IEEE open journal of signal processing最新文献_第9页

Depth Enhanced Cascaded Framework for OCTA Segmentation With Structure- and Connectivity-Aware Losses 具有结构和连接感知损失的OCTA分割深度增强级联框架

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-04 DOI: 10.1109/OJSP.2026.3690094

Bisheng Wang;Yuexuan Wang;Jaime S. Cardoso;Lin Wu

{"title":"Depth Enhanced Cascaded Framework for OCTA Segmentation With Structure- and Connectivity-Aware Losses","authors":"Bisheng Wang;Yuexuan Wang;Jaime S. Cardoso;Lin Wu","doi":"10.1109/OJSP.2026.3690094","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3690094","url":null,"abstract":"Optical coherence tomography angiography (OCTA), known for its high-resolution and noninvasive imaging capability, has become a key modality for visualizing retinal vasculature. Accurate and automated segmentation of capillaries, arteries, veins, and foveal avascular zone in OCTA images is essential for quantitative analysis and disease assessment. In this paper, we propose a depth enhanced cascaded framework specifically designed for multi-class OCTA segmentation. Our method investigates the spatial distribution of vasculature in retinal images and integrates a novel self-supervised depth prediction module to learn implicit depth cues from volumetric data, thereby improving the discrimination of overlapping vascular layers. In addition, we design two topology-aware loss functions that explicitly encourage structural integrity and continuity of vessel segmentation, particularly at bifurcations and endpoints. Experiments on the OCTA-6 mm and OCTA-3 mm datasets demonstrate that our method outperforms existing state-of-the-art approaches, with mIoU gains of around 2% over prior method, IPNv2, thereby highlighting enhanced segmentation accuracy and vascular topology preservation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"518-530"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11503897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Oriented Teacher: Exploiting Spatial Resolution Disparity to Enhance Semi-Supervised Aerial Object Detection 定向教师：利用空间分辨率差异来增强半监督空中目标检测

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/OJSP.2026.3685499

Yuqiu Li;Jian Xue;Ke Lu

{"title":"Oriented Teacher: Exploiting Spatial Resolution Disparity to Enhance Semi-Supervised Aerial Object Detection","authors":"Yuqiu Li;Jian Xue;Ke Lu","doi":"10.1109/OJSP.2026.3685499","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3685499","url":null,"abstract":"This study aims to exploit the issue of spatial resolution disparity in aerial imagery for enhanced Semi-Supervised Aerial Object Detection (SSAOD). The challenge could arise when the spatial resolution of the unlabeled image deviates significantly from the resolution distribution of previously processed labeled images, potentially interfering the pseudo-labeling process. This misalignment can lead to the generation of low-quality pseudo labels, increasing the occurrence of false positives and reducing the recall rate during the sample assignment phase for the student model. To address this, we introduce a novel framework, Oriented Teacher, which incorporates a Multi-resolution Teaching Strategy (MTS). This strategy employs a combination of image transformations to adaptively adjust the spatial resolution of the unlabeled image, ensuring better alignment with labeled images and higher-quality pseudo labels generated by the teacher model. Additionally, the teacher prediction scores are utilized as soft pseudo labels, providing more informative guidance. Then our framework features a Soft VariFocal Loss (SVF) mechanism, specifically designed to minimize the impact of false positives and easy negatives within the unsupervised branch. Through extensive experiments conducted on DOTA-v1.5 and DOTA-v2.0 datasets under various evaluation protocols, we demonstrate that our proposed method achieves state-of-the-art performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"472-481"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11487987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Sensor Dataset for 3D Human Pose Estimation Using Imaging Radar 基于成像雷达的三维人体姿态估计多传感器数据集

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-09 DOI: 10.1109/OJSP.2026.3711537

Trung Nguyen;Prashant Kumar Rai;Nikolay Serbenyuk;Reza Ghabcheloo;Nataliya Strokina

