Relevant bibliographies by topics / FUZZY THRESHOLDING AND ANR

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

Academic literature on the topic 'FUZZY THRESHOLDING AND ANR'

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Published: 11 September 2023

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Journal articles on the topic "FUZZY THRESHOLDING AND ANR"

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Nobuhara, Hajime, and Kaoru Hirota. "A Fuzzification of Morphological Wavelets Based on Fuzzy Relational Calculus and its Application to Image Compression/Reconstruction." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 373–78. http://dx.doi.org/10.20965/jaciii.2004.p0373.

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A new style of fuzzy wavelets is proposed by the fuzzification of morphological wavelets. Due to the correspondence of the morphological wavelets operations and fuzzy relational ones, wavelets analysis/synthesis schemes can be formulated based on fuzzy relational calculus. To enable efficient image compression/reconstruction, the concept of the alpha-band which is an alpha-cut generalization, is also proposed for thresholding wavelets. In an image compression/reconstruction experiment using test images extracted from the Standard Image DataBAse (SIDBA), it is confirmed that the root mean square error (RMSE) of the proposed soft thresholding is decreased to 87.3% of conventional hard thresholding, when the original image is "Lenna."
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Pal, Sankar K., and Ambarish Dasgupta. "Spectral fuzzy sets and soft thresholding." Information Sciences 65, no. 1-2 (November 1, 1992): 65–97. http://dx.doi.org/10.1016/0020-0255(92)90078-m.

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Bhandari, Dinabandhu, Nikhil R. Pal, and D. Dutta Majumder. "Fuzzy divergence, probability measure of fuzzy events and image thresholding." Pattern Recognition Letters 13, no. 12 (December 1992): 857–67. http://dx.doi.org/10.1016/0167-8655(92)90085-e.

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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 (March 29, 2022): 45–50. http://dx.doi.org/10.47392/irjash.2022.011.

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Tizhoosh, Hamid R. "Image thresholding using type II fuzzy sets." Pattern Recognition 38, no. 12 (December 2005): 2363–72. http://dx.doi.org/10.1016/j.patcog.2005.02.014.

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Bogiatzis, Athanasios, and Basil Papadopoulos. "Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures." Symmetry 11, no. 2 (February 22, 2019): 286. http://dx.doi.org/10.3390/sym11020286.

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Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.
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CHENG, H. D., YANHUI GUO, and YINGTAO ZHANG. "A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY." New Mathematics and Natural Computation 07, no. 01 (March 2011): 105–33. http://dx.doi.org/10.1142/s1793005711001834.

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Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.
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Shark, L. K., and C. Yu. "Denoising by optimal fuzzy thresholding in wavelet domain." Electronics Letters 36, no. 6 (2000): 581. http://dx.doi.org/10.1049/el:20000451.

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Li, Linguo, Xuwen Huang, Shunqiang Qian, Zhangfei Li, Shujing Li, and Romany F. Mansour. "Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding." Computers, Materials & Continua 72, no. 2 (2022): 3073–90. http://dx.doi.org/10.32604/cmc.2022.026625.

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Barrenechea, E., H. Bustince, M. J. Campión, E. Induráin, and V. Knoblauch. "Topological interpretations of fuzzy subsets. A unified approach for fuzzy thresholding algorithms." Knowledge-Based Systems 54 (December 2013): 163–71. http://dx.doi.org/10.1016/j.knosys.2013.09.008.

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Dissertations / Theses on the topic "FUZZY THRESHOLDING AND ANR"

1

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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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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Almotiri, Jasem. "A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding." Thesis, University of Bridgeport, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10975223.

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Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images.

The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures.

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Al-Azawi, Mohammad Ali Naji Said. "A new approach to automatic saliency identification in images based on irregularity of regions." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11122.

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This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene.
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Čambalová, Kateřina. "Volné algebraické struktury a jejich využití pro segmentaci digitálního obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-231711.

