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Simulation and analysis of a tomographic imaging system for cloud field measurement in rectangular icing wind tunnels

Simulation and analysis of a tomographic imaging system for ...

引用

2024 International conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Zhang, Quan Qing, Chao Zuo, Chenglin Hu, Jiahui Li, Yan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Low Speed Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan Mianyang621000 China Key Laboratory of Icing and Anti/De-Icing China Aerodynamics Research and Development Center Mianyang621000 China Intelligent Oil and Gas Laboratory Southwest Petroleum University Chengdu610500 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

The uniformity of cloud distribution is a critical metric for evaluating the performance of icing wind tunnels, directly affecting the accuracy of experimental results. To address the challenge of measuring cloud distribution in wind tunnels, this paper proposes a solution based on extinction tomography. A rectangular cross-sectional imaging system was developed specifically for rectangular wind tunnel structures, overcoming the limitations of traditional ring-shaped systems. To resolve the issue of insufficient projections, an iterative reconstruction algorithm combined with regularization techniques was employed for accurate image reconstruction. Additionally, the BM3D denoising algorithm was applied to enhance image quality. The results of simulation experiments demonstrate that the system effectively improves the PSNR and SSIM of reconstructed images under both Gaussian and speckle noise conditions, significantly enhancing the accuracy of cloud distribution measurements. © 2025 SPIE.

关键词： image reconstruction

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Classifier-Guided Captioning Across Modalities

Classifier-Guided Captioning Across Modalities

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shaulov, Ariel Shaharabany, Tal Shaar, Eitan Chechik, Gal Wolf, Lior Tel Aviv University Israel Bar-Ilan University Israel NVIDIA Israel

ISBN: (纸本)9798350368741

Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video captioning, where different semantic cues are needed. Addressing this challenge is crucial for creating more adaptable and versatile captioning frameworks applicable across diverse real-world contexts. In this work, we introduce a method to adapt captioning networks to the semantics of alternative settings, such as capturing audibility in audio captioning, where it is crucial to describe sounds and their sources. Our framework consists of two main components: (i) a frozen captioning system incorporating a language model (LM), and (ii) a text classifier that guides the captioning system. The classifier is trained on a dataset automatically generated by GPT-4, using tailored prompts specifically designed to enhance key aspects of the generated captions. Importantly, the framework operates solely during inference, eliminating the need for further training of the underlying captioning model. We evaluated the framework on various models and modalities, with a focus on audio captioning, and report promising results. Notably, when combined with an existing zero-shot audio captioning system, our framework improves its quality and sets state-of-the-art performance in zero-shot audio captioning. © 2025 IEEE.

关键词： Audio image-captioning Language Models

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Evaluation method for underwater polarization image quality applied to laser polarization illumination conditions 4

Evaluation method for underwater polarization image quality ...

引用

4th International Computational Imaging conference, CITA 2024

作者： Zhang, Yue Liu, Chenhao Wu, Rina Jing, Jun Han, Zehui Hong, Yining Du, Xiaolong School of Science Shenyang Ligong University Shenyang100159 China

ISBN: (纸本)9781510688834

In underwater imaging, in order to solve the problem of seawater backward scattering in underwater optical imaging, we can take advantage of the difference in polarization characteristics between the scattered light and the target signal light to improve the imaging quality by combining detected polarization imaging with polarized illumination. How to quantitatively evaluate the effect of polarization imaging with a unified and achievable means is an urgent subject. This paper combines the Stokes vector method to propose a quantitative measurement method of underwater polarized light imaging quality. For the external factors affecting the imaging, evaluating the degree of improvement of the imaging quality under the active laser polarized light illumination. The impurity concentration of the water body was changed to verify the response of the imaging method, the type of illumination source, and the polarization angle under the Stokes vector polarization transmission model, respectively, so as to determine the clarity and information entropy under different impurity concentrations. Compared with the traditional method of determining image improvement, the present method can obtain more accurate quantitative data on underwater polarization description. In this paper, the method is used to quantitatively analyze the polarized image characteristics under active laser polarized illumination conditions, and it is concluded that the clarity improvement can be up to 30% or more, and the information entropy improvement can be up to 10% or more. The method proposed in this paper provides analytical data and means for determining the effect of polarization imaging, thus effectively evaluating the performance of the underwater imaging system. © 2025 SPIE.

关键词： Underwater imaging

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A fast parallel algorithm for image elevation datum conversion based on multi-core technology 6

A fast parallel algorithm for image elevation datum conversi...

引用

6th International conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Fan, Xinhao Zhang, Hengjing Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9781510688018

Aiming at the prominent problem of low efficiency in the process of elevation datum transformation of DEM or DSM images, a fast calculation method based on MPI (Message Passing Interface) parallel technology is proposed. The purpose is to use multi-core technology to effectively accelerate the working speed and comprehensively improve the efficiency and quality of elevation datum transformation. The experiment uses two sets of DEM images with different image resolutions (DEM images are used in this paper due to the same processing method of DSM images and DEM images), and conducts batch image elevation datum conversion experiments on the Ubutun 20.4.3LTS operating system. The experiment uses the MPI parallel algorithm to make full use of the performance advantages of multi-core computers to achieve the purpose of processing multiple images at the same time. The results show that the use of MPI parallel technology can greatly improve the efficiency of image elevation datum conversion. The efficiency of using this method to calculate two sets of images is increased by 10.9 and 10.859 times, respectively. MPI parallel technology can give full play to the advantages of multi-core computers, and the parallel effect is best when the number of processes is about the total number of tasks. © 2025 SPIE.

