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Relevant bibliographies by topics / Image thresholding

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Academic literature on the topic 'Image thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Image thresholding"

1

Huang, Hui Xian, Juan Gong, and Te Zhang. "Method of Adaptive Wavelet Thresholding Used in Image Denoising." Advanced Materials Research 204-210 (February 2011): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1184.

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According to multi-resolution analysis of wavelet threshold denoising principle, this paper presented two improved algorithms of continuity and adaptive threshold based on hard thresholding. The soft thresholding (hyperbolic thresholding) was used in the intervals after setting two thresholds, and the isolated points were removed according to the adjacent correlation coefficient during the processing. As a result, the hard thresholding’s shortcomings were reduced. The simulation results show that improved algorithms have both better visual effect and PSNR than the traditional approaches.

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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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Riyaz, Mohammed M., and M. Sabibullah. "Thresholding techniques in computer vision applications." i-manager’s Journal on Image Processing 11, no. 2 (2024): 27. http://dx.doi.org/10.26634/jip.11.2.21001.

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Thresholding techniques are key pillars of image processing, especially for distinguishing objects in complex environments. This paper examines four types of thresholding strategies, each based on different theories, practical, popular, and advanced. Through a thorough literature review, the paper explains the thresholding techniques, thresholding operations, evaluation metrics, image processing techniques, and Python code for ROI of binary images in an understandable manner. The findings underscore the significance of thresholding in various applications, from object recognition to medical im

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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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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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Manda, Manikanta Prahlad, and Hi Seok Kim. "A Fast Image Thresholding Algorithm for Infrared Images Based on Histogram Approximation and Circuit Theory." Algorithms 13, no. 9 (2020): 207. http://dx.doi.org/10.3390/a13090207.

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Image thresholding is one of the fastest and most effective methods of detecting objects in infrared images. This paper proposes an infrared image thresholding method based on the functional approximation of the histogram. The one-dimensional histogram of the image is approximated to the transient response of a first-order linear circuit. The threshold value for the image segmentation is formulated using combinational analogues of standard operators and principles from the concept of the transient behavior of the first-order linear circuit. The proposed method is tested on infrared images gath

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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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Li, Qingyong, Weitao Lu, and Jun Yang. "A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images." Journal of Atmospheric and Oceanic Technology 28, no. 10 (2011): 1286–96. http://dx.doi.org/10.1175/jtech-d-11-00009.1.

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Abstract Cloud detection is the precondition for deriving other information (e.g., cloud cover) in ground-based sky imager applications. This paper puts forward an effective cloud detection approach, the Hybrid Thresholding Algorithm (HYTA) that fully exploits the benefits of the combination of fixed and adaptive thresholding methods. First, HYTA transforms an input color cloud image into a normalized blue/red channel ratio image that can keep a distinct contrast, even with noise and outliers. Then, HYTA identifies the ratio image as either unimodal or bimodal according to its standard deviati

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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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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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Dissertations / Theses on the topic "Image thresholding"

1

Hertz, Lois. "Robust image thresholding techniques for automated scene analysis." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15050.

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Katakam, Nikhil. "Pavement crack detection system through localized thresholding /." Connect to full text in OhioLINK ETD Center, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1260820344.

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Thesis (M.S.)--University of Toledo, 2009.<br>Typescript. "Submitted as partial fulfillment of the requirements for The Master of Science in Engineering." "A thesis entitled"--at head of title. Bibliography: leaves 65-68.

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Kieri, Andreas. "Context Dependent Thresholding and Filter Selection for Optical Character Recognition." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-197460.

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Thresholding algorithms and filters are of great importance when utilizing OCR to extract information from text documents such as invoices. Invoice documents vary greatly and since the performance of image processing methods when applied to those documents will vary accordingly, selecting appropriate methods is critical if a high recognition rate is to be obtained. This paper aims to determine if a document recognition system that automatically selects optimal processing methods, based on the characteristics of input images, will yield a higher recognition rate than what can be achieved by a m

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Granlund, Oskar, and Kai Böhrnsen. "Improving character recognition by thresholding natural images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.

