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Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function

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neural COMPUTING & APPLICATIONS 2023年第1期35卷 855-886页

作者： Hosny, Khalid M. Khalid, Asmaa M. Hamza, Hanaa M. Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia

image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.

关键词： image segmentation Optimization Thresholding Metaheuristic Satellite

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WaveTexNeT: Ensemble Based Wavelet-Xception Deep neural Network Architecture for Color Texture Classification

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TRAITEMENT DU signal 2022年第6期39卷 1917-1927页

作者： Simon, Philomina Vijayasundaram, Uma Univ Kerala Thiruvananthapuram Dept Comp Sci Thiruvananthapuram 695581 Kerala India Pondicherry Univ Kalapet Dept Comp Sci Pondicherry 605014 India

Recognizing real visual textures in the nature have been a challenging task since they are complex and stochastic. In spite of several decades of research, classifying the real world color textures are still challenging because of the intricate nature of the textures and the lack of substantial improvement of accuracy in benchmark datasets. Deep Learning techniques have found to be effective in identifying and classifying the texture patterns to a larger extent, but it could not capture spectral information and achieve excellent results for natural images. In this paper, we propose a deep convolutional neural network architecture, WaveTexNeT that combines Wavelet convolutional neural networks (WaveletCNN) and Xception model with luminance information for classifying real-world natural textures. Spectral and spatial features are extracted from WaveletCNN and Xception model. The highlight of the work is the utilization of spectral and spatial information along with luminance for texture classification. A color space image data augmentation technique is proposed that use luminance images from YIQ model for color texture classification. This work also throws light into the significance of luminance information for texture classification. Experimental analysis of the work reports that WaveTexNeT captures better feature representations and outperforms the accuracy obtained using the state-of-the-art methods. WaveTexNeT obtained an accuracy of 90.34% and 95.01% for the describable and material perception texture datasets DTD and FMD respectively.

关键词： luminance YIQ color model texture convolution neural network deep architecture wavelet CNN Xception color space image augmentation

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Improving T2I-Adapter via Integration of Visual and Textual Conditions with Attention Mechanism 31

Improving T2I-Adapter via Integration of Visual and Textual ...

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31st IEEE International Conference on image processing Challenges and Workshops, ICIPCW 2024

作者： Zhu, Zheng-An Fan, Xin-Yun Chiang, Chen-Kuo National Chung Cheng University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798331515942

Recently, relying solely on T2I has gradually proven insufficient to meet the demands for image generation. As a result, people have started exploring more controllable image-generation methods based on Diffusion technology. In addition to solely using textual descriptions to generate images, extra control conditions such as Sketch, Segmentation, and Canny are being considered. However, existing T2I control adapters mostly lack a comprehensive integration between textual and image conditions. Additionally, in multi-layer neural networks, certain features may be lost information. Therefore, this paper aims to address the aforementioned issues and propose a new architecture systematically. We utilize Cross Attention to merge textual descriptions and image features and introduce Coordinate Attention at each feature output to enhance the overall feature representation. Experimental results demonstrate that compares to state-of-The-Art methods, this approach achieved superior evaluation metrics and exhibited visual effects more in line with human assessment. © 2024 IEEE.

关键词： Multilayer neural networks

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Automated design of Convolutional neural Network architecture using Gray Wolf Optimization for plant seedlings classification 8

Automated design of Convolutional Neural Network architectur...

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8th IEEE International Conference on image and signal processing and their Applications, ISPA 2024

作者： Badis, Lamis Aliouat, Wahiba Bouchiba, Kenza Faculty of Sciences and Applied Sciences Department of Computer Sciences University of Bouira Algeria Faculty of Sciences and Applied Sciences LIM Laboratory Department of Computer Sciences University of Bouira Algeria

ISBN: (纸本)9798350309249

Convolutional neural Networks (CNNs) have gained significant popularity in image classification tasks, yet achieving their optimal design remains a challenge due to the vast array of possible layer configurations and associated hyperparameters. Selecting the best CNN model for a given task often demands considerable time investment in training numerous models. To address this issue, we propose an automated method for CNN architecture design, utilizing pretrained models like the Backbone and employing Gray Wolf Optimization. This approach automatically generates and evaluates candidate CNN architectures for classifying plant seedlings. Our objective is to distinguish between weed and crop seedlings. Additionally, we introduce a gray wolf representation to encode CNN architectures and their hyperparameters. Our method combines the strengths of transfer learning from pre-trained models to extract meaningful image features with the optimization capabilities of the Gray Wolf Optimization (GWO) algorithm. By leveraging these techniques, our method achieves exceptional accuracy, surpassing state-of-the-art methods with a validation accuracy of up to 97.83%. This innovative approach offers a transformative tool for enhancing the accuracy of CNN models, tailored specifically to the dataset at hand. © 2024 IEEE.

