A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization

Bhandari, Ashish Kumar; Rahul, Kusuma

doi:10.1016/j.asoc.2019.105515

Cited by 29 publications

(4 citation statements)

References 40 publications

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“…This is highly helpful for human visual performance. For example, fuzzy‐GOA 31 contrasted with the proposed model (database 2) while the deviations of SD, EQ, MI, and FF were 43.78%, 0.17%, 3.42%, and 1.31%. The values of the three metrics were higher than others inferring the fact that the proposed strategy acquired a superior fusion result.…”

Section: Implementation Resultsmentioning

confidence: 96%

“…Thresholding‐based segmentation needs exactness in partitioning the questionable images because of their intricate qualities, vulnerabilities, and innate fuzziness, as opined by Bhandari et al 31 The determination of the metaheuristic Grasshopper optimization algorithm (GOA) lessens this issue by choosing the optimal threshold esteems. Along these lines, to expand the nature of the sectioned image, a straightforward and compelling multilevel thresholding strategy was exploited through leveraging the idea of fusion depending on the local contrast.…”

Section: Review Of Literaturementioning

confidence: 99%

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A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

Velmurugan

Sivakumar

Sampson

2020

Int J Imaging Syst Tech

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Multi‐modality medical image fusion (MMIF) procedures have been generally utilized in different clinical applications. MMIF can furnish an image with anatomical as well as physiological data for specialists that could advance the diagnostic procedures. Various models were proposed earlier related to MMIF though there is a need still exists to enhance the efficiency of the previous techniques. In this research, the authors proposed a novel fusion model based on optimal thresholding with deep learning concepts. An enhanced monarch butterfly optimization (EMBO) is utilized to decide the optimal threshold of fusion rules in shearlet transform. Then, low and high‐frequency sub‐bands were fused on the basis of feature maps and were given by the extraction part of the deep learning method. Here, restricted Boltzmann machine (RBM) was utilized to conduct the MMIF procedure. A benchmark dataset was utilized for training and testing purposes. The investigations were conducted utilizing a set of generally‐utilized pre‐enrolled CT and MR images that are publicly accessible. From the usage of fused low and high level frequency groups, the fused image can be attained. The simulation performance results were attained and the proposed model was proved to offer effective performance in terms of SD, edge quality (EQ), mutual information (MI), fusion factor (FF), entropy, correlation factor (CF), and spatial frequency (SF) with respective values being 97.78, 0.96, 5.71, 6.53, 7.43, 0.97, and 25.78 over the compared methods.

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Section: Implementation Resultsmentioning

confidence: 96%

Section: Review Of Literaturementioning

confidence: 99%

A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

Velmurugan

Sivakumar

Sampson

2020

Int J Imaging Syst Tech

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“…It is first introduced by Saremi et al in [48]. It has been shown to be effective in solving various optimization problems, including medical image segmentation [49,50], image enhancement [51], image fusion and feature selection [52,53]. Its simplicity, efficiency, and robustness make it a popular optimization technique.…”

Section: E Search Optimization Algorithm (Goa)mentioning

confidence: 99%

MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification

Rampavan

Ijjina

2023

Preprint

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Object re-IDentification (reID) is the process of matching the same object across multiple cameras. An approach for object reID is crucial for identifying objects like people, cars, motorcycles etc., in video surveillance systems. This is an active area of research in both industry and academia due to the ever-growing population and need for smart surveillance, public safety and traffic management. This work proposes an approach for automatically designing a deep convolutional neural network, named MNASreID, for motorcycle re-identification task. Most current reID methods use deep convolutional neural networks as the backbone that are manually designed, which does not have the optimum settings as the network complexity increases. MNASreID uses Neural Architecture Search (NAS) to search for the backbones of the Deep Neural Network (DNN) model. In addition, the designed search space includes parameters of the architecture and its hyperparameters to find the optimum architecture for the reID task. This work uses Grasshopper Optimization Algorithm (GOA) based NAS to find the optimal DNN model. The outcomes of experiments on two motorcycle datasets demonstrate that our method can automatically find the efficient DNN model for motorcycle reID.

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“…Image segmentation technique is a primary step in computer vision and pattern recognition for pre-processing and analyzing images in the fields of remote sensing, medicine, etc. [1][2][3][4][5]. This technique divides an image into several homogeneous regions or segments with similar characteristics according to features, color, texture, and contrast [6,7].…”

Section: Introductionmentioning

confidence: 99%

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Liu

Jia

et al. 2022

Mathematics

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Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

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A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization

Cited by 29 publications

References 40 publications

A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

A novel approach in multimodality medical image fusion using optimal shearlet and deep learning

MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

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