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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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Deep Learning Based Automobile Identification Application 29th

Deep Learning Based Automobile Identification Application

引用

29th International conference on Neural Information processing

作者： Chantamit-O-Pas, Pattanapong Sangaroon, Pattanayu Srisura, Jukkapat King Mongkuts Inst Technol Ladkrahang Sch Informat Technol Bangkok 10520 Thailand

ISBN: (纸本)9789819916474;9789819916481

Today, the high competition among domestic automobile manufacturers is intense situation than previous years. This result gives advantages in a good variety of brands, models, engine sizes and appearances. This can cause some critical issues in recognizing and recalling a car by manufacturer. In addition, an owner may modify some parts of original vehicle such as the head bumper, the rear bumper, and the head light. This modification also affects the people who are looking for pre-owned cars. Despite the fact, the details are mismatch with the vehicle registration book that issued by the Department of Land Transport. From this incident, the researchers implemented a convolutional neural network (CNN) in the identification of vehicle characteristics to reduce the ambiguity for each car's models. The researchers conducted experiments using five algorithms. SVM, ResNet34, ResNet50 and Inception-ResNetV2. The researchers set up a library of two car models, Toyota Hilux and Honda Civic sedan and Civic Hatch-back, including models from past ten years ago until the present. The images are of 224 x 224 pixels. The data are categorized into two sets, a training set has 1,449 images which is counted as 80% of total images and a testing set is having 362 images which is about 20% of total. The total images are 1,811 and 26 Classes. Our experiments compared the accuracies of SVM, ResNet34, ResNet50, and Inception-ResNetV2, which came out to be 21.4%, 55.5%, 66.6%, and 92.8% respectively. As a result, Inception-ResNetV2 outperforms among all other methods.

关键词： Automobile image image Recognition Convolutional Neural Network

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image processing for Knowledge and Comparison of Piranesi’s Carceri Editions 1

引用

3rd International and Interdisciplinary conference on image and Imagination, IMG 2021

作者： Menconero, Sofia Department of History Representation and Restoration of Architecture Sapienza University of Rome Piazza Borghese 9 Rome00186 Italy

ISBN: (数字)9783031259067

ISBN: (纸本)9783031259050

Piranesi printed the 16 etchings of the Carceri d’invenzione in 1761. This version, which is more widespread and better known, derives from the reworking of matrices that the Venetian engraver produced in 1749–50. These are the 14 plates of the Invenzioni capricciose di Carceri all’acquaforte. Many studies have focused on the semantic and formal comparison of the two main versions of the series. Up to now, comparisons have been conducted using juxtaposed pictures, whether original prints or digital reproductions. This procedure involves some limitations due to the effort of synthesis and mental superimposition of the two pictures of different editions to correctly identify the areas where changes occurred. The contribution proposes a method of formal comparison between the two main editions of the series through the digital superimposition of two states of the same plate so that it is easier, more immediate, and more accurate to note the changes. This method is based on the application of image processing algorithms, which let to obtain a synthesis picture in false colours that clearly shows the modified parts of each plate. Observation of the results has suggested the distinction of two main types of modifications carried out by Piranesi (additions/integrations and removal), clearly identifiable in the synthesis pictures. In addition to its implications for Piranesi studies, this research shows how digital image processing can be an essential tool for knowledge in the field of art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Etching

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Improving The Hiding Capacity of image Steganography with Stego-Analysis

Improving The Hiding Capacity of Image Steganography with St...

引用

2023 IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2023

作者： Grandhe, Padmaja Mallikarjuna Reddy, A. Chillapalli, Kavyasri Koppera, Kavya Thambabathula, Manasa Reddy Surasani, L.P. Pscmrcet Cse Department Vijayawada India Anurag University Department of Cse Hyderabad India

ISBN: (纸本)9798350398465

Communicating online without fearing third-party interventions is becoming a challenge in the modern world. Especially the sectors like the military, and government organizations or private companies sharing sensitive information. They invest a lot of effort and cost into obtaining the advancement of safe communication techniques. image processing encryption techniques using various algorithms promote security over communication channels and using different analysis methods make the tool stand out in providing security to the information. In today's world, there are various steganographic mechanisms that convert the secret message into stego medium and send it across various communication channels. Using algorithms like Blind Hide promotes the security of the message along with using multiple analysis methods that will further improve the tool in giving out information of encoded accuracy, size of stego of the secret message. The aim is to generate a tool that will give out a benchmark value of how precisely the message is stored in the cover file. Using Stego and bulk analysis the information about the presence of the stego medium in the message can be known to the user. All these analysis methods make the tool more enhanced and secure. © 2023 IEEE.