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The thesis covers methods for image segmentation. Fuzzy segmentation is based on the thresholding method. This is generalized to accept multiple criteria. The whole process is mathematically based on the free algebra theory. Free distributive lattice is created from poset of elements based on image properties and the lattice members are represented by terms used by the threshoding. Possible segmentation results compose the equivalence classes distribution. The thesis also contains description of resulting algorithms and methods for their optimization. Also the method of area subtracting is introduced.
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SNEKHA. "GENETIC ALGORITHM BASED ECG SIGNAL DE-NOISING USING EEMD AND FUZZY THRESHOLDING." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15346.

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ElectroCardioGram (ECG) signal records electrical conduction activity of heart. These are very small signals in strength with narrow bandwidth of 0.05-120 Hz. Physicians especially cardiologist use these signals for diagnosis of the heart’s condition or heart diseases. ECG signal is contaminated with various artifacts such as Power Line Interference (PLI), Patient–electrode motion artifacts, Electrode-pop or contact noise, and Baseline Wandering and ElectroMyoGraphic (EMG) noise during acquisition. Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of such unwanted signals. So, de-noising of ECG signal is extremely important to prevent misinterpretation of patient’s cardiac activity. Various method are available for de-noising the ECG signal such as Hybrid technique, Empirical Mode Decomposition, Un-decimated Wavelet Transform, Hilbert-Hung Transform, Adaptive Filtering, FIR Filtering, Morphological Filtering, Noise Invalidation Techniques, Non- Local Means Technique and S-Transform etc. All these techniques have some limitations such as mode mixing problem, oscillation in the reconstructed signals, reduced amplitude of the ECG signal and problem of degeneracy etc. To overcome the above mentioned limitations, a new technique is proposed for denoising of ECG signal based on Genetic Algorithm and EEMD with the help of Fuzzy Thresholding. EEMD methods are used to decompose the electrocardiogram signal into true Intrinsic Mode Functions (IMFs).Then the IMFs which are ruled by noise are automatically determined using Fuzzy Thresholding and then filtered using Genetic Particle Algorithms to remove the noise. Use of Genetic Particle Filter mitigates the sample degeneracy of Particle Filter (PF).EEMD is used in this thesis instead of EMD because it solves the EMD mode mixing problem. EEMD represents a major improvement with great versatility and robustness in noisy ECG signal filtering.
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Tanjung, Guntur. "A study on image change detection methods for multiple images of the same scene acquired by a mobile camera." 2010. http://hdl.handle.net/2440/60533.

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Detecting regions of change while reducing unimportant changes in multiple outdoor images of the same scene containing fence wires (i.e., a chain-link mesh fence) acquired by a mobile camera from slightly different viewing positions, angles and at different times is a very difficult problem. Regions of change include appearing of new objects and/or disappearing of old objects behind fence wires, breaches in the integrity of fence wires and attached objects in front of fence wires. Unimportant changes are mainly caused by camera movement, considerable background clutter, illumination variation, tiny sizes of fence wires and non-uniform illumination that occurs across fence wires. There are several issues that arise from these kinds of multiple outdoor images. The issues are: (1) parallax (the apparent displacement of an object as seen from two different positions that are not on a line with the object) among objects in the scene, (2) changing in size of same objects as a result of camera movement in forward or backward direction, (3) background clutter of outdoor scenes, (4) thinness of fence wires and (5) significant illumination variation that occurs in outdoor scenes and across fence wires. In this dissertation, an automated change detection method is proposed for these kinds of multiple outdoor images. The change detection method is composed of two distinct modules, which are a module for detecting object presence and/or absence behind fence wires and another module for detecting breaches in the integrity of fence wires and/or attached objects in front of fence wires. The first module consist of five main steps: (1) automated image registration, (2) confidence map image production by the Zitnick and Kanade algorithm, (3) occlusion map image generation, (4) significant or unimportant changes decision by the first hybrid decision-making system and (5) false positives reduction by the template subtraction approach. The second module integrates: (1) the Sobel edge detector combined with an adaptive thresholding technique in extracting edges of fence wires, (2) an area-based measuring in separating small and big objects based on their average areas determined once in the calibration process and (3) the second hybrid decision-making system in classifying objects as significant or unimportant changes. Experimental results demonstrate that the change detection method can identify and indicate approximate locations and possible percentages of significant changes whilst reducing unimportant changes in these kinds of multiple outdoor images. The study has utilized occluded regions in a confidence map image produced by the Zitnick and Kanade algorithm as potential significant changes in the image change detection research. Moreover, the study proves that the use of the Sobel edge detector combined with an adaptive thresholding technique is applicable in extracting edges of outdoor fence wires. In the future, the method could be integrated into patrol robots in order to provide assistance to human guards in protecting outdoor perimeter security.
http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1522689
Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2010
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Tanjung, Guntur. "A study on image change detection methods for multiple images of the same scene acquired by a mobile camera." Thesis, 2010. http://hdl.handle.net/2440/60533.

