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Style Transfer: From Stitching to neural networks 5

Style Transfer: From Stitching to Neural Networks

引用

5th International conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Xu, Xinhe Wang, Zhuoer Zhang, Yihan Liu, Yizhou Wang, Zhaoyue Xu, Zhihao Zhao, Muhan Luo, Huaiying University of Illinois Urbana-Champaign Urbana United States Washington University in St. Louis St. Louis United States Georgia Institute of Technology Atlanta United States Carnegie Mellon University Pittsburgh United States Cornell University Ithaca United States

ISBN: (纸本)9798331506612

This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing pattern images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on applications and Performance of Deep Convolution neural Network Architecture for image Segmentation 4

A Survey on Applications and Performance of Deep Convolution...

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4th IEEE International conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Bhargavi, K. Krishna, Ch.V.Phani IFHE-ICFAI Cse Dept Shankarpally Hyderabad India Teegala Krishna Reddy Engineering College Cse Dept Hyderabad India

ISBN: (纸本)9798350387063

image segmentation constitutes a critical component within the realm of medical image processing and analysis, bearing profound implications on treatment efficacy and patient survival rates. This technique serves as a foundational preprocessing step, facilitating the partitioning of images into meaningful structures or components pivotal for object representation, detection, recognition, measurement, and visualization. Given its paramount significance, a plethora of research endeavors have been dedicated to this domain, yielding a myriad of segmentation approaches predicated on intensity, color, texture, and other modalities. Numerous computational paradigms encompassing supervised, unsupervised, parametric, and probabilistic region-based techniques have emerged in pursuit of refining segmentation accuracy and efficiency. Of late, the landscape has witnessed a surge in leveraging machine learning methodologies, particularly deep learning utilizing convolutional neural networks (CNNs), to develop robust and automated image segmentation models. This paper eschews conventional segmentation strategies, instead focusing squarely on scrutinizing deep neural convolution networks and their variants for automated image segmentation. By delving into the state-of-the-art algorithms proposed between 2015 and 2017, we meticulously assess their efficacy, performance, advantages, and limitations vis-à-vis prevailing research challenges and objectives. Through this critical evaluation, we discern nuanced insights into the current state of affairs, identifying gaps, limitations, and potential avenues for advancement. By juxtaposing contemporary methodologies against the backdrop of evolving research paradigms, we offer a comprehensive delineation of the landscape's trajectory, thereby elucidating the future scope and imperatives for further advancement. © 2024 IEEE.

关键词： Convolutional neural networks

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DocAssist: Signature Perception System using Deep learning

DocAssist: Signature Perception System using Deep Learning

引用

2024 International conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Sharma, Pooja Miglani, Ishika Sharma, Lucky Panipat Institute Of Engineering And Technology Samalkha India Panipat Institute Of Engineering And Technology Artificial Intelligence And Machine Learning Samalkha India

ISBN: (纸本)9798350306446

All In the realm of communication, individuals employ two primary modes: written and spoken language. Handwriting, in particular, serves as a powerful tool for conveying information and emotions across various contexts, including healthcare. Despite its widespread use, deciphering handwritten text remains a challenge due to variations in individual writing styles and the lack of standardization. This challenge is particularly pronounced in the medical field, where handwritten prescriptions can lead to serious consequences if misinterpreted. Our research addresses this issue by proposing DocAssist, a novel model that utilizes Convolutional neural networks to achieve an impressive 81% accuracy in recognizing doctors' handwriting. By leveraging machine learning and image recognition technologies, DocAssist aims to enhance prescription legibility, mitigate medication errors, and ultimately improve patient safety and healthcare outcomes. Through rigorous experimentation with the EMNIST dataset, our study demonstrates the effectiveness of DocAssist in accurately interpreting handwritten prescriptions, highlighting its potential to revolutionize prescription processing and alleviate the burden on healthcare professionals and pharmacists. © 2024 IEEE.

