Splicing Image and Its Localization: A Survey

Wang, Jinwei; Li, Yangyang

doi:10.32604/jihpp.2019.07186

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Image thresholding is a main method in computer vision [1,2], which is widely used in pattern recognition, medical diagnosis, target detection, damage detection, agricultural pest recognition and other fields [3][4][5][6]. The goal of this method is to subdivide an image into multiple complementary and non-coincident pixel groups based on a specific set of thresholds, so as to extract the region ofinterest or feature information from the original image [7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

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In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA. By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed. In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence. Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness. The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Sometimes in some image processing scenarios, we usually need to identify [11], classify [12] and locate the image [13] which is the process of identifying and searching small images within a large image. Nowadays, quantum computing is applied in image localization processing.…”

Section: Introductionmentioning

confidence: 99%

An Image Localization System Based on Single Photon

Wu¹,

Li²,

Zhu³

et al. 2022

Computers, Materials &Amp; Continua

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As an essential part of artificial intelligence, many works focus on image processing which is the branch of computer vision. Nevertheless, image localization faces complex challenges in image processing with image data increases. At the same time, quantum computing has the unique advantages of improving computing power and reducing energy consumption. So, combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms. At present, many quantum image localization algorithms have been proposed, and their efficiency is theoretically higher than the corresponding classical algorithms. But, in quantum computing experiments, quantum gates in quantum computing hardware need to work at very low temperatures, which brings great challenges to experiments. This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification. This scheme realizes the operation of the mixed national institute of standards and technology database (MNIST) quantum image localization by a learned transformation for non-noise condition, noisy condition, and environmental attack condition, respectively. Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization, the advantage of this method is that it does not deliberately require low temperature and entanglement resources, and it improves the lower bound of the localization success rate. This method paves a way to study quantum computer vision.

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A comprehensive survey on image authentication for tamper detection with localization

Chennamma

Madhushree

2022

Multimed Tools Appl

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Splicing Image and Its Localization: A Survey

Cited by 7 publications

References 27 publications

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

An Image Localization System Based on Single Photon

A comprehensive survey on image authentication for tamper detection with localization

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