关键词： Steganography

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BCD-Unet:A Novel Water Areas Segmentation Structure for Remote Sensing image 7

BCD-Unet:A Novel Water Areas Segmentation Structure for Remo...

引用

7th International conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Jonnala, Naga Surekha Gupta, Neha Vasantrao, Chafle Pratiksha Mishra, Anoop Kumar VIT-AP University School of Electronics Engineering Amaravati India

ISBN: (纸本)9798350397253

Surface water resource identification is one of the main techniques used in remote sensing image analysis. This is necessary to stop calamities like floods and droughts. Feature selection based on prior information and complex spectrum analysis are required by machine learning algorithms for extracting waterbodies. The use of deep learning-based algorithms, which have just lately been considered, has eliminated the need for manually extracting features, but they require a large amount of training data and processing power to achieve outstanding performance. Because of this, each deep learning architecture that has been demonstrated may recognize some of the patterns that are present in the given situations. To develop a reliable model for identifying surface water, this research trains and optimises deep learning architecture that have been presented in different disciplines using surface water data. This is accomplished by the model's output more accurately by extracting the finalized segmented mask of water bodies. Our suggested approach was tested using a Kaggle dataset. This dataset included Sentine1-2 satellite images of numerous water bodies. The suggested method outperforms current best practises in semantic segmentation performance. © 2023 IEEE.

关键词： Surface waters

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Robust Non-convex Model-Based Approach for Deep Learning-Based image processing 4th

Robust Non-convex Model-Based Approach for Deep Learning-Ba...

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4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Morotti, Elena Evangelista, Davide Loli Piccolomini, Elena Department of Political and Social Sciences University of Bologna Bologna Italy Department of Mathematics University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9783031812408

Total p-norm Variation (TpV) is a well-established technique in image processing, used to denoise and preserve edges. However, the related non-convex minimization is still a challenging task in optimization, both for the computational cost and for convergence guarantees toward a good local minimum. We propose a framework, called (TpV)2, embedding a convolutional neural network to speed up the iterative reconstruction while preserving converging features. The resulting hybrid method is robust and accurate. Verifications and comparisons illustrate that the proposed method is effective and promising. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse Problems Neural Network Non-convex optimization

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Increasing the accuracy of object boundaries detection and the quality of machine vision system data in terms of machine processing by a collaborative complex 32

Increasing the accuracy of object boundaries detection and t...

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conference on Signal processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Semenishchev, Evgenii Shraifel, Igor Makov, Sergey Surkov, Egor Zelensky, Aleksandr Voronin, Viacheslav Tula State Univ TulSU 92 Sq Lenina Tula 300012 Tula Russia Don State Tech Univ DSTU Inst Serv & Entrepreneurship 147 St Shevchenko Shakhty 346500 Rostov Russia Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

To article proposes an approach to improve the quality of data used in various processes based on machine vision systems. The paper proposes a combined approach to applying the method of multi-criteria processing based on the use of a combined criterion in order to implement an edge detector, smoothing and separation areas of the background / object in the image. The application of the method allows eliminating the noise caused by external factors (such as dust and water suspension on the lens or space). The generated data make it possible to form an adaptive criterion for changing the correction parameters for a non-linear change in color balance in areas of increased detail or selected masks of changes blocks. The proposed algorithms make it possible to increase the visibility of small elements, reduce the noise component, while maintaining the boundaries of objects, increase the accuracy of selecting the boundaries of objects and the visual quality of data. As test data used to evaluate the effectiveness, nature data and expert evaluation results for test images obtained by a machine vision system with a sensor with a resolution of 1024x768 (8-bit, color image, visible range) are used. images of simple shapes are used as analyzed objects.

关键词： boundaries detection image machine vision image smoothing

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CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders 37

CROMA: Remote Sensing Representations with Contrastive Radar...

