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Journal articles on the topic 'Thresholding technique'

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Published: 3 June 2025

Last updated: 20 July 2025

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1

Hassan, M. H., and P. Siy. "Real-time thresholding technique." Electronics Letters 23, no. 7 (1987): 339. http://dx.doi.org/10.1049/el:19870251.

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Roshan, A., and Y. Zhang. "MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-173-2019.

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<p><strong>Abstract.</strong> Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed

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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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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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Fang, Ning, P. Pai Srinivasa, and Nathan Edwards. "Wavelet-Based Denoising of Vibration Signals for Tool-Edge Wear Detection in High Speed Machining of Inconel 718." Advanced Materials Research 415-417 (December 2011): 1512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.1512.

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Denoising is an essential step and plays a significant role in tool condition monitoring. In the present study, four wavelet-based denoising techniques are studied and compared, including conventional hard-thresholding, conventional soft-thresholding, generalized soft-thresholding, and soft-thresholding with Stein’s unbiased risk estimate (SURE). The results show that soft-thresholding with SURE generates the lowest mean squared error, and hence is the most appropriate denoising technique for tool-edge wear detection in high speed machining of Inconel 718.

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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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Karakoyun, Murat, Nurdan Akhan Baykan, and Mehmet Hacibeyoglu. "Multi-Level Thresholding for Image Segmentation With Swarm Optimization Algorithms." International Research Journal of Electronics and Computer Engineering 3, no. 3 (2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.3.01.

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Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That’s why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-

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bonga, Siya, and Shi ra. "Separation from Brain Magnetic Resonance images (MRI) using Multistage Thresholding Technique." International Journal of Pharmacy and Biomedical Engineering 2, no. 3 (2015): 9–12. http://dx.doi.org/10.14445/23942576/ijpbe-v2i3p103.

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Image separation is a significant task concerned in dissimilar areas from image dispensation to picture examination. One of the simplest methods for image segmentation is thresholding. Though, many thresholding methods are based on a bi-level thresholding process. These methods can be extensive to form multi-level thresholding, but they become computationally expensive since a large number of iterations would be necessary for computing the most select threshold values. In order to conquer this difficulty, a new process based on a Shrinking Search Space (3S) algorithm is proposed in this paper.

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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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10

Dong, Yu Bing, Ming Jing Li, and Guang Liang Cheng. "Evaluation and Comparison of Thresholding Segmentation Techniques." Applied Mechanics and Materials 519-520 (February 2014): 689–92. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.689.

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Threshold technique is one of the important techniques in image segmentation. Various thresholding segmentation techniques such as histogram, grayscale expectations, Otsu, maximum entropy and iterative are studied and compared by using Matlab 7.0. Experimental results show that the iterative method can perform well and get a better result than the other thresholding segmentation methods.

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Suguna, GC. "Denoising wrist pulse signals using variance thresholding technique." Indian Journal of Science and Technology 13, no. 40 (2020): 4275–86. http://dx.doi.org/10.17485/ijst/v13i40.1625.

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Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak

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G., Sai Chaitanya Kumar, Kiran Kumar Reddi, Apparao Naidu G., and Harikiran J. "Noise Removal in Microarray Images Using Variational Mode Decomposition Technique." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 4 (2017): 1750–56. https://doi.org/10.12928/TELKOMNIKA.v15i4.5375.

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Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Enhancement, Gridding, Segmentation and Intensity extraction are important steps in microarray image analysis. This paper presents a noise removal method in microarray images based on Variational Mode Decomposition (VMD). VMD is a signal processing method which decomposes any input signal into discrete number of

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P.D., Sathya. "MINIMUM CROSS ENTROPY BASED IMAGE SEGMENTATION USING NEW HEURISTIC OPTIMIZATION TECHNIQUE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2019): 522–30. https://doi.org/10.5281/zenodo.2656443.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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Phanindra Kumar N.S.R. and Prasad Reddy P.V.G.D. "Evolutionary Image Thresholding for Image Segmentation." International Journal of Computer Vision and Image Processing 9, no. 1 (2019): 17–34. http://dx.doi.org/10.4018/ijcvip.2019010102.

