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

共 12594 条记录，以下是111-120 订阅

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Improving Compressive Imaging Recovery via Measurement Augmentation

Improving Compressive Imaging Recovery via Measurement Augme...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Romario Gualdrón-Hurtado Roman Jacome Leon Suarez Emmanuel Martinez Henry Arguello Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In compressive imaging systems, the scene is acquired via linear coded noisy projections, known as measurements, requiring a recovery process to estimate the underlying signal. This recovery is inherently ill-posed, posing a challenge for accurate signal recovery. Existing methods that employ prior information about the signal often fail in practical scenarios. In this work, instead of developing a new prior over the signal, we exploit the structure of the low-dimensional measurements to synthesize an augmented measurement set that can be used in various recovery methods to improve its performance. We used a deep neural network to generate the synthetic measurements from the acquired data. We show the benefits of this approach in two schemes, deep learning-based recovery and the plug-and-play (PnP) algorithm. Particularly, our method is interpreted as a non-linear preconditioning technique for the PnP algorithm. We show improved performance for different sensing matrices.

关键词： image coding Atmospheric measurements Imaging Signal processing algorithms Particle measurements Acoustic measurements Sensors Noise measurement Speech processing image reconstruction

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HPDNET: Hyper Prior Dependent Demosaic Neural Network

HPDNET: Hyper Prior Dependent Demosaic Neural Network

引用

International image processing, Applications and systems conference (IPAS)

作者： Youngil Seo Sungho Jun Dongpan Lim Seongwook Song System LSI Division Samsung Electronics Yongin Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation.

关键词： image quality Deep learning image sensors Artificial neural networks Pressing Feature extraction Rendering (computer graphics) Information filters Computational efficiency image restoration

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Patch based image processing for complex environment characterization

Patch based image processing for complex environment charact...

引用

International image processing, Applications and systems conference (IPAS)

作者： Corentin Menier Cyril Meurie Timothée Guillemaille Yassine Ruichek Juliette Marais Univ Gustave Eiffel COSYS-LEOST Villeneuve d’Ascq France UTBM Belfort France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

关键词： Training Global navigation satellite system Visualization Satellites Three-dimensional displays Accuracy Uncertainty Vegetation mapping Cameras Trajectory

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Sport Safety Enhancement: A Novel Approach with Monitoring Matrix-Based image Acquisition and Analysis

Sport Safety Enhancement: A Novel Approach with Monitoring M...

引用

Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Xinyi Zhang Department of Physical Education Shandong Management University Jinan China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

With the development of video array imaging technology, the accuracy and stability of video image acquisition have been improved. In this situation, this study proposes novel approach with monitoring matrix-based image acquisition and analysis for sport safety enhancement. This study includes the following four major innovations 1) Developed an image acquisition system based on a monitoring matrix, which can accurately capture the facial information of athletes through array imaging mode; 2) Proposed a new facial feature extraction algorithm, which realizes real-time analysis of facial images by fusing EAR and MAR features; 3) Proposed an improved expression recognition algorithm, and the improved activation function optimizes the expression recognition network to accurately identify the facial expressions of athletes; 4) Designed an image classification algorithm, and the 8 -pixel average mode is used for fuzzy information processing, so as to provide real-time warning information about the physical condition of athletes through the expression data. The experimental results show that the proposed method has satisfactory recognition accuracy on the Cambridge University Computer Laboratory's facial expression data set, and can accurately provide early warning of the athlete's status.

关键词： image recognition Accuracy Face recognition Imaging Feature extraction Real-time systems Classification algorithms Safety Monitoring Sports

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

引用

International Workshop on Mobile Terahertz systems (IWMTS)

作者： Yevhen Ivanenko Aman Batra Viet Thuy Vu Mats I. Pettersson Thomas Kaiser Blekinge Institute of Technology Karlskrona Sweden University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325–0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm.

关键词： Terahertz wave imaging Radar remote sensing Accuracy Heuristic algorithms Signal sampling Approximation algorithms Radar polarimetry Reliability image reconstruction Remote sensing

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image Search Engine with Recognition

Image Search Engine with Recognition

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： JMSV Ravi Kumar Bellaganti Dheeraj Geddada Gowtham Dokala Vivek Babu Bonthu Asha Kiran M. Srikanth Department of Information Technology SRKR Engineering College Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use.

