Fuzzy bit-plane-dependence image segmentation

Choy, Siu-Kai; Yuen, Fei Lung; Yu, Carisa Kwok Wai

doi:10.1016/j.sigpro.2018.08.010

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…According to [ 5 ], one of the main advantages of using the GA (genetic algorithm) for segmentation is its capacity to select a few parameters, such as the size of the inspection window or some heuristic edges, or the ideal number of regions of a division result. In the paper by [ 6 ], an algorithm was presented that integrates adjacent sequence and borderline alteration for figure division applications, as well as the bitplane possibility representation based on agglomerative fuzzy computation. Moreover, other encryption schemes, such as four-picture encryption, encryption employing gesture broadcast, and encryption using original image uniqueness, are provided in [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators

Premkumar¹,

Mahdal

Elangovan

2022

Sensors

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Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image’s pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.

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Section: Introductionmentioning

confidence: 99%

An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators

Premkumar¹,

Mahdal

Elangovan

2022

Sensors

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“…Image segmentation is beneficial in the detection of embryonic egg regions. Image segmentation is part of image processing, aiming to divide the image into several parts [45], [46]. Image segmentation can be done using grayscale or color images [47].…”

Section: Introductionmentioning

confidence: 99%

Chicken Egg Detection Based-on Image Processing Concept: A Review

Saifullah

Mehriddinovich

Tolentino

2021

Comput. Inf. Process. Lett.

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The concept of image processing has been implemented and developed in various fields, including the poultry industry. The focus in development is on egg detection. Detection is not only in the concept of object detection but also in other things such as weight prediction, egg physical characteristics, to embryo detection. This staged process starts from the image acquisition process, preprocessing, segmentation up to identifying or detecting eggs. This article provides details about the concept of image processing in detecting chicken eggs based on a review of previous studies. The studies discussed the basic concepts of image processing in detecting chicken eggs and their technical application. Based on image processing’s basic concept, there are four main parts: image acquisition, preprocessing, segmentation, and identification or classification. The acquisition process is carried out with a variety of tools that can capture images to be processed. The result of the acquisition is preprocessed by one or more methods that can improve image quality. After that, the image segmentation process is used to determine the object to be detected. Image segmentation can be used as a reference for objects processed by feature extraction. The feature extraction aims to provide certain fertile (embryonic) characteristics and unfertile (non-embryonic) egg images. The identification process is precise which objects are detected and not. The concept of segmentation and identification/classification can be implemented in computer-based applied applications. Besides, these methods are still developing and improving their accuracy and implementation in the poultry industry.

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“…Some prominent issues of digital images like poor contrast, uneven intensity distribution, poor illumination, ambiguous boundaries, etc. make the problem difficult and hard to analyze [7] , [8] , [9] , [10] . Therefore, the crisp method may not be suitable always to segment such types of images [8] .…”

Section: Introductionmentioning

confidence: 99%

“…make the problem difficult and hard to analyze [7] , [8] , [9] , [10] . Therefore, the crisp method may not be suitable always to segment such types of images [8] . The theory of the fuzzy sets can help in this issue [11] .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

Chakraborty

Mali

2020

Applied Soft Computing

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In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method. The EMO method is extended, modified, and combined with the modified type 2 fuzzy C-Means algorithm to improve its efficiency especially for biomedical image segmentation. The proposed FEMO method uses fuzzy membership and the electromagnetism-like optimization method to locate the optimal positions for the cluster centers. The proposed FEMO approach does not have any dependency on the initial selection of the cluster centers. Moreover, this method is suitable for the biomedical images of different modalities. This method is compared with some standard metaheuristics and evolutionary methods (e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Electromagnetism-like optimization (EMO), Ant Colony Optimization (ACO), etc.) based image segmentation approaches. Four different indices Davies–Bouldin, Xie–Beni, Dunn and index are used for the comparison and evaluation purpose. For the GA, PSO, ACO, EMO and the proposed FEMO approach, the optimal average value of the Davies–Bouldin index is 1.833578359 (8 clusters), 1.669359475 (3 clusters), 1.623119284 (3 clusters), 1.647743907 (4 clusters) and 1.456889343 (3 clusters) respectively. It shows that the proposed approach can efficiently determine the optimal clusters. Moreover, the results of the other quantitative indices are quite promising for the proposed approach compared to the other approaches The detailed comparison is performed in both qualitative and quantitative manner and it is found that the proposed method outperforms some of the existing methods concerning some standard evaluation parameters.

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Fuzzy bit-plane-dependence image segmentation

Cited by 17 publications

References 30 publications

An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators

An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators

Chicken Egg Detection Based-on Image Processing Concept: A Review

Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

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