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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: 20 February 2023

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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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2

KHASHMAN, ADNAN, and BORAN SEKEROGLU. "DOCUMENT IMAGE BINARISATION USING A SUPERVISED NEURAL NETWORK." International Journal of Neural Systems 18, no. 05 (October 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 method to global thresholding, where a neural network is trained using local threshold values of an image in order to determine an optimum global threshold value which is used to binarise the whole image. The proposed method is compared with five local thresholding methods, and the experimental results indicate that our method is computationally cost-effective and capable of binarising scanned degraded documents with superior results.

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3

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 (August 24, 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 gathered from the standard databases and the experimental results are compared with the existing state-of-the-art infrared image thresholding methods. We realized through the experimental results that our method is well suited to perform infrared image thresholding.

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4

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 (July 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 quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

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5

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 (January 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 hybrid bacteria foraging optimization technique and particle swam optimization (hBFOA-PSO). The proposed hBFOA-PSO algorithm performance in segmenting the image is tested using natural and standard images. Experiments show that the proposed hBFOA-PSO is better than particle swarm optimization (PSO), the cuckoo search (CS) and the adaptive Cuckoo Search (ACS).

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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 (October 1, 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 deviation, and the unimodal and bimodal images are handled by fixed and minimum cross entropy (MCE) thresholding algorithms, respectively. The experimental results demonstrate that HYTA shows an accuracy of 88.53%, which is far higher than those of either fixed or MCE thresholding alone. Moreover, HYTA is also verified to outperform other state-of-the-art cloud detection approaches.

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7

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 (September 28, 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-level thresholding technique uses n threshold values where n greater than one. In this paper, two swarm optimization algorithms (Particle Swarm Optimization, PSO and Cat Swarm Optimization, CSO) are applied on finding the optimum threshold values for the multi-level thresholding. In literature, there are some minimization or maximization functions to find the best threshold values for thresholding problem. Some of these methods are: Tsalli’s Entropy, Kapur’s Entropy, Renyi’s Entropy, Otsu’s Method (within class variance/between class variance), the Minimum Cross Entropy Thresholding (MCET) etc.In this work, Otsu’s (within class variance) method, which is one of these popular functions,is used as the fitness function of algorithms.In the experiments, five real images are segmented by usingParticle Swarm Algorithm and Cat Swarm Optimization Algorithms. The performances of the swarm algorithms on multi-level thresholding problem arecompared with Peak Signal-to-Noise Ratio (PSNR) and fitness function (FS) values. As a result, the PSO yields better performance than CSO.

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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 (June 14, 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, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.

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Chikanbanjar, Milan. "Comparative analysis between non-linear wavelet based image denoising techniques." Journal of Science and Engineering 5 (August 31, 2018): 58–67. http://dx.doi.org/10.3126/jsce.v5i0.22373.

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Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) and SSIM (Structural Similarity index). Here, non-linear wavelet transform denoising techniques of natural images are studied, analyzed and compared using thresholding techniques such as soft, hard, semi-soft, LevelShrink, SUREShrink, VisuShrink and BayesShrink. On most of the tests, PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods. For instance, from tests PSNR and SSIM values of lena image for VISUShrink Hard, VISUShrink Soft, VISUShrink Semi Soft, LevelShrink Hard, LevelShrink Soft, LevelShrink Semi Soft, SUREShrink, BayesShrink thresholding methods at the variance of 10 are 23.82, 16.51, 23.25, 24.48, 23.25, 20.67, 23.42, 23.14 and 0.28, 0.28, 0.28, 0.29, 0.22, 0.25, 0.16 respectively which shows that the PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods, and so on. Thus, it can be stated that the performance of LevelShrink Hard thresholding method is better on most of tests.

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Badgainya, Shruti, Prof Pankaj Sahu, and Prof Vipul Awasthi. "Image Denoising for AWGN Corrupted Image Using OWT and Thresholding." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 220–26. http://dx.doi.org/10.31142/ijtsrd18338.

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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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2

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.
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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3

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 manual choice. Such a recognition system, including a learning framework for selecting optimal thresholding algorithms and filters, was developed and evaluated. It was established that an automatic selection will ensure a high recognition rate when applied to a set of arbitrary invoice images by successfully adapting and avoiding the methods that yield poor recognition rates.

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4

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 preprocessing approaches to separate text from its background have been researched lately. In our study, we assess the improvement reached by two of these preprocessing methods called k-means and Otsu by comparing their results from an OCR algorithm. The study showed that the preprocessing made some improvement on special occasions, but overall gained worse accuracy compared to the unaltered images.
Dagens optisk teckeninläsnings (OCR) algoritmer är kapabla av att extrahera text från bilder inom fördefinierade förhållanden. De moderna metoderna har uppnått en hög träffsäkerhet för maskinskriven text med minimala förvrängningar, men bilder tagna i en naturlig scen är fortfarande svåra att hantera. De senaste åren har ett stort intresse för att förbättra tecken igenkännings algoritmerna uppstått, eftersom fler kraftfulla och handhållna enheter används. Det huvudsakliga problemet när det kommer till igenkänning i naturliga bilder är olika förvrängningar som infallande ljus, textens textur och komplicerade bakgrunder. Olika metoder för förbehandling och därmed separation av texten och dess bakgrund har studerats under den senaste tiden. I våran studie bedömer vi förbättringen som uppnås vid förbehandlingen med två metoder som kallas för k-means och Otsu genom att jämföra svaren från en OCR algoritm. Studien visar att Otsu och k-means kan förbättra träffsäkerheten i vissa förhållanden men generellt sett ger det ett sämre resultat än de oförändrade bilderna.

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5

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 the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.

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6

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 the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.

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7

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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8

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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9

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 from the background. This ultimately leads to just concentrating on the object which in the present context is the driver.

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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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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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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---. Bradford, 1987.

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Book chapters on the topic "Image thresholding"

1

Mitchell, H. B. "Image Thresholding." In Image Fusion, 155–61. Berlin, Heidelberg: 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, 121–32. Cham: 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, 5–50. London: 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, 207–13. Boston, MA: 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, 111–21. Berkeley, CA: 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, 65–80. Berkeley, CA: 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, 169–88. Berkeley, CA: 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, 191–226. Cham: 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, 101–23. Cham: 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, 129–52. Berlin, Heidelberg: 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"

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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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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 thresholding techniques are examined for the case of wavy flow regime. These algorithms are used to estimate the wave shape and mix region between liquid and gas. In general, the adaptive algorithms are able to compensate for non-uniform illumination and they are able to estimate wave shape and mix region correctly. The execution time for the adaptive techniques is higher than global thresholding technique, but with the availability of new powerful PCs, it will become a minor issue.

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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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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"

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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. Fort Belvoir, VA: Defense Technical Information Center, January 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 pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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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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Abstract:

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 pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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