Pattern Recognition Letters最新文献_第3页

Leveraging language to generalize natural images to few-shot medical image segmentation 利用语言对自然图像进行泛化，实现少镜头医学图像分割

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.patrec.2026.01.009

Feifan Song , Yuntian Bo , Shidong Wang , Yang Long , Haofeng Zhang

{"title":"Leveraging language to generalize natural images to few-shot medical image segmentation","authors":"Feifan Song , Yuntian Bo , Shidong Wang , Yang Long , Haofeng Zhang","doi":"10.1016/j.patrec.2026.01.009","DOIUrl":"10.1016/j.patrec.2026.01.009","url":null,"abstract":"<div><div>Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) typically involves pre-training on a large-scale source domain dataset (e.g., natural image dataset) before transferring to a target domain with limited data for pixel-wise segmentation. However, due to the significant domain gap between natural images and medical images, existing Few-shot Segmentation (FSS) methods suffer from severe performance degradation in cross-domain scenarios. We observe that using only annotated masks as cross-domain cues is insufficient, while rich textual information can effectively establish knowledge relationships between visual instances and language descriptions, mitigating domain shift. To address this, we propose a plug-in Cross-domain Text-guided (CD-TG) module that leverages text-domain alignment to construct a new alignment space for domain generalization. This plug-in module consists of two components, including: (1) Text Generation Unit that utilizes the GPT-4 question-answering system to generate standardized category-level textual descriptions, and (2) Semantic-guided Unit that aligns visual features with textual embeddings while incorporating existing mask information. We integrate this plug-in module into five mainstream FSS methods and evaluate it on four widely used medical image datasets, and the experimental results demonstrate its effectiveness. Code is available at <span><span>https://github.com/Lilacis/CD_TG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 66-72"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nighttime flare removal via frequency decoupling 通过频率解耦去除夜间耀斑

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.patrec.2026.01.012

Minglong Xue , Aoxiang Ning , Jinhong He , Shuaibin Fan , Senming Zhong

{"title":"Nighttime flare removal via frequency decoupling","authors":"Minglong Xue , Aoxiang Ning , Jinhong He , Shuaibin Fan , Senming Zhong","doi":"10.1016/j.patrec.2026.01.012","DOIUrl":"10.1016/j.patrec.2026.01.012","url":null,"abstract":"<div><div>Existing methods for nighttime flare removal struggle to effectively decouple flare features from normal image texture features, frequently resulting in the loss of local details after flare removal. Due to the differences in the frequency distribution characteristics of flare and content structural information—where flare and lighting information are primarily concentrated in the low-frequency range, while content structural information is concentrated in the high-frequency range—we propose a frequency decoupling de-flare network (FDDNet). This method effectively decouples flare from content, enabling efficient flare removal. Specifically, the network consists of the Frequency Decoupling Module (FDM) and the Frequency Fusion Module (FFM). The FDM divides the image’s frequency features into low-frequency and high-frequency components by setting masks. It dynamically optimizes its weights to effectively decouple flare from content while maximizing the retention of structural content information. In addition, based on the traditional skip connections, we propose the Frequency Fusion Module. The module separately fuses the amplitude and phase of features from both the encoding and decoding stages, reducing the impact of flare and brightness anomalies on the reconstructed image while repairing local damage caused by flare removal. Extensive experiments show that our method significantly improves the performance of nighttime flare removal.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 73-79"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signature-in-signature: A fidelity-preserving and usability-ensuring framework for dynamic handwritten signature protection 签名中的签名：动态手写签名保护的保真度和可用性框架

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.patrec.2026.01.001

Tianyu Chen, Qi Cui, Zhangjie Fu

{"title":"Signature-in-signature: A fidelity-preserving and usability-ensuring framework for dynamic handwritten signature protection","authors":"Tianyu Chen, Qi Cui, Zhangjie Fu","doi":"10.1016/j.patrec.2026.01.001","DOIUrl":"10.1016/j.patrec.2026.01.001","url":null,"abstract":"<div><div>Dynamic handwritten signature (DHS) verification is widely used for identity authentication in modern applications, offering a blend of convenience and security. However, traditional verification processes necessitate users to upload multiple signature templates to remote application servers, raising critical risks to the privacy and security of sensitive data. To address these concerns, this paper proposes a robust watermarking framework for DHS data. By embedding unique watermarks as digital signatures into DHS data, the framework ensures effective traceability of DHS, allowing the identification of sources in case of DHS misuse or leakage. Specifically, we introduce a velocity-based loss function that minimizes trajectory distortion during the watermark embedding process, effectively preserving the fidelity of the DHS. In parallel, the training process leverages contrastive learning to ensure that the watermarked DHS remains closer to the original signature in feature space than to other DHS templates. This design guarantees that the usability of the watermarked DHS is unaffected, maintaining the accuracy and reliability of signature verification systems. Extensive experiments conducted on the large-scale dynamic signature dataset demonstrate that the watermarked signatures retain visual integrity and remain imperceptible to human observation. Furthermore, the embedded watermarks exhibit compatibility with a wide range of existing verification methods, ensuring that the framework does not compromise existing verification performance.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 45-51"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data augmentation in time series forecasting through inverted framework 利用倒框架进行时间序列预测的数据扩充