关键词： Convolutional neural networks

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SINR: Deconvolving Circular SAS images Using Implicit neural Representations

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第2期17卷 458-472页

作者： Reed, Albert Blanford, Thomas Brown, Daniel C. Jayasuriya, Suren Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Penn State Univ Appl Res Lab State Coll PA 16801 USA Penn State Univ Appl Res Lab State Coll PA 16801 USA Penn State Univ Grad Program Acoust State Coll PA 16801 USA Arizona State Univ Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

Circular synthetic aperture sonars (CSAS) capture multiple observations of a scene to reconstruct high-resolution images. We can characterize resolution by modelingCSAS imaging as the convolution between a scene's underlying point scattering distribution and a system-dependent point spread function (PSF). The PSF is a function of the system bandwidth and determines a fixed degree of blurring on reconstructed imagery. In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution. However, deconvolution is an ill-posed inverse problem and sensitive to noise. We propose an optimization method that leverages an implicit neural representation (INR) to deconvolve CSAS images. We highlight the performance of our SAS INR pipeline, which we call SINR, by implementing and comparing to existing deconvolution methods. Additionally, prior SAS deconvolution methods assume a spatially-invariant PSF, which we demonstrate yields subpar performance in practice. We provide theory and methods to account for a spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to achieve superior deconvolution performance on simulated and real acoustic SAS data.

关键词： Synthetic aperture sonar Deconvolution image reconstruction Imaging signal to noise ratio Interference Pipelines Deconvolution inverse problems neural networks optimization synthetic aperture sonar

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MSV-RGNN: MULTISCALE VOXEL GRAPH neural NETWORK FOR 3D OBJECT DETECTION 30

MSV-RGNN: MULTISCALE VOXEL GRAPH NEURAL NETWORK FOR 3D OBJEC...

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30th IEEE International Conference on image processing (ICIP)

作者： Lee, Wonjoon Woo, Sungmin Kim, Donghyeong Lee, Sangyoun Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781728198354

This paper proposes a two-stage 3D object detection framework, multiscale voxel graph neural network (MSV-RGNN) which aims to fully exploit multiple scale graph features by establishing global and local relationships between voxel features at different 3D convolutional neural network (CNN) layers. In contrast to conventional graph-based methods, our proposed multiscale-voxel-graph region-of-interest (RoI) pooling module constructs graphs across diverse voxel resolutions to obtain geometric structure information on voxel features. Initially, our multiscale-voxel-graph RoI pooling module sample voxel center points with voxel-wise feature vectors and 3D region proposals from backbone network. Subsequently, graphs are constructed at different scales and graph features are aggregated for second-stage refinement. The experimental results demonstrate the potential of using multiscale graphs across different voxel resolutions for 3D object detection, achieving decent experimental results with state-of-the-art methods.

关键词： multiscale graph 3D object detection voxel

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Detectify : image Tampering Detection using Error Level Analysis (ELA) and Convolutional neural Network (CNN)

Detectify : Image Tampering Detection using Error Level Anal...

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2024 International Conference on signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Geethanjali, T.M. Darshan, T.S. Surya, K. Rahul, H.U. Sheety, Ipshika N PES College of Engineering Vishvesvaraya Technological University Dept. Information Science and Engineering Mandya India

ISBN: (纸本)9798331540685

In the evolving digital landscape, the proliferation of manipulated images poses a significant challenge to the authenticity and integrity of visual content. This project investigates cutting-edge image manipulation detection techniques, employing a combination of Error Level Analysis (ELA) and Convolutional neural Networks (CNN) for robust prediction. Focusing on the widely-used CASIA V2.0 dataset, the study provides a comprehensive evaluation of image manipulation methods. Error Level Analysis is utilized as a forensic tool to identify alterations in the compression levels of manipulated images. By scrutinizing variations in error levels, the project aims to enhance the detection accuracy of manipulated regions within visual content. The CNN model is meticulously crafted and trained using preprocessed ELA images to acquire nuanced features essential for discerning tampering- induced alterations. The proposed hybrid approach, integrating ELA and CNN, establishes a robust framework for detecting image manipulation that is adaptable and efficient. Through the meticulous examination of the CASIA V2.0 dataset, this project contributes to ongoing efforts in combating digital image manipulation. This study serves as a valuable resource for forensic analysts, researchers, and practitioners working towards ensuring the veracity of digital images, offering a nuanced understanding of image manipulation techniques in the contemporary digital era. © 2024 IEEE.