{"title":"A Multi-Sensor Dataset for 3D Human Pose Estimation Using Imaging Radar","authors":"Trung Nguyen;Prashant Kumar Rai;Nikolay Serbenyuk;Reza Ghabcheloo;Nataliya Strokina","doi":"10.1109/OJSP.2026.3711537","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3711537","url":null,"abstract":"This work focuses on 3D Human Pose Estimation (HPE) using imaging radar. We introduce DualRadarHPE, a new multi-sensor dataset containing calibrated and temporally aligned data from two widely adopted radar sensors – Texas Instruments (TI) and Bosch. The dataset includes three radar data representations from TI: raw ADC data, heatmap, and point-cloud; and point clouds from Bosch. The data was captured in both indoor and outdoor scenarios. These scenarios were designed to reflect challenges of real-world applications, such as presence of large metallic objects that can increase radar sensor noise, sensor motion, and multiple people in the scene. We overview state-of-the-art open-access datasets and benchmarks for radar-based 3D HPE and evaluate two popular point-cloud-based methods and the most recent heatmap-based method. We introduce necessary algorithmic adaptations to improve their performance. Finally, we propose a new RadarViT method that is more light-weight and outperforms the existing methods with respect to the mean per joint position error. We tested this model in outdoor conditions with and without re-training, in the presence of multiple persons, and under dynamic conditions. The dataset and the code will be made publicly available.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"753-765"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11602699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Signal Processing Society Publication Information IEEE信号处理学会出版物信息

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-08-20 DOI: 10.1109/OJSP.2025.3647345

引用次数: 0

Contextual Attention for Robust Audio-Visual Emotion Recognition 鲁棒视听情感识别的语境注意

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-26 DOI: 10.1109/OJSP.2025.3648710

Lucas Goncalves;Huang-Cheng Chou;Ali N. Salman;Chi-Chun Lee;Carlos Busso

{"title":"Contextual Attention for Robust Audio-Visual Emotion Recognition","authors":"Lucas Goncalves;Huang-Cheng Chou;Ali N. Salman;Chi-Chun Lee;Carlos Busso","doi":"10.1109/OJSP.2025.3648710","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3648710","url":null,"abstract":"<italic>Audio-visual emotion recognition</i> (AVER) often performs well under ideal conditions but faces significant challenges in scenarios with missing modalities (e.g., missing frames of audio and/or video). Addressing these challenges is crucial for the effective deployment of AVER systems in <italic>human-computer interaction</i> (HCI) applications, where robustness can significantly impact user experience. This study introduces a novel approach that enhances AVER robustness by leveraging a decoder-like summarizer structure. This structure processes audio and visual content and generates contextual summaries that effectively capture emotional cues even when modalities are degraded. To enhance system resilience against missing modalities, we integrate modality dropout during training, enabling the summarizer to adaptively handle these scenarios. We define the context summary length as the number of learnable query tokens used in the summarizer, a fixed hyperparameter in our model. We analyze how varying context summary lengths affect performance, identifying an optimal balance between compression and expressiveness. In addition to improving robustness, we systematically evaluate model calibration across emotions in current <italic>state-of-the-art</i> (SOTA) AVER methods. Our experiments on the MSP-IMPROV and CREMA-D databases demonstrate that our model achieves superior performance across macro-, micro-, and weighted-F1 scores, both under ideal conditions and in scenarios with modality losses. Additionally, we conduct ablation studies to assess the impact of different context lengths on our summarizer structure in terms of overall AVER performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"42-53"},"PeriodicalIF":2.7,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sequential Inference for Non-Gaussian Additive Processes 非高斯加性过程的顺序推理

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-25 DOI: 10.1109/OJSP.2025.3648708

Zixin Liu;Simon Godsill

引用次数: 0

Multiple Model Recursive Gaussian Process for Robust Target Tracking 多模型递归高斯过程鲁棒目标跟踪

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-19 DOI: 10.1109/OJSP.2025.3646127