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hyb

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Mapayi, Temitope, Serestina Viriri, and Jules-Raymond Tapamo. "Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/597475.

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Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates

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Prahara, Murinto, and Erik Ujianto. "Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 65–77. http://dx.doi.org/10.15849/ijasca.221128.05.

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Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time t

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17

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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18

S, Sheela, and Sumathi M. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. https://doi.org/10.17485/IJST/v13i39.1602.

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Abstract <strong>Objective:</strong> To achieve the accurate segmentation of ovarian cyst from the ultrasound images. <strong>Method:</strong> Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enh

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19

GC, Suguna, and Veerabhadrappa ST. "Denoising wrist pulse signals using variance thresholding technique." Indian Journal of Science and Technology 13, no. 40 (2020): 4275–86. https://doi.org/10.17485/IJST/v13i40.1625.

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Abstract <strong>Background/Objectives:</strong> Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. <strong>Methods/Statistical analysis:</strong> Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. <strong>Findings</

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20

S., Alex. "Detection of Fungal Disease in Cabbage Images Using Adaptive Thresholding Technique Compared with Threshold Technique." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 1112–25. http://dx.doi.org/10.47059/revistageintec.v11i4.2172.

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21

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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Kim, Chi Hyun, Henry Zhang, George Mikhail та ін. "Effects of Thresholding Techniques on μCT-Based Finite Element Models of Trabecular Bone". Journal of Biomechanical Engineering 129, № 4 (2006): 481–86. http://dx.doi.org/10.1115/1.2746368.

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Microimaging based finite element analysis is widely used to predict the mechanical properties of trabecular bone. The choice of thresholding technique, a necessary step in converting grayscale images to finite element models, can significantly influence the predicted bone volume fraction and mechanical properties. Therefore, we investigated the effects of thresholding techniques on microcomputed tomography (micro-CT) based finite element models of trabecular bone. Three types of thresholding techniques were applied to 16-bit micro-CT images of trabecular bone to create three different models

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Sowjanya, Kotte, Munazzar Ajreen, Paka Sidharth, Kakara Sriharsha, and Lade Aishwarya Rao. "Fuzzy thresholding technique for multiregion picture division." International Research Journal on Advanced Science Hub 4, no. 03 (2022): 45–50. http://dx.doi.org/10.47392/irjash.2022.011.

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Ahmed, Arwa, and Alnazeer Osman. "Optic Disc Segmentation Using Manual Thresholding Technique." Journal of Clinical Engineering 44, no. 1 (2019): 28–34. http://dx.doi.org/10.1097/jce.0000000000000295.

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Goh, Ta Yang, Shafriza Nisha Basah, Haniza Yazid, Muhammad Juhairi Aziz Safar, and Fathinul Syahir Ahmad Saad. "Performance analysis of image thresholding: Otsu technique." Measurement 114 (January 2018): 298–307. http://dx.doi.org/10.1016/j.measurement.2017.09.052.

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Cheriet, M., J. N. Said, and C. Y. Suen. "A recursive thresholding technique for image segmentation." IEEE Transactions on Image Processing 7, no. 6 (1998): 918–21. http://dx.doi.org/10.1109/83.679444.

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Uthayakumar, R., and A. Gowrisankar. "Generalized Fractal Dimensions in Image Thresholding Technique." Information Sciences Letters 3, no. 3 (2014): 125–34. http://dx.doi.org/10.12785/isl/030306.

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Pootheri, Shamna, Daniel Ellam, Thomas Grübl, and Yang Liu. "A Two-Stage Automatic Color Thresholding Technique." Sensors 23, no. 6 (2023): 3361. http://dx.doi.org/10.3390/s23063361.

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Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing ba

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Hashem, Abdel-Rahiem A., Ahmed I. Taloba, and Majid A. Askar. "Textual Characters Detection in Complex Scene Images based on Bradley Thresholding Technique in Compare with Statistical Thresholding Technique." International Journal of Computer Applications 185, no. 36 (2023): 47–53. http://dx.doi.org/10.5120/ijca2023923158.