关键词： image enhancement

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Smart Parking Management system for University 2

Smart Parking Management System for University

引用

2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Cuartero, Jade G. Agusan del Sur State College of Agriculture and Technology Department of Information System Bunawan Philippines

ISBN: (纸本)9798331505745

University campuses face a parking congestion due to limited space availability and inefficient parking management, leading to wasted time, increased fuel consumption, and traffic congestion. This study developed an intelligent parking management system-integrating image processing for automatic license plate recognition and Dijkstra's Algorithm to optimize route selection;ensuring drivers reach available slots with minimal delay. The system features real-time updates, automated slot reservations, and guided navigation, significantly enhancing efficiency. Evaluation based on ISO/IEC 25010 software quality standards yielded high ratings in functionality (3.30), usability (3.33), reliability (3.18), and efficiency (3.24), indicating strong system performance. The study concludes that integrating automated parking solutions with AI-driven optimization can significantly improve parking space utilization and reduce congestion. Future enhancements include expanding database capacity, upgrading image processing accuracy, and integrating IoT sensors for real-time monitoring, ensuring a more scalable and sustainable parking solution. © 2025 IEEE.

关键词： Photointerpretation

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NCL-CIR: Noise-aware Contrastive Learning for Composed image Retrieval

NCL-CIR: Noise-aware Contrastive Learning for Composed Image...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Peng Lee, Yujian Chen, Zailong Liu, Xubo Zhang, Hui Hu, Yiyang Jing, Guquan Hong Kong Baptist University Hong Kong BNU-HKBU United International College China University of Wollongong Australia University of Surrey United Kingdom

ISBN: (纸本)9798350368741

Composed image Retrieval (CIR) seeks to find a target image using a multi-modal query, which combines an image with modification text to pinpoint the target. While recent CIR methods have shown promise, they mainly focus on exploring relationships between the query pairs (image and text) through data augmentation or model design. These methods often assume perfect alignment between queries and target images, an idealized scenario rarely encountered in practice. In reality, pairs are often partially or completely mismatched due to issues like inaccurate modification texts, low-quality target images, and annotation errors. Ignoring these mismatches leads to numerous False Positive Pair (FFPs) denoted as noise pairs in the dataset, causing the model to overfit and ultimately reducing its performance. To address this problem, we propose the Noise-aware Contrastive Learning for CIR (NCL-CIR), comprising two key components: the Weight Compensation Block (WCB) and the Noise-pair Filter Block (NFB). The WCB coupled with diverse weight maps can ensure more stable token representations of multi-modal queries and target images. Meanwhile, the NFB, in conjunction with the Gaussian Mixture Model (GMM) predicts noise pairs by evaluating loss distributions, and generates soft labels correspondingly, allowing for the design of the soft-label based Noise Contrastive Estimation (NCE) loss function. Consequently, the overall architecture helps to mitigate the influence of mismatched and partially matched samples, with experimental results demonstrating that NCL-CIR achieves exceptional performance on the benchmark datasets. © 2025 IEEE.

关键词： image retrieval Information system

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

引用

12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-Based Prediction of Human Ageing Related Proteins;the Insight of Clustering Algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat system: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation system Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA Technology;enhancing Citrus Fruit Classification and quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through Internet of Medical Things;image Processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-Based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation system Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819789450

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SSTHyper: Sparse Spectral Transformer for Hyperspectral image Reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image reconstruction

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Synthetic images with Dense Annotations and Ensemble Learning for DFU Segmentation 27th

Synthetic Images with Dense Annotations and Ensemble Learnin...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Xu, Pin Xiao, Xiongjiang Yuen, Weimin Li, Yanyi Li, Kuan Yin, Jianping Dongguan City University Guangdong China Guangdong University of Science and Technology Guangdong China Dongguan University of Technology Guangdong China

ISBN: (纸本)9783031783975

Automated image segmentation technology for Diabetic foot ulcer (DFU) management is pivotal in alleviating the healthcare system’s workload, considering the severity of DFU as a complication for diabetics. Due to the constraints of annotation costs and privacy, the scale of the publicly available DFU image segmentation datasets is relatively small, which greatly limits the performance improvement of deep learning models. We explore the potential of synthetic image technology in enhancing the performance of DFU image segmentation. We use the FreestyleNet model to generate high-quality synthetic images and employ an error pixel filtering strategy to handle the discrepancies between the synthetic images and masks. To improve the effectiveness and diversity of the synthetic dataset, we specifically designed a mask difficulty calculation method for DFU synthetic images and proposed two innovative resampling strategies based on it. The efficacy of the novel resampling strategies has been demonstrated through comparative experiments conducted against the average sampling method. Furthermore, integrating synthetic image technology with ensemble learning strategies elevates model performance even higher. Our approach achieved a Dice of 73.72% in the Diabetic Foot Ulcer Challenge 2022 on MICCAI 2022, better than the 72.87% Dice that ranked first in the testing phase, ranking second on the Live Leaderboard (as of July 5, 2024). Our code will be released at https://***/xupin262/Synthetic_DFU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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