ALI EMRE BALCı;Raj Thilak Rajan

{"title":"Multiple Model Recursive Gaussian Process for Robust Target Tracking","authors":"ALI EMRE BALCı;Raj Thilak Rajan","doi":"10.1109/OJSP.2025.3646127","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3646127","url":null,"abstract":"Accurate tracking of targets is vital for safe and reliable operations, particularly in complex and dynamic environments such as urban areas. Traditional tracking methods, including Kalman and particle filters, often perform poorly in real world scenarios, due to inaccurate models and sparse or noisy measurements. Gaussian process (GP) based methods offer a flexible and data driven alternative with uncertainty quantification that does not depend on predefined dynamical equations. However, state of the art GP tracking approaches require expensive hyperparameter optimization, which limits their practicality for real time applications. In this work, we introduce a novel GP mixture based computationally efficient tracking method, which is capable of modeling complex system behavior and adapt to changing dynamics. Our proposed solution, named Multiple Model Recursive Gaussian Process (MM-RGP), adapts continuously to changing dynamics, is capable of modeling complex behavior, and is robust against sparse observation. In addition, the proposed method avoids hyperparameter optimization and adapts to incoming data. We demonstrate the effectiveness of our solution using the example of uncrewed aerial vehicle (UAV) tracking, with both simulated and real datasets, and propose directions for extending our work.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"23-31"},"PeriodicalIF":2.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11304544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Direction of Arrival Estimation for the Coexistence of Uncorrelated and Coherent Signals via Rotation Spatial Differencing Method 基于旋转空间差分的非相关和相干共存信号的到达方向估计

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-15 DOI: 10.1109/OJSP.2025.3644786

Peng Luo;Boyu Pang;Defeng Wu;W. Zeng

{"title":"Direction of Arrival Estimation for the Coexistence of Uncorrelated and Coherent Signals via Rotation Spatial Differencing Method","authors":"Peng Luo;Boyu Pang;Defeng Wu;W. Zeng","doi":"10.1109/OJSP.2025.3644786","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3644786","url":null,"abstract":"This paper presents a novel direction of arrival (DOA) estimation method via rotation spatial differencing technique that offers high resolution, robustness, and stable performance. To suppress external environmental noise and improve estimation accuracy, a new modified covariance matrix is constructed using a rotation matrix technique. Additionally, a spatial differencing matrix is built with neighboring subarrays to achieve signal decoherence. Due to the introduction of the new modified covariance matrix, the differencing matrix has a new feature without compromising the array flow pattern, leading to improved spatial differencing. Finally, a multiple signal classification (MUSIC) spectral search algorithm using the singular value decomposition (SVD) is applied to accurately localize both uncorrelated and coherent signals at once, which greatly facilitates the DOA estimation process. Experimental results demonstrate that the proposed method delivers superior DOA estimation performance, providing accurate and stable signal direction estimation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"11-22"},"PeriodicalIF":2.7,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11300944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

List of Reviewers 审稿人名单

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-15 DOI: 10.1109/OJSP.2025.3635745

引用次数: 0

RANF: Neural Field-Based HRTF Spatial Upsampling With Retrieval Augmentation and Parameter Efficient Fine-Tuning 基于神经场的HRTF空间上采样与检索增强和参数有效微调

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-12-04 DOI: 10.1109/OJSP.2025.3640517

Yoshiki Masuyama;Gordon Wichern;François G. Germain;Christopher Ick;Jonathan Le Roux

{"title":"RANF: Neural Field-Based HRTF Spatial Upsampling With Retrieval Augmentation and Parameter Efficient Fine-Tuning","authors":"Yoshiki Masuyama;Gordon Wichern;François G. Germain;Christopher Ick;Jonathan Le Roux","doi":"10.1109/OJSP.2025.3640517","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3640517","url":null,"abstract":"This paper gives an in-depth description of our submission to Task 2 of the Listener Acoustic Personalization (LAP) challenge 2024, which aims to reconstruct head-related transfer functions (HRTFs) with dense spatial grids from sparse measurements. Neural fields (NFs) with parameter-efficient fine-tuning (PEFT) have led to dramatic performance improvements in HRTF spatial upsampling and personalization. Despite these advances, spatial upsampling performance remains limited in scenarios with very sparse measurements. Our proposed system, named retrieval-augmented NF (RANF), incorporates HRTFs retrieved from a dataset as auxiliary inputs. We leverage multiple retrievals via transform-average-concatenate and adopt a PEFT technique tailored for retrieval augmentation. Furthermore, we capitalize on the results of a signal-processing-based spatial upsampling method as optional inputs. By incorporating these auxiliary inputs, our system demonstrated state-of-the-art performance on the SONICOM dataset and placed first in Task 2 of the LAP challenge 2024.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"32-41"},"PeriodicalIF":2.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0