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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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Sheela, S. "Enhancer for ovarian cyst segmentation using adaptive thresholding technique." Indian Journal of Science and Technology 13, no. 39 (2020): 4142–50. http://dx.doi.org/10.17485/ijst/v13i39.1602.

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Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficie

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Mapayi, Temitope, Serestina Viriri, and Jules-Raymond Tapamo. "Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/895267.

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Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques

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Gurinder, Kaur Sodhi, .Chatterji S, and Kant Sharma Kamal. "Skin Cancer Diagnosis using Ostu Thresholding with and Without Bat Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1569–73. https://doi.org/10.35940/ijeat.B3651.049420.

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Nowadays , Cancer diseases is becoming a house hold name but science has exponentially manifold to fight against the deadly disease. Even though ;many cancer diseases are still untouched or not followed properly clinically detected at early stages and lead to incurable disease ,one of the cancer disease termed as Skin Cancer . The main concern is early detection so that it should not be spread to other parts of the body and patient fails to recover . This is not detectable if it’s not known to patient and not informed to concerned doctor at a right time . This manuscript discusses and ex

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Bustince, H., E. Barrenechea, M. Pagola, and R. Orduna. "Image Thresholding Computation Using Atanassov’s Intuitionistic Fuzzy Sets." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 2 (2007): 187–94. http://dx.doi.org/10.20965/jaciii.2007.p0187.

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In this paper, a new thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions is introduced. In recent years, various thresholding techniques ([18, 24]) based on fuzzy set theory have been introduced to overcome the problem of non-uniform illumination and inherent image vagueness. In this paper we analyze this task and propose a new method for handling the grayness ambiguity and vagueness during the process of threshold selection.

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Lou, Linjiang, Chen Chen, Minmin Li, and Kun Liu. "Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 331–36. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-331-2024.

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Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm.

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Hamdoun, Nabila, and Driss Mentagui. "Image Processing in Automatic License Plate Recognition Using Combined Methods." Serdica Journal of Computing 16, no. 1 (2022): 1–23. http://dx.doi.org/10.55630/sjc.2022.16.1-23.

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There are many existing studies released in the field of Computer Vision, especially the field of Automatic License Plate Recognition. However, most of them are focused on using one method at the time, such as Thresholding algorithms, Edge Detections or Morphological transformations. This research paper proposes to automate the License plate recognition process, by combining four algorithms from the three methods mentioned above: Adaptive Thresholding, Otsu's Thresholding, Canny Edge Detection and Morphological Gradient applied to Edge Detection. The Goal achieved is to obtain the best binary

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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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Gujjunoori, Sagar, Madhu Oruganti, N. Aparna, M. Srija, and Chaitrali Dangare. "Tracking and Size Estimation of Objects in Motion based on Median of Localized Thresholding." International Journal of Engineering & Technology 7, no. 4.6 (2018): 78. http://dx.doi.org/10.14419/ijet.v7i4.6.20241.

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Motion detection and tracking play an important role in Computer vision and Robotics. Optical flow based methods to estimate the motion are widely explored during the last decade. The motion information retrieved from these techniques has enormous applications. Video analysis based on the size, speed, and directions of objects have wider applications in computer vision, robotics and watermarking. Segmentation of moving objects based on the optical flow is very challenging. In this paper, we present a model to estimate the size of a moving object based on the optical flow technique and present

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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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Serri Ismael Hamad. "Comparative between the binary thresholding technique and the Otsu method for the people detection." International Journal of Computer Technology and Science 2, no. 1 (2025): 98–111. https://doi.org/10.62951/ijcts.v2i1.134.

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In image detection processes where there is a variation in brightness between pixels, techniques are required to obtain optimal and adaptable threshold values for these variations. Therefore, a comparison between the binary thresholding technique and the adaptive method of Otsu is made, in videos with dynamic and static background, weighing the response time of the algorithm, memory used, requirement of the central processing unit and hits in the detections, in the languages of Python and M (Matlab). The techniques in Python present better results in terms of response time and memory space; wh

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Maji, Pradipta, Malay K. Kundu, and Bhabatosh Chanda. "Second Order Fuzzy Measure and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images." Fundamenta Informaticae 88, no. 1-2 (2008): 161–76. https://doi.org/10.3233/fun-2008-881-207.

