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

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image Completion Via Dual-Path Cooperative Filtering 48

Image Completion Via Dual-Path Cooperative Filtering

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Granger, Eric East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing China Shanghai Jiao Tong University School of Automation China Ets Lab. d'Imagerie de Vision et d'Intelligence Artificielle Dept. of Systems Eng. Canada

ISBN: (纸本)9781728163277

Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for restoring images, which predicts the most effective kernels based on the input scene. Motivated by this approach, we address image completion as a filtering problem. Deep feature-level semantic filtering is introduced to fill in missing information, while preserving local structure and generating visually realistic content. In particular, a Dual-path Cooperative Filtering (DCF) model is proposed, where one path predicts dynamic kernels, and the other path extracts multi-level features by using Fast Fourier Convolution to yield semantically coherent reconstructions. Experiments on three challenging image completion datasets show that our proposed DCF outperforms state-of-art methods. © 2023 IEEE.

关键词： Semantics

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DOE-based Structured Light For Robust 3D Reconstruction

DOE-based Structured Light For Robust 3D Reconstruction

引用

2023 IEEE International conference on Robotics and Biomimetics, ROBIO 2023

作者： Su, Bohuai Gu, Feifei Du, Hubing Song, Zhan Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China The Chinese University of Hong Kong Hong Kong Xi'an Technological University School of Mechatronic Engineering Xi'an710032 China

ISBN: (纸本)9798350325706

Spatial structured light (SL) enables three-dimensional measurements in a single shot by projecting patterns with coded information. However, conventional structured light systems typically employ a digital light processing projector (DLP) to project specific light patterns or coded patterns onto the surface of the object under examination. This reliance on projectors limits the miniaturization potential of such structured light systems, thereby hindering their suitability for industrial applications. The main contributions of the work in this paper include: 1) We introduce a novel and practical depth-aware miniature solution for smart terminals that employs a diffractive optical element (DOE) instead of a digital projector to project coded patterns. 2) We propose a one-dimensional SL pattern generation method that is successfully etched onto a DOE. 3) In contrast to traditional image processing techniques, all crucial steps in the pattern decoding process, such as corner detection and block recognition, are executed using deep learning methods, ensuring heightened robustness in real-world applications. The experimental results verify the effectiveness of the proposed method. © 2023 IEEE.

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A High-Capacity Reversible Data Hiding based on Improved Rhombus Prediction

A High-Capacity Reversible Data Hiding based on Improved Rho...

引用

2023 International conference on image, Signal processing, and Pattern Recognition, ISPP 2023

作者： Liu, Mengyao Zhou, Quan Zhang, Yi Hu, Yanlang Liu, Juanni Xi'an China

ISBN: (纸本)9781510666351

Reversible data hiding algorithms based on prediction error histogram of rhombus prediction need excellent prediction performance to achieve more embedding capacity. However, the cost of improving the prediction accuracy is to reduce the number of prediction pixels, which results in a reduction in embedding capacity. It means high prediction accuracy and large embedding capacity become two contradictory conditions. In this paper, we proposed a reversible data hiding algorithm based on improved rhombus prediction, which can break through the upper limit of prediction pixel numbers and increase the embedding capacity. The proposed algorithm utilizes the characteristics of reversible data hiding process to carry out the second stage reversible embedding based on the traditional rhombus prediction. It can embed the additional data into the context pixels of the first stage rhombus prediction so that the embedding capacity will be increased significantly. At the same time, reusing the reversible data hiding characteristics can ensure the lossless recovery of the carrier image and secret information. Experiments show that the algorithm can improve embedding capacity significantly and ensure the high visual performance of marked images. © 2023 SPIE.

关键词： Forecasting

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A Route Recommendation Algorithm Based on Multi-Dimensional Data Fusion and Analysis of its Social Utility 3

A Route Recommendation Algorithm Based on Multi-Dimensional ...