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Detecting regions of change while reducing unimportant changes in multiple outdoor images of the same scene containing fence wires (i.e., a chain-link mesh fence) acquired by a mobile camera from slightly different viewing positions, angles and at different times is a very difficult problem. Regions of change include appearing of new objects and/or disappearing of old objects behind fence wires, breaches in the integrity of fence wires and attached objects in front of fence wires. Unimportant changes are mainly caused by camera movement, considerable background clutter, illumination variation, tiny sizes of fence wires and non-uniform illumination that occurs across fence wires. There are several issues that arise from these kinds of multiple outdoor images. The issues are: (1) parallax (the apparent displacement of an object as seen from two different positions that are not on a line with the object) among objects in the scene, (2) changing in size of same objects as a result of camera movement in forward or backward direction, (3) background clutter of outdoor scenes, (4) thinness of fence wires and (5) significant illumination variation that occurs in outdoor scenes and across fence wires. In this dissertation, an automated change detection method is proposed for these kinds of multiple outdoor images. The change detection method is composed of two distinct modules, which are a module for detecting object presence and/or absence behind fence wires and another module for detecting breaches in the integrity of fence wires and/or attached objects in front of fence wires. The first module consist of five main steps: (1) automated image registration, (2) confidence map image production by the Zitnick and Kanade algorithm, (3) occlusion map image generation, (4) significant or unimportant changes decision by the first hybrid decision-making system and (5) false positives reduction by the template subtraction approach. The second module integrates: (1) the Sobel edge detector combined with an adaptive thresholding technique in extracting edges of fence wires, (2) an area-based measuring in separating small and big objects based on their average areas determined once in the calibration process and (3) the second hybrid decision-making system in classifying objects as significant or unimportant changes. Experimental results demonstrate that the change detection method can identify and indicate approximate locations and possible percentages of significant changes whilst reducing unimportant changes in these kinds of multiple outdoor images. The study has utilized occluded regions in a confidence map image produced by the Zitnick and Kanade algorithm as potential significant changes in the image change detection research. Moreover, the study proves that the use of the Sobel edge detector combined with an adaptive thresholding technique is applicable in extracting edges of outdoor fence wires. In the future, the method could be integrated into patrol robots in order to provide assistance to human guards in protecting outdoor perimeter security.
Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2010
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Ensafi, Pegah. "Weighted Opposition-Based Fuzzy Thresholding." Thesis, 2011. http://hdl.handle.net/10012/5796.

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With the rapid growth of the digital imaging, image processing techniques are widely involved in many industrial and medical applications. Image thresholding plays an essential role in image processing and computer vision applications. It has a vast domain of usage. Areas such document image analysis, scene or map processing, satellite imaging and material inspection in quality control tasks are examples of applications that employ image thresholding or segmentation to extract useful information from images. Medical image processing is another area that has extensively used image thresholding to help the experts to better interpret digital images for a more accurate diagnosis or to plan treatment procedures. Opposition-based computing, on the other hand, is a recently introduced model that can be employed to improve the performance of existing techniques. In this thesis, the idea of oppositional thresholding is explored to introduce new and better thresholding techniques. A recent method, called Opposite Fuzzy Thresholding (OFT), has involved fuzzy sets with opposition idea, and based on some preliminary experiments seems to be reasonably successful in thresholding some medical images. In this thesis, a Weighted Opposite Fuzzy Thresholding method (WOFT) will be presented that produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using both synthetic and real world images. Experimental evaluations were conducted on two sets of synthetic and medical images to validate the robustness of the proposed method in improving the accuracy of the thresholding process when fuzzy and oppositional ideas are combined.
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Bai, Rong. "Wavelet Shrinkage Based Image Denoising using Soft Computing." Thesis, 2008. http://hdl.handle.net/10012/3876.