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A robust thresholding technique is proposed in this paper for segmentation of brain MR images. It is based on the fuzzy thresholding techniques. Its aim is to threshold the gray level histogram of brain MR images by splitting the image histogram into multiple crisp subsets. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through a second order fuzzy measure such as fuzzy correlation, fuzzy entropy, and index of fuzziness. To calculate the second order fuzzy measure, a weighted co-occurrence matrix is presented, which e

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Wooh, S. C., and I. M. Daniel. "Enhancement Techniques for Ultrasonic Nondestructive Evaluation of Composite Materials." Journal of Engineering Materials and Technology 112, no. 2 (1990): 175–82. http://dx.doi.org/10.1115/1.2903304.

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Conventional ultrasonic C-scanning sometimes produces distorted and degraded images due to a variety of reasons, including surface roughness, beam dispersion, extraneous noise and imperfect fidelity of the total acquisition system. Enhancement techniques, using computer data acquisition and processing, can be used to enhance and restore the image. Enhancement techniques described include contrast stretching and median filtering, histogram equalization, thresholding, dynamic thresholding, thresholding depending on boundary characteristics, one-dimensional segmentation and intensity scans with h

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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, print

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Gopatoti, Anandbabu, and Ramadass N. "Denoising of MRI Images Using Sub Band Adaptive Thresholding Technique with Neighbourhood Pixel Filtering Algorithm." Journal of Advanced Research in Dynamical and Control Systems 8, no. 12 (2016): 5–12. https://doi.org/10.5281/zenodo.11035720.

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Image denoising has become popular in medical imaging especially in the Magnetic Resonance Imaging (MRI). Initially we Presented Subband adaptive thresholding technique based on wavelet coefficient for reducing the noise in MRI Images, latter this technique is used along with Neighbourhood Pixel Filtering Algorithm  (NPFA)for denoising MRI Images. Using Maximum Likelihood (ML) estimator or a Maximum a Posterior (MAP) estimator, a statistical model is proposed to calculate the noise variance for each coefficient based on the subband. A new mechanism for reduction of noise by

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Lingam, K. Mallikharjuna, and V. S. K. Reddy. "Content relative thresholding technique for key frame extraction." International Journal of Knowledge-based and Intelligent Engineering Systems 23, no. 4 (2020): 249–58. http://dx.doi.org/10.3233/kes-190416.

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N. D., Salih, Wan Noorshahida Mohd Isa, and Marwan D. Saleh. "THRESHOLDING BASED TECHNIQUE FOR RETINAL BLOOD VESSEL EXTRACTION." Indian Journal of Computer Science and Engineering 12, no. 6 (2021): 1875–85. http://dx.doi.org/10.21817/indjcse/2021/v12i6/211206189.

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El-Zaart, A. "Images thresholding using ISODATA technique with gamma distribution." Pattern Recognition and Image Analysis 20, no. 1 (2010): 29–41. http://dx.doi.org/10.1134/s1054661810010037.

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Friel, Nial, and Ilya S. Molchanov. "A new thresholding technique based on random sets." Pattern Recognition 32, no. 9 (1999): 1507–17. http://dx.doi.org/10.1016/s0031-3203(99)00017-5.

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Roques, S., F. Bourzeix, and K. Bouyoucef. "Soft-thresholding technique and restoration of 3C273 jet." Astrophysics and Space Science 239, no. 2 (1996): 297–304. http://dx.doi.org/10.1007/bf00645783.

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Lee, Howard, and Yi-Ping Phoebe Chen. "Skin cancer extraction with optimum fuzzy thresholding technique." Applied Intelligence 40, no. 3 (2013): 415–26. http://dx.doi.org/10.1007/s10489-013-0474-0.

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