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Fuller, Anthony Millard, Koreen Green, James R. Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Carleton Univ Dept Geog & Environm Studies Ottawa ON Canada

ISBN: (纸本)9781713899921

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data;this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimodal representations. Our method separately encodes masked-out multispectral optical and synthetic aperture radar samples-aligned in space and time-and performs cross-modal contrastive learning. Another encoder fuses these sensors, producing joint multimodal encodings that are used to predict the masked patches via a lightweight decoder. We show that these objectives are complementary when leveraged on spatially aligned multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our cross- and self-attention matrices. These strategies improve representations and allow our models to effectively extrapolate to images up to 17.6x larger at test-time. CROMA outperforms the current SoTA multispectral model, evaluated on: four classification benchmarks-finetuning (*** arrow 1.8%), linear (*** arrow 2.4%) and nonlinear (*** arrow 1.4%) probing, kNN classification (*** arrow 3.5%), and K-means clustering (*** arrow 8.4%);and three segmentation benchmarks (*** arrow 6.4%). CROMA's rich, optionally multimodal representations can be widely leveraged across remote sensing applications.

关键词： image enhancement

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Predicting Autonomous Vehicle Navigation Parameters via image and image-and-Point Cloud Fusion-based End-to-End Methods

Predicting Autonomous Vehicle Navigation Parameters via Imag...

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IEEE International conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Beycimen, Semih Ignatyev, Dmitry Zolotas, Argyrios Cranfield Univ Ctr Autonomous & Cyber Phys Syst SATM Bedford MK43 0AL England

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. image-based and image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.

关键词： Training Laser radar Recurrent neural networks Navigation Prediction algorithms Data models Land vehicles

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Enhancing Patient Privacy in IoT-Enabled Intelligent systems: A Deep and Broad Learning-Based Efficient Encryption Network 8

Enhancing Patient Privacy in IoT-Enabled Intelligent Systems...

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8th International conference on Smart Internet of Things

作者： Zhu, Xiaoqiang Liu, Jiqiang Zhang, Dalin Li, Lingkun Wang, Nan Beijing Jiaotong Univ Sch Cyberspace Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China

ISBN: (纸本)9798350366457;9798350366440

The rapid development of the Internet of Things (IoT) is enabling a wide range of applications in intelligent medical systems. Among others, medical imaging equipment produces sensitive user privacy information, however, current solutions from academia and industry often neglect the importance of secure communication mechanisms. There are open research challenges such as low real-time processing and poor security, even when using cryptography. This paper proposes EDBNet, an efficient encryption network based on deep and broad learning to improve patient privacy for medical images. To be specific, a four-layer convolutional neural network is employed to extract the horizontal and vertical factors and utilize broad learning to guide the training model to obtain two feature matrices. The training process includes pre-training and fine-tuning, with the open-source COVID-CT-Dataset enabling dual-stream encryption. To further enhance ciphertext image security in a privacy-protected environment, chaotic cryptography is utilized to consummate the encryption network, which includes scrambling and diffusion combing with the SHA3-256 algorithm. The proposed EDBNet is evaluated by extensive experiments, which show that it outperforms several state-of-the-art algorithms, such as the average cipher entropy of 7.9971, encryption quality of 248, and encrypted/decrypted time of around 1 second.

关键词： IoT image encryption Privacy protection Machine learning Chaos

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Medical image super-resolution algorithm based on multi-scale feature aggregation 2

Medical image super-resolution algorithm based on multi-scal...

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2024 International conference on Medical Artificial Intelligence

作者： Han, Xinyu Gong, Xun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transp Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China Southwest Jiaotong Univ Mfg Ind Chains Collaborat & InformationSupport Te Chengdu 611756 Peoples R China

ISBN: (纸本)9798350377620;9798350377613

Ultrasound images are vital for medical diagnostics but often suffer from information loss and blurred details due to limitations in imaging systems and sensor technologies. Many researchers have proposed super-resolution algorithms to enhance medical images. But existing super-resolution methods struggle with the uneven distribution of noise and clarity. To address this, we propose a super-resolution algorithm for medical images based on multi-scale feature aggregation Leveraging the architecture of Unet as our primary framework, our method enhances output details through multi-scale hole convolutions. Taking into consideration the characteristics of ultrasonic images, we propose a frequency domain-based module to enhances edge information while effectively denoising the image. Furthermore, to improve the quality of the output images, we introduce an image processing nodule grounded in global information. The module ensures clarity while considering the overarching context, thereby preserving global consistency and enhancing output quality. We experiment on three datasets to demonstrate the effectiveness of our model. Additionally, significant improvements in medical image segmentation are observed, proving the practicability of our proposed approach.

关键词： Ultrasound images super-resolution multi-scale feature aggregation frequency-domain global information

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