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The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocess

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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." University of Sydney. School of Electrical and Information Engineering, 2005. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits t

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Zhao, Mansuo. "Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits t

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Bunn, Wendy J. "Sensitivity to distributional assumptions in estimation of the ODP thresholding function /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1918.pdf.

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Quan, Jin. "Image Denoising of Gaussian and Poisson Noise Based on Wavelet Thresholding." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1380556846.

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Pakalapati, Himani Raj. "Programming of Microcontroller and/or FPGA for Wafer-Level Applications - Display Control, Simple Stereo Processing, Simple Image Recognition." Thesis, Linköpings universitet, Elektroniksystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89795.

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In this work the usage of a WLC (Wafer Level Camera) for ensuring road safety has been presented. A prototype of a WLC along with the Aptina MT9M114 stereoboard has been used for this project. The basic idea is to observe the movements of the driver. By doing so an understanding of whether the driver is concentrating on the road can be achieved. For this project the display of the required scene is captured with a wafer-level camera pair. Using the image pairs stereo processing is performed to obtain the real depth of the objects in the scene. Image recognition is used to separate the object f

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Vantaram, Sreenath Rao. "Fast unsupervised multiresolution color image segmentation using adaptive gradient thresholding and progressive region growing /." Online version of thesis, 2009. http://hdl.handle.net/1850/9016.

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Books on the topic "Image thresholding"

1

Dey, Nilanjan, Nadaradjane Sri Madhava Raja, and Venkatesan Rajinikanth. Beginner's Guide to Multi-Level Image Thresholding. Taylor & Francis Group, 2020.

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Dey, Nilanjan, Nadaradjane Sri Madhava Raja, and Venkatesan Rajinikanth. Beginner's Guide to Multi-Level Image Thresholding. Taylor & Francis Group, 2020.

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Dey, Nilanjan, Nadaradjane Sri Madhava Raja, and Venkatesan Rajinikanth. Beginner's Guide to Multi-Level Image Thresholding. Taylor & Francis Group, 2020.

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Dey, Nilanjan, Nadaradjane Sri Madhava Raja, and Venkatesan Rajinikanth. Beginner's Guide to Multi-Level Image Thresholding. Taylor & Francis Group, 2020.

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Beginner's Guide to Multi-Level Image Thresholding. Taylor & Francis Group, 2020.

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Beginner's Guide to Multi-Level Image Thresholding. CRC Press LLC, 2020.

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i

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Sahandi, Mohammad Reza. Image compression using vector encoding: Illumination correction, noise reduction, and thresholding of digitized CCTV signals produce binary images which are encoded as vector lists---. 1987.

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Book chapters on the topic "Image thresholding"

1

Mitchell, H. B. "Image Thresholding." In Image Fusion. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_12.

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Caponetti, Laura, and Giovanna Castellano. "Image Thresholding." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44130-6_9.

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Burger, Wilhelm, and Mark J. Burge. "Automatic Thresholding." In Principles of Digital Image Processing. Springer London, 2013. http://dx.doi.org/10.1007/978-1-84882-919-0_2.

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Pau, L. F. "Image Quantization and Thresholding." In Computer Vision for Electronics Manufacturing. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0507-1_14.

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Pajankar, Ashwin. "Morphology, Thresholding, and Segmentation." In Raspberry Pi Image Processing Programming. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2731-2_9.

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Kovalevsky, Vladimir. "Shading Correction with Thresholding." In Modern Algorithms for Image Processing. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_4.

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Pajankar, Ashwin. "Morphology, Thresholding, and Segmentation." In Raspberry Pi Image Processing Programming. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8270-0_9.

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Oliva, Diego, Mohamed Abd Elaziz, and Salvador Hinojosa. "Contextual Information in Image Thresholding." In Metaheuristic Algorithms for Image Segmentation: Theory and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12931-6_15.

