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Relevant bibliographies by topics / Image thresholding;fuzzy partition;entropy maximization

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Journal articles
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Conference papers

Academic literature on the topic 'Image thresholding;fuzzy partition;entropy maximization'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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Journal articles on the topic "Image thresholding;fuzzy partition;entropy maximization"

1

Assas, Ouarda. "Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding." International Journal of Applied Evolutionary Computation 6, no. 3 (2015): 33–48. http://dx.doi.org/10.4018/ijaec.2015070103.

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Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset

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Xia, Dong-xue, Chun-gui Li, and Shu-hong Yang. "Fast Threshold Selection Algorithm of Infrared Human Images Based on Two-Dimensional Fuzzy Tsallis Entropy." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/308164.

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Infrared images are fuzzy and noisy by nature; thus the segmentation of human targets in infrared images is a challenging task. In this paper, a fast thresholding method of infrared human images based on two-dimensional fuzzy Tsallis entropy is introduced. First, to address the fuzziness of infrared image, the fuzzy Tsallis entropy of objects and that of background are defined, respectively, according to probability partition principle. Next, this newly defined entropy is extended to two dimensions to make good use of spatial information to deal with the noise in infrared images, and correspon

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Benabdelkader, Souad, and Mohammed Boulemden. "Recursive algorithm based on fuzzy 2-partition entropy for 2-level image thresholding." Pattern Recognition 38, no. 8 (2005): 1289–94. http://dx.doi.org/10.1016/j.patcog.2004.03.018.

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Roy, Apurba, and Santi P. Maity. "On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 01 (2019): 1950003. http://dx.doi.org/10.1142/s0219691319500036.

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In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platfor

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Kubicek, Jan, Alice Varysova, Martin Cerny, et al. "Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images." Sensors 22, no. 17 (2022): 6335. http://dx.doi.org/10.3390/s22176335.

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The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of thes

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"Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (2019): 4272–79. http://dx.doi.org/10.35940/ijitee.a4847.119119.

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Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS

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"Brain Tumor Segmentation using Multi Level Thresholding using Fuzzy Entropy." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 2641–43. http://dx.doi.org/10.35940/ijrte.e5944.018520.

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In image processing field, image processing technique is used to distinguish the object from its image scene at pixel level. The image segmentation process is the significant task in the processing of image field as well as in image analysis. The most difficult task in the image analysis field is the automatic separation of object from its background. To alleviate this problem several image segmentation process is introduced are gray level thresholding, edge detection method, interactive pixel classification method, neural network approach and segmentation based on fuzzy approach This chapter

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Dissertations / Theses on the topic "Image thresholding;fuzzy partition;entropy maximization"

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 t

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

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Abstract:

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

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Conference papers on the topic "Image thresholding;fuzzy partition;entropy maximization"

1

Ouarda, Assas. "Image thresholding using type-2 fuzzy c-partition entropy and particle swarm optimization algorithm." In 2015 International Conference on Computer Vision and Image Analysis Applications (ICCVIA). IEEE, 2015. http://dx.doi.org/10.1109/iccvia.2015.7351880.

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