Threshold-based methods are prevalent across numerous domains, with specific relevance to image binarization, which traditionally employs global and local threshold algorithms. This paper presents a novel approach to image binarization, where the capacity of neural networks is utilized not just for determining optimal thresholds, but also for combining multiple global thresholds sourced from existing binarization techniques. The primary objective of our method is to develop a robust binarization strategy capable of managing a wide array of image conditions. By integrating the strengths of

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Rahman Ahad, Md Atiqur, and Israt Jahan. "A Study of Left Ventricular (LV) Segmentation on Cardiac Cine-MR Images." Jurnal Kejuruteraan 34, no. 3 (2022): 463–73. http://dx.doi.org/10.17576/jkukm-2022-34(3)-13.

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Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed data

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Ashir, Abubakar M. "Multilevel Thresholding for Image Segmentation Using Mean Gradient." Journal of Electrical and Computer Engineering 2022 (February 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/1254852.

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Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically i

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Kadry, Seifedine, Venkatesan Rajinikanth, Jamin Koo, and Byeong-Gwon Kang. "Image multi-level-thresholding with Mayfly optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5420. http://dx.doi.org/10.11591/ijece.v11i6.pp5420-5429.

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<span>Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in whi

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Mohammed, Zhana Fidakar, and Alan Anwer Abdulla. "Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images." UHD Journal of Science and Technology 4, no. 1 (2020): 9. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp9-17.

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood

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Pham, D. T., and R. J. Alcock. "Automatic Detection of Defects on Birch Wood Boards." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 210, no. 1 (1996): 45–52. http://dx.doi.org/10.1243/pime_proc_1996_210_292_02.

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In a factory which produces veneer boards from birch wood, grading of the boards into different quality categories is usually part of the production process. To improve the efficiency of grading, attempts are being made to automate it using automated visual inspection (AVI). Integral to the process of inspection is segmentation. Segmentation is the part of the AVI process concerned with separating clear wood and defective areas in the image. This paper describes a system that is used to segment the images of birch wood boards. The system consists of four modules which are called global adaptiv

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Ouled Zaid, Azza, Christian Olivier, Olivier Alata, and Francois Marmoiton. "Transform image coding with global thresholding: Application to baseline JPEG." Pattern Recognition Letters 24, no. 7 (2003): 959–64. http://dx.doi.org/10.1016/s0167-8655(02)00219-2.

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Purwanto, Dannu, and Agustiyar Agustiyar. "GLOBAL THRESHOLDING IMPLEMENTATION FOR NOISE HANDLING IN DIGITAL IMAGE RECOGNITION." Jurnal Transformatika 21, no. 2 (2024): 93. http://dx.doi.org/10.26623/transformatika.v21i2.8713.

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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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Michalak, Hubert, and Krzysztof Okarma. "Fast Binarization of Unevenly Illuminated Document Images Based on Background Estimation for Optical Character Recognition Purposes." JUCS - Journal of Universal Computer Science 25, no. (6) (2019): 627–46. https://doi.org/10.3217/jucs-025-06-0627.

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One of the key operations during the image preprocessing step in Optical Character Recognition (OCR) algorithms is image binarization. Although for uniformly illuminated images, obtained typically by atbed scanners, the use of a single global threshold may be sufficient for further recognition of individual characters, it cannot be applied directly in case of non-uniform lightened document images. Such problem may occur during capturing photos of documents in unknown lighting conditions making a proper text recognition impossible in some parts of the image. Since the application of popular ada

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Ansari, R. A., and B. K. Mohan. "Noise Filtering of Remotely Sensed Images using Iterative Thresholding of Wavelet and Curvelet Transforms." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 57–64. http://dx.doi.org/10.5194/isprsarchives-xl-1-57-2014.

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This article presents techniques for noise filtering of remotely sensed images based on Multi-resolution Analysis (MRA). Multiresolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image interpretation. The multiresolution image analysis methods have the ability to analyze the image in an adaptive manner, capturing local information as well as global information. Further, noise being one of the biggest problems in image analysis and interpretation for further processing, is effectively handled by multi-resolution methods. The paper aims at the analysis o

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Zuraini, Othman, Abdullah Azizi, Kasmin Fauziah, and Sakinah Syed Ahmad Sharifah. "Road crack detection using adaptive multi resolution thresholding techniques." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 4 (2019): 1874–81. https://doi.org/10.12928/TELKOMNIKA.v17i4.12755.

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Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist’s knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image

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Cao, Qinglin, Letu Qingge, and Pei Yang. "Performance Analysis of Otsu-Based Thresholding Algorithms: A Comparative Study." Journal of Sensors 2021 (October 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/4896853.

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Image thresholding is a widely used technology for a lot of computer vision applications, and among various global thresholding algorithms, Otsu-based approaches are very popular due to their simplicity and effectiveness. While the usage of Otsu-based thresholding methods is well discussed, the performance analyses of these methods are rather limited. In this paper, we first review nine Otsu-based approaches and categorize them based on their objective functions, preprocessing, and postprocessing strategies. Second, we conduct several experiments to analyze the model characteristics using diff

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Barros, Wysterlânya K. P., Leonardo A. Dias, and Marcelo A. C. Fernandes. "Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA." Sensors 21, no. 12 (2021): 4151. http://dx.doi.org/10.3390/s21124151.

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This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis resu

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Mishra, Shashwati, and Mrutyunjaya Panda. "Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions." International Journal of Fuzzy System Applications 8, no. 4 (2019): 39–59. http://dx.doi.org/10.4018/ijfsa.2019100103.

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Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussia

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Garg, Meenu, and Sheifali Gupta. "Extraction of Vasculature Map of Color Retinal Fundus Image." Journal of Computational and Theoretical Nanoscience 16, no. 10 (2019): 4188–201. http://dx.doi.org/10.1166/jctn.2019.8500.

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This paper presents an unsupervised and novel method for extraction of vasculature map from colored retinal fundus images. Proposed technique makes use of a fusion of bimodal masking and global thresholding technique for extraction of vessels. For this, adaptive histogram equalization method is used for enhancement of the retinal input fundus images while, on the other hand, an average filter is used on the masked images to remove the noise from the image. At this stage, bimodal masking is used to generate the masked image to exclude the pixels that belong to the background. The use of this te

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Qin, Jun, ChuTing Wang, and GuiHe Qin. "A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization." Mathematical Problems in Engineering 2019 (June 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/6706590.

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Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is r

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Xiong, Fu Song. "Image Thresholding Using Parzen Window Estimation and Tsallis Entropy." Applied Mechanics and Materials 198-199 (September 2012): 277–83. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.277.

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Image segmentation is an important problem of digital image processing and also a common difficult problem. In this paper, a new image thresholding method based on parzen-window estimation and Tsallis entropy is proposed. The method used Parzen-window technology to estimate the spatial probability distribution of image gray level values，then combined with the Tsallis entropy to construct a new criterion function, and at last searched the optimal global threshold in the scope of gray level to maximum the criterion function. This new method has some advantages, such as high accuracy to image seg

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