引用

3rd International conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Dai, Yazhe Li, You College of Electrical and Information Engineering JiLin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798331508456

The current route recommendation algorithm has problems such as single data. This paper designs a route recommendation algorithm based on multidimensional data fusion, uses convolutional neural network (CNN) to extract the features of user behavior data, geographic location information and environmental factor data, and then uses the attention mechanism to realize the fusion processing of multidimensional data. Based on the fused multidimensional data features, collaborative filtering and content-based recommendation algorithms are used to generate personalized route recommendations in combination with user preferences and real-time traffic conditions. Experiments have shown that the recommendation accuracy of this algorithm can reach up to 100%under controlled test conditions. Compared with the Bellman-Ford algorithm, this algorithm takes an average of 5.46 minutes less time, and the adoption rate of the recommendation results of this algorithm is as high as 99.9%, which proves the effectiveness of the algorithm in the cultural and tourism route recommendation scenario. © 2025 IEEE.

关键词： image fusion

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Lightweight DL-based Drone image Classification for Surveillance Applications 21

Lightweight DL-based Drone Image Classification for Surveill...

引用

21st International System-on-Chip Design conference, ISOCC 2024

作者： Yadav, Abhishek Fujita, Masahiro Kumar, Binod Indian Institute of Technology Jodhpur India University of Tokyo Japan

ISBN: (纸本)9798350377088

With the extensive use of surveillance-based systems in the present age, it is recommended to employ lightweight deep neural networks (DNNs) that not only have small silicon footprints and low latency but also provide enhanced security. In this paper, we incorporate a lightweight Attention-CNN algorithm on drones for image classification in smart city surveillance systems. By processing data directly on the drone, our approach significantly reduces the delay associated with data transmission to centralized servers, enhancing the effectiveness of surveillance operations. We ensure that our algorithms maintain high accuracy while operating within the constraints of the hardware resources. Additionally, we evaluate the resilience of these DL models against adversarial attacks, considering the security implications of deploying in real-life applications. To address the demands of latency-sensitive applications that require rapid analysis of high-resolution images, we also explore the implementation on a field-programmable gate array board. The experimental results show a maximum reduction factor of 9.56 in the memory size along with a 2.92% drop in the accuracy in the software implementation and the total power consumption on the ZCU104 FPGA board is 10 W. © 2024 IEEE.

关键词： Drones

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Detection and Correction of image Distortions Induced by Parasitic Light Sensitivity using Deep Learning Methods 15

Detection and Correction of Image Distortions Induced by Par...

引用

15th International conference on Machine Vision (ICMV)

作者： Azmi, Sara Esam Bartels, Chris Hussnain, Zille Guelen, J. A. P. Bondarev, Egor Eindhoven Univ Technol VCA Grp Eindhoven Netherlands Cyclomedia BV Zaltbommel Netherlands

ISBN: (纸本)9781510666184;9781510666191

images captured by mobile camera systems are subject to distortions that can be irreversible. Sources of these distortions vary and can be attributed to sensor imperfections, lens defects, or shutter inefficiency. One form of image distortion is associated with high Parasitic-Light-Sensitivity (PLS) in CMOS image Sensors when combined with Global Shutters (GS-CIS) in a moving camera system. The resulting distortion appears as widespread semi-transparent purple artifacts, or a complex purple fringe, covering a large area in the scene around high-intensity regions. Most of the earlier approaches addressing the purple fringing problems have been directed towards the simplest forms of this distortion and rely on heuristic image processing algorithms. Recently, machine learning methods have shown remarkable success in many image restoration and object detection problems. Nevertheless, they have not been applied for the complex purple fringing detection or correction. In this paper, we present our exploration and deployment of deep learning algorithms in a pipeline for the detection and correction of the purple fringing induced by high-PLS GS-CIS sensors. Experiments show that the proposed methods outperform state-of-the-art approaches for both problems of detection and color restoration. We achieve a final MS-SSIM of 0.966 on synthetic data, and a distortion classification accuracy of 96.97%. We further discuss the limitations and possible improvements over the proposed methods.