关键词： Convolutional neural networks

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ULTRASOUND signal processing: FROM MODELS TO DEEP LEARNING

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ULTRASOUND IN MEDICINE AND BIOLOGY 2023年第3期49卷 677-698页

作者： Luijten, Ben Chennakeshava, Nishith Eldar, Yonina C. Mischi, Massimo VAN Sloun, Ruud J. G. Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Weizmann Inst Sci Fac Math & Comp Sci Rehovot Israel Philips Res Eindhoven Netherlands Eindhoven Univ Technol Dept Electl Engn NL-5612 AZ Eindhoven Netherlands

ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms have been derived from physical principles. These algorithms rely on assumptions and approximations of the underlying measurement model, lim-iting image quality in settings where these assumptions break down. Conversely, more sophisticated solutions based on statistical modeling or careful parameter tuning or derived from increased model complexity can be sensitive to different environments. Recently, deep learning-based methods, which are optimized in a data-driven fashion, have gained popularity. These model-agnostic techniques often rely on generic model structures and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning and exploiting domain knowledge. These model-based solutions yield high robustness and require fewer parameters and training data than conventional neural networks. In this work we provide an overview of these techniques from the recent literature and discuss a wide variety of ultra-sound applications. We aim to inspire the reader to perform further research in this area and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on model-based deep learning techniques for medical ultrasound. (E-mail: ***@***) (c) 2022 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Ultrasound Deep learning Probabilistic modeling

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Auto-Metric Graph neural Network optimized with Capuchin search optimization algorithm for coinciding diabetic retinopathy and diabetic Macular edema grading

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BIOMEDICAL signal processing AND CONTROL 2023年第Part2期80卷 000-000页

作者： Chandran, J. Jasper Gnana Jabez, J. Srinivasulu, Senduru Francis Xavier Engn Coll Dept Elect & Elect Engn Tirunelveli India Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India

Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major eternal blindness in aged people. In this manuscript, Auto-Metric Graph neural Network (AGNN) optimized with Capuchin search optimization al-gorithm is proposed for coinciding DR and DME grading (AGNN-CSO-DR-DME). The novelty of this work is to identify the Diabetic retinopathy and diabetic macular edema grading at initial stage with higher accuracy by decreasing the error rate and computation time. Initially, input image is taken from two public benchmark datasets that is ISBI 2018 imbalanced diabetic retinopathy grading dataset and Messidor dataset. Then, the input fundus image is pre-processed by APPDRC filtering method removes noise in input images. Also, the pre-processed images are given to the Gray level co-occurrence matrix (GLCM) window adaptive algorithm based feature extraction method. The extracted features of the DR and DME are fed to AGNN for classifying the grading of both DR and DME diseases. Generally, AGNN not reveal any adoption of optimization methods compute optimum parameters for assuring correct grading of both DR and DME diseases. Thus, CSOA is used for opti-mizing the AGNN weight parameters. The proposed method is carried out in python, its efficiency is assessed under performances metrics, such as f-measure, execution time and accuracy. The proposed method attains higher accuracy in ISBI 2018 IDRiD dataset 99.57 %, 97.28 %, and 96.34 %, compared with existing methods, like CANet-DR-DME, HDLCNN-MGMO-DR-DME, ANN-DR-DME and 91.17 %, 96.52 % and 97.36 %higher ac-curacy in Messidor dataset compared with existing methods, like CANet-DR-DME, TCNN-DR-DME, and 2-D-FBSE-FAWT-DR-DME.

关键词： Auto-Metric Graph neural Network Capuchin search optimization Diabetic Retinopathy Diabetic Macular Edema Grading Messidor dataset

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HDTC: HYBRID MODEL OF DUAL-TRANSFORMER AND CONVOLUTIONAL neural NETWORK FROM RGB-D FOR DETECTION OF LETTUCE GROWTH TRAITS 30

HDTC: HYBRID MODEL OF DUAL-TRANSFORMER AND CONVOLUTIONAL NEU...

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30th IEEE International Conference on image processing (ICIP)

作者： Wu, Zhengxian Liu, Xingpeng Xue, YiMing Wen, Juan Peng, Wanli China Agr Univ Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781728198354

Automatic detection of lettuce growth traits is of great significance in modern greenhouse cultivation. Existing methods mainly focus on capturing coarse representations from RGB or RGB-D images with learnable convolutional neural networks. However, due to the significant appearance-varying discrepancies at different growth stages, coarse representations and inefficient depth fusion strategies limit the performance of automatic detection of lettuce growth traits. To alleviate the above problem, this paper proposes a novel detection method for lettuce growth traits based on transformer and convolutional neural network. In this method, we design a dual-transformer module and a residual module to effectively extract multi-scale representations and depth representations from appearance-varying lettuce images. In addition, a feature coupling bridge is proposed to fuse the multi-scale representations and depth representations. The experimental results show that our method outperforms the state-of-the-art methods.

关键词： Growth trait detection dual-transformer feature coupling bridge

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