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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20446 条记录，以下是61-70 订阅

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Smart Agriculture: CNN-based systems for Early Detection and Diagnosis of Plant Diseases and Pests 2

Smart Agriculture: CNN-based Systems for Early Detection and...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Nishitha, Panchareddy Sarika, Tuniki Navya, Soleti Peneti, Subhashini Rajeshwari, Dablikar Vijayakumari, E. N. MLR Inst Technol Hyderabad Dept Comp Sci & Informat Technol Hyderabad India CMR Engn Coll Dept Informat Technol Hyderabad India MLR Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9798331540661;9798331540678

Pests impact agricultural production, prompting the need for efficient disease detection in plants. Traditional methods, reliant on manual observation, are time- consuming and imprecise. It uses the Convolutional Neural Network (CNN) for prediction of diseases. The CNNs enhance precision, offering farmers a more effective and streamlined method for disease detection, potentially transforming agricultural practicesUsing transfer learning and pre-trained models, the system successfully extracts information from photographs submitted via a user-friendly website. The CNN based algorithm analyzes these photos using powerful image pre-processing techniques, giving farmers and individuals precise insights into recognized diseases or pest infestations. Plant photos obtained with cellphones are processed using CNN, a specialized image recognition system. A CNN is an effective method in the AI, which scans the image and produces the maximum correct output. It is primarily used in image recognition tasks, where it processes input in the form of pixels. The website design not only allows for easy image submission but also provides results and probable therapy recommendations. This comprehensive solution, which combines cutting-edge CNN technology, transfer learning, and an easy-to-use web interface, enables users to proactively protect their crops, resulting in a more resilient and sustainable agricultural ecosystem.

关键词： Convolutional Neural Network (CNN) Disease Detection Transfer Learning Agricultural Production image Pre-Processing

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

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6th ieee international conference on image Processing, Applications and systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 ieee.

关键词： Frequency domain analysis

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An Efficient Anomalous Sound Detection by Robust Processing and Reformation of Objective 6

An Efficient Anomalous Sound Detection by Robust Processing ...

引用

6th international conference on AI Circuits and systems (AICAS)

作者： Tsai, Tsung-Lin Lo, Yi-Cheng Wu, An-Yeu (Andy) Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan

ISBN: (纸本)9798350383638;9798350383645

Boosted by the internet of Things (IoT) and neural network (NN), an accurate anomalous sound detection (ASD) system is critical in Industry 4.0 for enabling proactive maintenance. Previous works have enhanced ASD's accuracy by incorporating multi-domain features through advanced NN techniques. However, this often results in significant computational and memory overhead, which contradicts the IoT scenario. Therefore, we propose a lightweight ASD system by inspecting the core issues. First, to mitigate the noise effect of IoT, we propose dynamic signal processing to stabilize the feature extractor. Second, to adapt to the varying IoT environment, we apply structural learning to enhance the NN's generalization. Additionally, to overcome ASD's data scarcity, we improve the training strategy to strengthen NN's interpretation capability. Finally, compared to the state-of-the-art method on the DCASE 2020 dataset, our approach shows 1.34% accuracy improvement, 16.9% computation reduction, and 43.5% storage reduction.

关键词： Anomalous sound detection internet of Things robustness design dynamic signal processing structural learning

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Lessons from applying SRGAN on Sentinel-2 images for LULC classification 17

Lessons from applying SRGAN on Sentinel-2 images for LULC cl...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Goh, Yun Si Chua, Wen Qing Yean, Seanglidet Lee, Bu Sung Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350370911

Satellite images are commonly used to monitor land use land cover (LULC) changes. Unfortunately, publicly available images often lack the resolution required for detailed urban studies. In this study, we enhanced the resolution of Sentinel-2 (S2) satellite images from 10 meters to 2.5 meters using two super-resolution models: Real-SR and Real-ESRGAN. We tested the suitability of the enhanced images for LULC classification of an urban city, Singapore. From our results, colors have mostly been preserved and man-made objects have become sharper. However, the enhanced images also exhibit colour change, darkening, and salt-and-pepper effects. At this stage, there is no conclusive evidence that enhanced images can improve LULC classification. In fact, they have worsened classification accuracy by 17 - 30%, and the Kappa coefficient by 0.2 - 0.4. Although our application of super-resolution on LULC classification is not successful, it is a first attempt and could be further improved. © 2023 ieee.

关键词： image classification

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A SAR image Remote Sensing Detection Method based on Joint Spatial Fully Connected CRF 8

A SAR Image Remote Sensing Detection Method Based on Joint S...

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8th international conference on Smart internet of Things

作者： Zhang, Jianlong Luo, Jiangtao Xiong, Huihuang Zhang, Leichang Chen, Chen Li, Wan Yang, Fei Li, Huimin Gan, Qaun Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Telecommun Engn Xian Peoples R China Minist Water Resources China Xian Peoples R China

ISBN: (纸本)9798350366457;9798350366440

To address the localization issues caused by the loss of in-situ information in traditional dual phase pictures change detection methods, this paper proposes a SAR image change detection method based on a fully connected Conditional Random Field (CRF) within a geo-difference joint space. Firstly, a multi-scale fusion Siamese neural network is designed to initially locate change regions in remote sensing images based on their geographic spatial information, serving as the unary potential function of the CRF model. Secondly, a differential image is used to construct a pairwise potential function based on grayscale and positional information. Finally, the final change localization is achieved through the iterative convergence of the energy function. Simulation results indicate that the proposed method yields change detection results with complete regions and accurate boundary localization, demonstrating competitive detection performance compared to other state-of-the-art methods.