关键词： purple fringing deep learning image restoration image distortion distortion correction PLS factor synthetic data generation

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Secure image Retrieval in an Untrusted Cloud Environment 4th

Secure Image Retrieval in an Untrusted Cloud Environment

引用

4th International conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Prakash, Vijay Khandelwal, Kavya Gupta, Disha Garg, Lalit University of Malta Msida Malta School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science & Engineering Graphics Era Hill University Dehradun India

ISBN: (纸本)9789819994410

image processing in the scrambled area has received increasing attention for its many anticipated uses, such as providing efficient and safe solutions for protection-saving applications in untrustworthy environments. The high degree of ciphertext development caused by the existing homomorphic encryptions is one barrier to the widespread use of these algorithms. This paper considers the problem of managing images in scrambled space. We develop an image encryption scheme that limits ciphertext expansion while preserving the homomorphic characteristic using image design elements. The proposed encryption scheme first uses a probabilistic homomorphic cryptosystem to jumble the image’s pixels, then packs the complete scrambled image to conserve space. Our approach offers a significantly lower ciphertext development factor while maintaining the homomorphic characteristic than the component-wise encryption scheme. It is unnecessary to demand additional intuitive norms when using secure sign-handling tools on the compressed encoded image. We provide a rapid calculation for the encryption and pressure of the proposed image encryption plot, which expedites the computation and significantly increases the effectiveness of our strategy. The security, ciphertext extension percentage, and computing complexity are also being examined. Our validation and testing approach shows that the proposed computations are valid. The proposed algorithms for image encryption make sense as an image encryption scheme for safe picture management applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image retrieval

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Refined Feature Fusion-Based Transformer Network for Hyperspectral and LiDAR classification 2

Refined Feature Fusion-Based Transformer Network for Hypersp...

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Shen, xinxin Zhu, Hongyu Meng, Chao Chinese Flight Test Establishment Xi'an China

ISBN: (纸本)9798350331417

Multimodal remote sensing images describe the same surface scene from different viewpoints, and can gain more reliable information about the earth's surface. As a result, fusion classification of multi-modal remote sensing images has gradually become a hot area of research in recent years. There is a large body of work on devising better fusion algorithms. However, if the features of single-modal data sources cannot be thoroughly explored, the classification performance of the network will be significantly degraded. In this paper, a refined feature fusion-based Transformer network(RFFT) for hyperspectral and LiDAR classification is proposed, which effectively compensates for the aforementioned shortcomings. In particular, a shallow specific feature extraction network(SSFE) is designed. It can use a more appropriate feature extractor to obtain the shallow feature of the individual sources. A refined spectral Tokenizer module (RSTM) is then proposed to finely explore richer unique spectral information through grouping and parallel perception. Finally, a spectral intergroup-spatial perception fusion(SISP) is built to capture global multimodal feature dependencies and perform a complete fusion. Experiments results on the public Houston2013 and Trento datasets show that the proposed RFFT performs well. © 2023 IEEE.

关键词： featrue fusion hyperspectral LiDAR multimodal remote sensing image fusion classification Remote sensing

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Advances in Artificial Intelligence, Big Data and algorithms - Proceedings of the 3rd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2023

Advances in Artificial Intelligence, Big Data and Algorithms...

引用

3rd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2023

ISBN: (纸本)9781643684444

The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout systems;application of lightweight emotion recognition model in intelligent construction site monitoring;research on the application of smart wearable in the emotional management of the elderly;artificial intelligence technology in the field of broadcasting and hosting;intelligent inspection method for photovoltaic modules based on image processing and deep learning;application of AI algorithms in power system load forecasting under the new situation;research on the intelligent operation and maintenance control system of power distribution network based on big data technology;research progress of intelligent rail transit train control system;and design and implementation of intelligent potted plant management system based on Internet of Things.

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Research on Hyperspectral image Target Detection Algorithm Based on Depth Learning 13th

Research on Hyperspectral Image Target Detection Algorithm B...

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13th International conference on Frontier Computing, FC 2023

作者： Wang, Zhiping Tang, Cundong Chen, Li Wang, Yi School of Digital Media and Art Design Nanyang Institute of Technology Henan Nanyang473306 China College of Information Science and Technology Northwest University Henan Nanyang473004 China

ISBN: (纸本)9789819995370

This method includes two steps: (1) The first step is to use neural network to extract depth from the collected hyperspectral images, and then use it to classify the hyperspectral images. (2) The second step is to use the classification result obtained in step 1 to detect the target in the collected hyperspectral image. we need more algorithms, such as classification algorithm and data mining algorithm, which can help us achieve our goals. In this technology, the training data is divided into two parts: one part contains the original images captured with different spectral bands;The other part contains the same original image after processing with various algorithms. The final HIC model consists of a set of convolutional neural networks, which are trained to predict. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Deep learning

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