关键词： image enhancement

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Brain Tumor Classification Using ML and DL Approaches 5

Brain Tumor Classification Using ML and DL Approaches

引用

5th IEEE International conference on Cybernetics, Cognition and machine learning applications, ICCCMLA 2023

作者： Solanki, Shubhangi Singh, Uday Pratap Chouhan, Siddharth Singh LNCT University Department of Computer Science Engineering Bhopal462042 India School of Mathematics Shri Mata Vaishno Devi University Kakrayl Katra182320 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798350338287

In medical image processing, categorizing brain tumors is one of the most critical and challenging challenges that must be solved. Because manual classification carried out with the assistance of humans often leads to inaccurate diagnoses and projections, we have turned to machine learning to help us. The proposed work includes a performance evaluation of autonomous brain tumor identification from MR imaging as well as a CT scan utilizing basic image processing approaches depending on multiple hard and soft computing techniques. Furthermore, six classical classifiers were used to identify brain tumors in the photos. Then, to incorporate deep learning methods into our study, we used Convolutional neural networks and Deep Convolutional neural networks for brain tumor identification. The outcome of the conventional ML with the best accuracy method SVM was analyzed to the outcome of the conventional CNN. The performance of Deep CNN models is assessed, and it is discovered that VGG16 outperforms all other Deep learning models. When contrasted with these traditional classification methods, the SVM's findings were the most accurate. After that, a CNN is used, which, in contrast to the conventional classifiers, shows a discernible rise in overall performance that is much superior. It was decided to test several different layers of CNN with a variety of split ratios for the dataset. The outcomes of the experiments show that a five-layered CNN with an 80:20 split ratio is capable of achieving the greatest performance accuracy possible, which is 97.86%. © 2023 IEEE.

关键词： Convolutional neural networks

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Perceptual learning Technology Based on Computer Vision and machine learning Algorithms 14

Perceptual Learning Technology Based on Computer Vision and ...

引用

14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Wang, Tianjian School of Computer Science and Technology Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798331531935

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security. © 2025 IEEE.

关键词： image segmentation

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Research on Application System of Computer Aided Design in Innovative Design of Intangible Cultural Heritage Clothing 2

Research on Application System of Computer Aided Design in I...

引用

2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Gu, Xiaoxue College of Art Liaoning Communication University Shenyang China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

In this paper, a semantic-based intangible cultural heritage database is constructed based on the integration of structured retrieval and the characteristics of C language, and then it is debugged and compiled, and the data is stored in the warehouse. The loopholes in the model are repaired, and finally the Han-style clothing library is constructed based on multi-view deep learning. The method of Han Fu shape measurement based on convolutional neural network is studied. A two-level convolutional neural network model is constructed. This paper improves the accuracy of the model with 2 deep transfers and repeated training. The pixel spacing of each coordinate is calculated based on image segmentation, and scaled with the plane distribution map of cultural relics provided in archaeological data and one actual measured size to obtain the scale information of the complete garment. Experiments show that the two-transfer learning convolutional networks have better convergence and learning performance. This method is used to measure the size of Han-style clothing Hanyu, and the measurement accuracy is 0.62%-5.22%. The implementation of this project will open up a new way for the restoration of ancient Chinese clothing and the measurement of cultural relics. © 2024 IEEE.

关键词： Convolutional neural networks

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Masking Improves Contrastive Self-Supervised learning for ConvNets, and Saliency Tells You Where

Masking Improves Contrastive Self-Supervised Learning for Co...

引用

IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Chin, Zhi-Yi Jiang, Chieh-Ming Huang, Ching-Chun Chen, Pin-Yu Chiu, Wei-Chen Natl Yang Ming Chiao Tung Univ Taipei Taiwan IBM Res Armonk NY USA

ISBN: (纸本)9798350318920;9798350318937

While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straight-forward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines. Our code is publicly available at: https://***/joycenerd/Saliency-Guided-Masking-for-ConvNets

关键词： Algorithms and algorithms formulations machine learning architectures

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International conference on Artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

International Conference on Artificial Intelligence, Compute...

