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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79682 条记录，以下是171-180 订阅

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

This research introduces "Jaddah,"an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency. © 2025 ieee.

关键词： image enhancement

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Constructing Datasets From Public Police Body Camera Footage

Constructing Datasets From Public Police Body Camera Footage

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Rosas-Smith, Jamie Bartelds, Martijn Huang, Ruizhe García-Perera, Leibny Paola Livescu, Karen Jurafsky, Dan Field, Anjalie Johns Hopkins University United States Stanford University United States Toyota Technological Institute Chicago United States

ISBN: (纸本)9798350368741

The enormous potential of body-worn cameras to improve accountability in policing remains largely unrealized due to large volumes of unreviewed footage. Transcription and diarization tools could aid in reviewing footage, but lack of public data hinders their development. We develop a pipeline to construct public datasets, making use of the small number of videos publicly released by police departments, with capacity to update the data as footage gets released or removed. Our pipeline produces two datasets, a large one with transcriptions automatically extracted from department-generated captions, and a smaller test set where we manually validated transcripts and alignment. We benchmark ASR models, including models fine-tuned on our data, on our test set, to show applications of our datasets and continued challenges of this domain. Our work presents a new vision for leveraging public body-worn camera footage-even when it can't be rereleased-to help address this critical social issue. © 2025 ieee.

关键词： accountability noisy domains policing social applications speech recognition

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A 3D U-Net based Network for Segmenting medical images of Fetal MRI in three Dimensions 6

A 3D U-Net based Network for Segmenting medical images of Fe...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Singh, Maneesha Singh, Sameesha Hutter, Jana Jiancong, L. MRIIRS Centre for Health Innovations Faridabad India King's College London Centre for Developing Brain United Kingdom King's College London Centre for Developing Brain London United Kingdom

ISBN: (纸本)9798331506520

This study introduces a U-Net based algorithmic framework designed to segment 3D MRI images of perinatal fetal brains from a cohort of 20 fetuses, with gestational ages ranging from 20 to 36 weeks. Furthermore, an optimization network based on U-Net is also proposed to enhance the segmentation efficiency of the proposed U-Net framework. The experimental results indicate that the resultant Dice Score metric, Hausdorff distance and Jaccard Index all showed notable enhancements when compared to the U-Net pipeline. The final results confirm that the proposed method is highly effective for segmenting fetal brains across various gestational ages, with the optimized model yielding superior segmentation quality as compared to the conventional U-Net model. © 2025 ieee.

关键词： image segmentation

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Time-varying EEG signal Reconstruction Based on Local Graph signal Smoothness

Time-varying EEG Signal Reconstruction Based on Local Graph ...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cao, Yuting Wang, Jian Li, Zhengpin Jiang, Xue-Qin Hou, Xinxin School of Information Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798350368741

Electroencephalography (EEG) signals record the electrical activity of the brain and have significant applications in neuroscience and medicine. However, accurately reconstructing EEG signals has been a challenge due to potential signal missing and noise interference during signal acquisition. The paper exploits the underlying structure of EEG signals and presents an efficient method for reconstructing EEG signals based on local graph signal smoothness (LGS-based). Firstly, we introduce the concept of local graph signal smoothness according to distinct functional areas of the cerebral cortex. Then, considering the graph that does not properly represent similar relationships between signals will have a negative impact on the reconstruction performance, we propose a joint graph learning and EEG signal reconstruction optimization method. Since it is not jointly convex, we utilize the alternating direction method of multipliers (ADMM) to solve it. In experiments, it is shown that the proposed LGS-based method outperforms benchmark methods in EEG signal reconstruction. Additionally, the proposed method achieves a higher signal-to-noise ratio (SNR) as well as a smaller normalized mean square error (RMSE). © 2025 ieee.

关键词： alternating direction method of multipliers (ADMM) EEG signals reconstruction graph learning local graph signal smoothness

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Aligning Text-to-image Diffusion Models without Human Feedback

Aligning Text-to-Image Diffusion Models without Human Feedba...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Tao Kuang, Huafeng Lin, Xianming Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China

ISBN: (纸本)9798350368741

Incorporating human feedback to optimize text-to-image models has demonstrated significant effectiveness. However, the process of collecting high-quality human preference labels is both resource-intensive and time-consuming. To address this challenge, we propose a novel approach that leverages a large language model (LLM) to generate sophisticated prompts, guiding the diffusion model towards enhanced image generation. This process inherently produces ranking pairs that approximate human preferences. We further introduce a novel integration of AI feedback with a Supervised Fine-Tuning (SFT) policy, aligning the model with preference labels derived from AI. Our experiments demonstrate that our approach achieves a notable approximation of human preferences, achieving a performance level of 68.13% compared to human-level benchmarks and delivering competitive results. Furthermore, we showcase the synergistic effects of combining AI feedback with human feedback, resulting in further improvements in image quality. This research offers fresh insights into AI feedback learning within text-to-image generation and lays the groundwork for more efficient and cost-effective training methodologies. © 2025 ieee.

关键词： AI feedback diffusion model human feedback large language model

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CurMIM: Curriculum Masked image Modeling

CurMIM: Curriculum Masked Image Modeling

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 ieee.

关键词： Curriculum Learning image Representation Masked image Modeling Visual Pre-training

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Decoupling While Coupling: Towards More Accurate Stereo image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 ieee.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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18th international Workshop on Design and Architecture for signal and image processing, DASIP 2025

18th International Workshop on Design and Architecture for S...

引用

18th international Workshop on Design and Architecture for signal and image processing, DASIP 2025

ISBN: (纸本)9783031878961

The proceedings contain 10 papers. The special focus in this conference is on Design and Architectures for signal and image processing. The topics include: LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based on LiDAR Data;A Practical HW-Aware NAS Flow for AI Vision applications on Embedded Heterogeneous SoCs;Endoscopy image Classification for Wireless Capsules with CNNs on Microcontroller-Based Platforms;joint Underwater Depth Estimation and Dehazing from a Single image Using Attention U-Net;KD-AHOSVD: Neural Network Compression via Knowledge Distillation and Tensor Decomposition;Novel Scheduling and Shifter Networks for 5G LDPC Decoders;Comparison Between In-Core Hardware IDS, Off-Core Hardware IDS and Software IDS;comparative Study of Memory Optimization Techniques for Dataflow-Modeled applications.

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Extremum Encoding for Joint Baseband signal Compression and Time-Delay Estimation for Distributed Systems

Extremum Encoding for Joint Baseband Signal Compression and ...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Weiss, Amir Kochman, Yuval Wornell, Gregory W. Faculty of Engineering Bar-Ilan University Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Research Laboratory of Electronics Massachusetts Institute of Technology United States

ISBN: (纸本)9798350368741

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension introduces new challenges, e.g., due to unknown phase of the signal of interest and random phase of the noise, rendering a naïve application of the original scheme inapplicable and irrelevant. In the face of these challenges, we propose a judiciously adapted, though natural, extension of the scheme, paving its way to RF applications. While our extension leads to a different statistical analysis, including extremes of non-Gaussian distributions, we show that, ultimately, its asymptotic behavior is akin to the original scheme. We derive an exponentially tight upper bound on its error probability, corroborate our results via simulation experiments, and demonstrate the superior performance compared to two benchmark approaches. © 2025 ieee.

关键词： compression for estimation data compression distributed estimation max-index estimator Time-delay estimation

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 ieee.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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