关键词： Deepfakes Visualization Sentiment analysis image recognition Face recognition Search engines Feature extraction User experience Artificial intelligence Usability

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Productivity Estimation Based on Optical Remote Sensing image Spatiotemporal Fusion Algorithm

Productivity Estimation Based on Optical Remote Sensing Imag...

引用

6th International conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Chu, Jingyi UC Santa Barbara Santa BarbaraCA93117 United States

ISBN: (纸本)9783031882869

With the rapid development of remote sensing technology, the application of optical remote sensing images in productivity estimation is becoming increasingly widespread. However, due to the limited temporal and spatial resolution of remote sensing images, accurately estimating productivity still faces challenges. This study improves the spatiotemporal resolution of optical remote sensing images through spatiotemporal fusion algorithms to achieve more accurate productivity estimation. We use advanced spatiotemporal fusion technology to combine high temporal resolution but low spatial resolution images with high spatial resolution but low temporal resolution images to generate composite images with high spatiotemporal resolution. These composite images can more accurately capture the dynamic changes in surface productivity. Even at the longest time scale of 3 months, the spectral fidelity remains at a high level of 0.87, indicating that the model still has good spectral fidelity performance when processing remote sensing images with long time intervals. This study not only improves the accuracy of productivity estimation, but also provides new data support for resource management and decision-making in industries such as agriculture and forestry, which has important theoretical and practical significance. In addition, our method can also provide reference for other fields and promote the widespread application and development of remote sensing technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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A Lightweight and Efficient Convolutional Neural Network for Crowd Counting

A Lightweight and Efficient Convolutional Neural Network for...

引用

International image processing, Applications and systems conference (IPAS)

作者： Marios Constantinou Panayiotis Kolios Christos Kyrkou KIOS CoE University of Cyprus Nicosia Cyprus Department of Computer Science KIOS CoE University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The crowd counting task plays a key role in ensuring public safety during large gatherings events. Most prominent works in this area, use large and computationally demanding deep learning model architectures, which require substantial computational power, limiting their usage in a real-world scenario under resource constraints. In this work we consider the trade-off between the model’s predicted accuracy and computational speed. We propose an improved version of HR-Net, which is substantially smaller and faster than the original, but preserves its localization and counting performance. Through targeted removal of unnecessary modules and branches, we demonstrate an increase in frames-per-second by 37.71% on an Nvidia Jetson Orin, and a reduction of GMACs and parameters by 77.41% and 73.07% respectively, while retaining competitive localization and counting performance, specifically for aerial imagery scenarios. Our modifications enable the algorithm to process in real-time higher resolution images, which is crucial when dealing with small objects. Furthermore, because most crowd counting datasets contain random images gathered from the web, and limited aerial images of crowds, we introduce a specialized dataset of high-resolution aerial imagery for sparse and dense crowds in various environments, that contains new drone-captured annotated image data.

关键词： Location awareness Deep learning Limiting image resolution Computational modeling Computer architecture Predictive models Real-time systems Public security Inference algorithms

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Fourth International conference on image processing and Capsule Networks 1

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丛书名： Lecture Notes in Networks and systems

1000年

作者： Subarna Shakya João Manuel R. S. Tavares Antonio Fernández-Caballero George Papakostas

ISBN: (数字)9789819970933

ISBN: (纸本)9789819970926

This book includes high-quality research papers presented at the Fourth International conference on image processing and Capsule Networks (ICIPCN 2023), which is held in Bangkok, Thailand, during 10–11 August 2023. This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations.

关键词： Computational Intelligence image processing and Computer Vision Signal, image and Speech processing algorithms

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Optical Authenticity in Pushbroom System for Spectral Information Protection

Optical Authenticity in Pushbroom System for Spectral Inform...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Pablo Gomez Roman Jacome Emmanuel Martinez Hans Garcia Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access or modification before applying a protective strategy. In response, this work presents a pushbroom HSI system that integrates an authentication key on the acquisition process, ensuring the key imperceptibility without affecting performance in spectral classification tasks. In addition, this work develops an algorithm designed to simulate attacks through class modifications in the HSI. Additionally, a detection algorithm is proposed to identify modifications by comparing the signed HSI with the altered version, effectively recognizing the embedded key. The proposed method was tested using simulated data and acquired data in the optical laboratory. Results demonstrate that the system maintains classification accuracy, further enhancing imperceptibility. Additionally, modifications are detectable by the key with 99% accuracy.

关键词： Integrated optics Accuracy Authentication Signal processing algorithms Optical imaging Classification algorithms Optical sensors Optical signal processing Speech processing Hyperspectral imaging

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