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Noise reduction is an open problem and has received considerable attention in the literature for several decades. Over the last two decades, wavelet based methods have been applied to the problem of noise reduction and have been shown to outperform the traditional Wiener filter, Median filter, and modified Lee filter in terms of root mean squared error (MSE), peak signal noise ratio (PSNR) and other evaluation methods. In this research, two approaches for the development of high performance algorithms for de-noising are proposed, both based on soft computing tools, such as fuzzy logic, neural networks, and genetic algorithms. First, an improved additive noise reduction method for digital grey scale nature images, which uses an interval type-2 fuzzy logic system to shrink wavelet coefficients, is proposed. This method is an extension of a recently published approach for additive noise reduction using a type-1 fuzzy logic system based wavelet shrinkage. Unlike the type-1 fuzzy logic system based wavelet shrinkage method, the proposed approach employs a thresholding filter to adjust the wavelet coefficients according to the linguistic uncertainty in neighborhood values, inter-scale dependencies and intra-scale correlations of wavelet coefficients at different resolutions by exploiting the interval type-2 fuzzy set theory. Experimental results show that the proposed approach can efficiently and rapidly remove additive noise from digital grey scale images. Objective analysis and visual observations show that the proposed approach outperforms current fuzzy non-wavelet methods and fuzzy wavelet based methods, and is comparable with some recent but more complex wavelet methods, such as Hidden Markov Model based additive noise de-noising method. The main differences between the proposed approach and other wavelet shrinkage based approaches and the main improvements of the proposed approach are also illustrated in this thesis. Second, another improved method of additive noise reduction is also proposed. The method is based on fusing the results of different filters using a Fuzzy Neural Network (FNN). The proposed method combines the advantages of these filters and has outstanding ability of smoothing out additive noise while preserving details of an image (e.g. edges and lines) effectively. A Genetic Algorithm (GA) is applied to choose the optimal parameters of the FNN. The experimental results show that the proposed method is powerful for removing noise from natural images, and the MSE of this approach is less, and the PSNR of is higher, than that of any individual filters which are used for fusion. Finally, the two proposed approaches are compared with each other from different point of views, such as objective analysis in terms of mean squared error(MSE), peak signal to noise ratio (PSNR), image quality index (IQI) based on quality assessment of distorted images, and Information Theoretic Criterion (ITC) based on a human vision model, computational cost, universality, and human observation. The results show that the proposed FNN based algorithm optimized by GA has the best performance among all testing approaches. Important considerations for these proposed approaches and future work are discussed.
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Book chapters on the topic "FUZZY THRESHOLDING AND ANR"

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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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Oh, Jun-Taek, and Wook-Hyun Kim. "EWFCM Algorithm and Region-Based Multi-level Thresholding." In Fuzzy Systems and Knowledge Discovery, 864–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_107.

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Li, Jianli, Bingbin Dai, Kai Xiao, and Aboul Ella Hassanien. "Density Based Fuzzy Thresholding for Image Segmentation." In Communications in Computer and Information Science, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35326-0_13.

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Roy, Sudipta, Nidul Sinha, and Asoke Kr Sen. "Fuzzy Soft Thresholding Based Hybrid Denoising Model." In Advances in Digital Image Processing and Information Technology, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24055-3_1.

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Kubicek, Jan, Marek Penhaker, Iveta Bryjova, and Martin Augustynek. "Classification Method for Macular Lesions Using Fuzzy Thresholding Method." In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, 239–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32703-7_48.

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Bruzzese, D., and U. Giani. "Automatic Multilevel Thresholding Based on a Fuzzy Entropy Measure." In Classification and Multivariate Analysis for Complex Data Structures, 125–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13312-1_12.