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2024 International conference on Artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

ISBN: (纸本)9798350394528

The proceedings contain 158 papers. The topics discussed include: duct inspection and monitoring robot;deep learning-based approaches for preventing and predicting wild animals disappearance: a review;classification and tracking of items on a moving conveyor belt using convolutional networks and image processing;critical analysis of the 220/110/20 kV Sardanesti power substation from Romania in the context of identification elements of instability and insecurity;machine learning based collaborative prediction of SSD failures in the cloud;the impact of explainable ai on low-accuracy models: a practical approach with movie genre prediction;utilizing transfer learning-based algorithms for breast ultrasound data in multi-instance classification;predictive maintenance model-based on multi-stage neural network systems for wind turbines;and using teaching learning-based optimization with convolutional neural network to detect pneumonia based on chest X-Ray images.

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Layer-wise Relevance Backward Propagation for Fake Face Detection and Reconstruction 5

Layer-wise Relevance Backward Propagation for Fake Face Dete...

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5th International conference on image processing and Capsule networks, ICIPCN 2024

作者： David, Muhumuza William, Mawejje Mark Bridget, Nabagereka Raudha, Nakayiza Hellen Nakibuule, Rose Marvin, Ggaliwango Makerere University Dept of Computer Science Arua Uganda Muni University Dept of Computer and Information Science Kampala Uganda

ISBN: (纸本)9798350367171

Deep learning techniques involving Generative Adversarial networks (GANs) enable artificially generated facial images. This alone presents difficulties in terms of authenticity resulting in ethical concerns among institutional applications for digital forensics, security and social media moderation. A question remains today, why would someone fake identity and how can we improve performance of the deep neural networks? This project initiated a novel approach called Layer-wise relevance backward propagation for fake face detection and reconstruction that determines the relevance of each layer to the final prediction by extracting the most important features in the input images while giving the reason as to how the model has come to the decision. Layer wise backward relevance propagation (LBRP) being the most effective explainable technique to give relevance scores, the Convolutional neural Network (CNN) was trained using human face dataset from flickr-face-hq along with pretrained models models including VGG-16, Res-net, Mobile-net, Vision *** improve and monitor model performance, accuracy was consider along other evaluation metrics such as precision and finally machine learning operations (MLOPs) was used for managing the pipeline resources of the model thus an accuracy 99.84% with VGG-16 was obtained along with different visualization of Grad-cam and more to give insights into each prediction made by the model. Therefore due tho this effectiveness from the results obtained during image processing tasks we propose an algorithm that combines several techniques that ensure improved accuracy, precision through incorporation of interpretability using explainable methods involving LBRP and grad-cam for fake face detection and reconstruction of faces to enable capture informative features at different scales, integrating transfer learning and ensembled method for scalable performance. © 2024 IEEE.

关键词： Generative adversarial networks

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Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs 34th

Elevating Healthcare AI: Achieving Efficiency and Accuracy i...

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34th Brazilian conference on Intelligent Systems

作者： Ferreira, Gabriel Bicalho Silva, Pedro Silva, Rodrigo Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil

ISBN: (纸本)9783031790379;9783031790386

machine learning, particularly convolutional neural networks (CNNs), has gained prominence in healthcare applications, including medical diagnosis and clinical support. However, the increasing size of CNNs poses challenges in resource-constrained medical devices and real-time applications. This paper explores the effectiveness of pruning and quantization on the ResNet50 model within the MedMNIST dataset, a valuable resource for medical image classification. The study evaluates a surrogate-based multiobjective compression method on three MedMNIST datasets: RetinaMNIST for diabetic retinopathy grading, DermaMNIST for disease categorization, and BloodMNIST for blood cell classification. Results demonstrate that the proposed compression method successfully identifies less computationally intensive models while maintaining or improving accuracy across all three healthcare-related datasets. A reduction of about 50% in inference time and an increase of more than 1% in accuracy were observed. These findings emphasize the practicality of compression techniques in healthcare applications, particularly for resource-constrained environments and real-time decision-making scenarios. This research opens avenues for further validation and exploration in other healthcare-related applications with higher-quality neural network models, ultimately enhancing the deployment of machine learning in the healthcare domain.

关键词： Healthcare Artificial intelligence Convolutional neural networks neural Network Compression

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