关键词： SAR image Change Detection Siamese Neural Network FC-CRF

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X-CAUNET: CROSS-COLOR CHANNEL ATTENTION WITH UNDERWATER image-ENHANCING TRANSFORMER 49

X-CAUNET: CROSS-COLOR CHANNEL ATTENTION WITH UNDERWATER IMAG...

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49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Pramanick, Alik Sarma, Sandipan Sur, Arijit Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati India

ISBN: (纸本)9798350344868;9798350344851

Underwater image enhancement is essential to mitigate the environment-centric noise in images, such as haziness, color degradation, etc. With most existing works focused on processing an RGB image as a whole, the explicit context that can be mined from each color channel separately goes unaccounted for, ignoring the effects produced by the wavelength of light in underwater conditions. In this work, we propose a framework called X-CAUNET that addresses this research gap by using cross-attention transformers. The input image is split into three channels (R-G-B), local context is captured using convolutional layers with different receptive field sizes, and a message-passing mechanism allows for context correlation between them. To maintain consistency, another transformer is used on the original image to aggregate global context, and a weighted combination of all the outputs enhances the input degraded image. Extensive experiments demonstrate we achieve state-of-the-art PSNR and SSIM with 2.66% and 2.11% relative gains. Code is available at: https://***/Alik033/X-CAUNET.

关键词： Cross-attention transformer underwater image enhancement deep learning

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Boosting Guided Super-Resolution Performance with Synthesized images 17

Boosting Guided Super-Resolution Performance with Synthesize...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Suarez, Patricia L. Carpio, Dario Sappa, Angel Espol Polytechnic University Guayaquil Ecuador Computer Vision Center Bellaterra Barcelona08193 Spain

ISBN: (纸本)9798350370911

Guided image processing techniques are widely used for extracting information from a guiding image to aid in the processing of the guided one. These images may be sourced from different modalities, such as 2D and 3D, or different spectral bands, like visible and infrared. In the case of guided cross-spectral super-resolution, features from the two modal images are extracted and efficiently merged to migrate guidance information from one image, usually high-resolution (HR), toward the guided one, usually low-resolution (LR). Different approaches have been recently proposed focusing on the development of architectures for feature extraction and merging in the cross-spectral domains, but none of them care about the different nature of the given images. This paper focuses on the specific problem of guided thermal image super-resolution, where an LR thermal image is enhanced by an HR visible spectrum image. To improve existing guided super-resolution techniques, a novel scheme is proposed that maps the original guiding information to a thermal image-like representation that is similar to the output. Experimental results evaluating five different approaches demonstrate that the best results are achieved when the guiding and guided images share the same domain. © 2023 ieee.

关键词： image enhancement

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Trust based Cooperative Secure Routing Protocols for internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 ieee.

关键词： Network layers

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Research on image Segmentation Methods of Highway Pavement Distress based on Semantic Segmentation Convolutional Neural Network

Research on Image Segmentation Methods of Highway Pavement D...

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9th international conference on signal and image Processing (ICSIP)

作者： Shao, Yongjun Zhang, Ziyi Wang, Xingang Zhao, Chihang Zheng, Youfeng Ma, Xinyi Deng, Wenhao Huang, Yaxin Shaanxi Expressway Engn Testing & Testing Co Ltd Xian Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China

ISBN: (纸本)9798350350920

Aiming at the problem that the image segmentation accuracy of highway pavement distress is easily affected by complex texture, noisy background, uneven illumination conditions and external environmental interference, this paper studies the image segmentation methods of highway pavement distress based on semantic segmentation Convolutional Neural Networks (CNN). Firstly, the methods of the image segmentation highway pavement distress based on FCN-DenseNet, DeepLabv3+, MobileNet are compared and analyzed. Secondly, the four variants of CNN models are investigated for the image segmentation of highway pavement distress, including FCN-DenseNet121 for Pavement Distress Segmentation (FCN-D121-PDS), DeepLabv3-DRN for Pavement Distress Segmentation (DL-D-PDS), DeepLabv3-MobilenetV3 for Pavement Distress Segmentation (DL-M-PDS and DeepLabv3-Mobilenet1 for Pavement Distress Segmentation (DL-M1-PDS). Finally, the comparative experiments were conducted, and the results showed that the average of DL-M1-PDS network is superior to the other three methods, with image segmentation accuracy of 98.20%.

关键词： Highway Pavement Distress image Segmentation Convolutional Neural Networks (CNN) Semantic Segmentation

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Key-Independent image Encryption using DNA Encoding and Chaotic Dynamics 10

Key-Independent Image Encryption using DNA Encoding and Chao...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Maharaja Vannia Moorthi, V. Krishna, Megha R. Kumar, Gopika Anil Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver's end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity. © 2024 ieee.

关键词： Cryptography

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