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Vlachos, Ioannis K., and George D. Sergiadis. "An Automated Image Thresholding Scheme for Highly Contrast-Degraded Images Based on a-Order Fuzzy Entropy." In Fuzzy Logic and Applications, 332–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/10983652_40.

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Moreno, Ginés, Jaime Penabad, José A. Riaza, and Germán Vidal. "Symbolic Execution and Thresholding for Efficiently Tuning Fuzzy Logic Programs." In Logic-Based Program Synthesis and Transformation, 131–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63139-4_8.

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Tao, Li-Li, Yu-Ying Zhou, Miao Ma, and Kai-Fang Yang. "Multilevel Thresholding Image Segmentation Based on Extension of Artificial Raindrop Algorithm." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 1472–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_159.

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Sarkar, S., S. Paul, R. Burman, S. Das, and S. S. Chaudhuri. "A Fuzzy Entropy Based Multi-Level Image Thresholding Using Differential Evolution." In Swarm, Evolutionary, and Memetic Computing, 386–95. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_34.

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Conference papers on the topic "FUZZY THRESHOLDING AND ANR"

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Wu, Weishu, Changxi Yang, Scott Campbell, and Pochi Yeh. "A Photorefractive Optical Fuzzy Logic Processor." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.otue10.

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Fuzzy logic 1 has potential application in fields such as pattern recognition and process control. Since Liu first introduced an optical fuzzy logic processor utilizing a lens-array-based multiple imaging system, 2 many other systems have also been proposed and demonstrated. Most of early implementations were based on the principle of shadow-casting, with spatially encoded patterns being superimposed on each other by use of either light source array 3 or lens-array. 2 To obtain correct output of the fuzzy logic maximization (or minimization) operations, thresholding devices were needed in some systems. These thresholding devices, as well as the complex encoding patterns, make the systems complicated. Other systems utilized a complex encoding scheme which resulted in an output pattern different from the input patterns. Thus, the encoding scheme proposed for two-input fuzzy logic operations was difficult to be extended to multiple-input operations. 3,4, In this paper, we propose and demonstrate a novel optical fuzzy logic processor based on four-wave mixing in photorefractive crystals. Specifically, the recording of light-induced gratings is utilized to achieve minimization operations, while the readout of degenerated gratings is utilized to achieve maximization operations. Our system has several advantages including simple data encoding scheme, full parallelism, high speed, high accuracy, and simple architecture (no thresholding devices).
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Thakkar, Mehul, and Hitesh Shah. "Edge detection techniques using fuzzy thresholding." In 2011 World Congress on Information and Communication Technologies (WICT). IEEE, 2011. http://dx.doi.org/10.1109/wict.2011.6141263.

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Ichihashi, Hidetomo, Toshiro Ogita, Katsuhiro Honda, and Akira Notsu. "Improvement by sorting and thresholding in PCA based nearest neighbor search." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250773.

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Othman, A., H. R. Tizhoosh, and F. Khalvati. "Self-Configuring and Evolving Fuzzy Image Thresholding." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.130.

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Dash, Ajaya Kumar, and Banshidhar Majhi. "Image segmentation using fuzzy based histogram thresholding." In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091443.

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Rajesh, R., N. Senthilkumaran, J. Satheeshkumar, B. Shanmuga Priya, C. Thilagavathy, and K. Priya. "On the type-1 and type-2 fuzziness measures for thresholding MRI brain images." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007444.

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Cheng, Heng-Da, and Yen-Hung Chen. "Novel fuzzy entropy approach to thresholding and enhancement." In Medical Imaging '98, edited by Kenneth M. Hanson. SPIE, 1998. http://dx.doi.org/10.1117/12.310977.

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Thakkar, Mehul, and Hitesh Shah. "Automatic thresholding in edge detection using fuzzy approach." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705868.

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Amaral, T. G., M. M. Crisostomo, and A. Traca de Almeida. "Image thresholding by minimisation of fuzzy compactness and linear index of fuzziness." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793111.

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Wu, Jianhua, Zhaoyu Pian, Li Guo, Kun Wang, and Liqun Gao. "Medical Image Thresholding Algorithm Based on Fuzzy sets Theory." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318543.

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