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Oliva, Diego, Mohamed Abd Elaziz, and Salvador Hinojosa. "Tsallis Entropy for Image Thresholding." In Metaheuristic Algorithms for Image Segmentation: Theory and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12931-6_9.

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Forero-Vargas, Manuel Guillermo. "Fuzzy Thresholding and Histogram Analysis." In Fuzzy Filters for Image Processing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36420-7_6.

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Conference papers on the topic "Image thresholding"

1

Olhede, Sofia C. "Hyperanalytic Thresholding." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312693.

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Al-Qunaieer, Fares S., Hamid R. Tizhoosh, and Shahryar Rahnamayan. "Oppositional fuzzy image thresholding." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584265.

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Zhang, Yujin, Jan J. Gerbrands, and Eric Backer. "Thresholding three-dimensional image." In Lausanne - DL tentative, edited by Murat Kunt. SPIE, 1990. http://dx.doi.org/10.1117/12.24141.

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Kowalski, Matthieu. "Thresholding RULES and iterative shrinkage/thresholding algorithm: A convergence study." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025843.

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Al-Nasser, Mustafa, Moustafa Elshafei, and Abdelsalam Al-Sarkhi. "Image Adaptive Thresholding for Multiphase Wavy Flow." In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-22263.

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Multiphase flow measurement is a very challenging issue in process industry. One of the promising approaches for multiphase flow analysis is image processing. Image segmentation is very important step in multiphase flow analysis. Determination of appropriate threshold value is very critical step for correct identification of the liquid and gas phases. There are two main thresholding techniques: Global and Adaptive. Adaptive thresholding is more suitable for multiphase flow case due to it’s adaptability to image conditions such non-uniform illumination and noise. In this work, six adaptive thre

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Jiang, Wenfei, Fan Zhang, Longin Jan Latecki, Zhibo Chen, and Yi Hu. "Coefficient Thresholding with Image Restoration." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.54.

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Othman, Ahmed A., and Hamid R. Tizhoosh. "Image thresholding using neural network." In 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2010. http://dx.doi.org/10.1109/isda.2010.5687030.

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Sung, Jung-Min, Dae-Chul Kim, Bong-Yeol Choi, and Yeong-Ho Ha. "Image thresholding using standard deviation." In IS&T/SPIE Electronic Imaging, edited by Kurt S. Niel and Philip R. Bingham. SPIE, 2014. http://dx.doi.org/10.1117/12.2040990.

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Zhao, Mingsheng, and Congxiao Bao. "Image thresholding by histogram transformation." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by David P. Casasent and Andrew G. Tescher. SPIE, 1994. http://dx.doi.org/10.1117/12.177723.

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Ray, Nilanjan, and Baidya Nath Saha. "Edge Sensitive Variational Image Thresholding." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379515.

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Reports on the topic "Image thresholding"

1

Beauchemin, M., and K. B. Fung. Image Thresholding Based on Spatial Variation Attribute Similarity. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220053.

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Abdallah, Mahmoud A., and Ram-Nandan P. Singh. Image Data Compression by Adaptive Thresholding of Wavelet Coefficients. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada375823.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavemen

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavemen

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Hammouti, A., S. Larmagnat, C. Rivard, and D. Pham Van Bang. Use of CT-scan images to build geomaterial 3D pore network representation in preparation for numerical simulations of fluid flow and heat transfer, Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331502.

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Non-intrusive techniques such as medical CT-Scan or micro-CT allow the definition of 3D connected pore networks in porous materials, such as sedimentary rocks or concrete. The definition of these networks is a key step towards the evaluation of fluid flow and heat transfer in energy resource (e.g., hydrocarbon and geothermal reservoirs) and CO2 sequestration research projects. As material heterogeneities play a role at all scales (from micro- to project-scale), numerical models represent a powerful tool for bridging the gap between small-scale measurements provided by X-ray imaging techniques

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