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.patrec.2026.01.019

Hongming Tan , Ting Chen , Ruochong Jin , Wai Kin Victor Chan

{"title":"Data augmentation in time series forecasting through inverted framework","authors":"Hongming Tan , Ting Chen , Ruochong Jin , Wai Kin Victor Chan","doi":"10.1016/j.patrec.2026.01.019","DOIUrl":"10.1016/j.patrec.2026.01.019","url":null,"abstract":"<div><div>Currently, iTransformer is one of the most popular and effective models for multivariate time series (MTS) forecasting. Thanks to its inverted framework, iTransformer effectively captures multivariate correlation. However, the inverted framework still has some limitations. It diminishes temporal interdependency information, and introduces noise in cases of nonsignificant variable correlation. To address these limitations, we introduce a novel data augmentation method on inverted framework, called DAIF. Unlike previous data augmentation methods, DAIF stands out as the first real-time augmentation specifically designed for the inverted framework in MTS forecasting. We first define the structure of the inverted sequence-to-sequence framework, then propose two different DAIF strategies, Frequency Filtering and Cross-variation Patching to address the existing challenges of the inverted framework. Experiments across multiple datasets and inverted models have demonstrated the effectiveness of our DAIF. Our codes are available at <span><span>https://github.com/Travistan123/time-series-daif</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 152-159"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Voxel-MPI: Scene-adaptive multiplane images based local voxel tokenization with attention coordination for 3D scene representation 体素- mpi：基于局部体素标记的场景自适应多平面图像，具有三维场景表示的注意协调

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.patrec.2026.01.006

Yu Liu, Xin Ding, Qiong Liu

{"title":"Voxel-MPI: Scene-adaptive multiplane images based local voxel tokenization with attention coordination for 3D scene representation","authors":"Yu Liu, Xin Ding, Qiong Liu","doi":"10.1016/j.patrec.2026.01.006","DOIUrl":"10.1016/j.patrec.2026.01.006","url":null,"abstract":"<div><div>With the continuous optimization of learning models, 3D scene reconstruction for novel view synthesis has witnessed remarkable progress and rapid development in recent years. Compared to mainstream 3D reconstruction methods such as NeRF and 3DGS, the Multiplane Image (MPI) method demonstrates a significant balance between computational efficiency and the preservation of global structure. To enhance scene details, some studies combine local planes with global Multilayer Perceptron (MLP) learning for MPI representation. However, the inherent global consistency of global MLP networks hinders the adaptive learning of local density information, leading to the loss of local geometric and texture details in the rendered images. To address this issue, we propose a method called Voxel-MPI, which adaptively enhances local texture representation in MPI. First, we voxelize the global MPI and encode an independent MLP network for the local MPI of each voxel, enabling adaptive learning of local scene information. Next, independently learning each local MPI can lead to inconsistent rendering between blocks, causing blocky artifacts. To mitigate this, we design a Voxel Attention Block that coordinates information learned across voxel-based local MPI at the same depth, ensuring consistency and coherence in scene rendering. Experimental results demonstrate that our method outperforms existing methods on widely used real-world datasets.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 168-173"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MM-Net: Facial expression recognition based on multi-level and multi-scale attention mechanisms 基于多层次多尺度注意机制的面部表情识别

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.patrec.2026.01.007

Dongjing Wang , Hao Peng , Xin Zhang , Na Li , Wenxiu Wang , Jinlin Zhu , Shuiguang Deng

{"title":"MM-Net: Facial expression recognition based on multi-level and multi-scale attention mechanisms","authors":"Dongjing Wang , Hao Peng , Xin Zhang , Na Li , Wenxiu Wang , Jinlin Zhu , Shuiguang Deng","doi":"10.1016/j.patrec.2026.01.007","DOIUrl":"10.1016/j.patrec.2026.01.007","url":null,"abstract":"<div><div>Facial expression recognition (FER) has gained significant attention due to its diverse applications. Achieving accurate facial expression recognition requires the consideration of both global central features and subtle local features. To this end, we propose the Multi-level and Multi-scale Network (MM-Net), an FER network that leverages both multi-level and multi-scale attention mechanisms. Specifically, we design a multi-hierarchical feature learning mechanism to facilitate the FER task with Multi-Level Attention Block (MLAB) and Multi-scale Attention Block (MSAB). The MLAB focuses on learning fine-grained features with adaptive attention across different blocks in the shallow network. Meanwhile, the MSAB facilitates the multi-scale fusion of deep features, enabling the network to capture richer semantic information and expression of features. In addition, we propose Limited Center Loss, which optimizes the network by minimizing the distance between the same classes while increasing the gap between different classes. Experimental results on public datasets show that our proposed MM-Net outperforms current state-of-the-art methods, achieving results of 90.42% on the RAF-DB dataset, 90.05% on FERPlus, 65.91% on AffectNet, and 57.52% on SFEW.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"201 ","pages":"Pages 87-94"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An illumination-robust feature decomposition approach for low-light crowd counting 弱光人群计数的光照鲁棒特征分解方法

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.patrec.2025.12.005

Jian Cheng, Chen Feng, Yang Xiao, Zhiguo Cao

引用次数: 0

End-to-end interactive joint model: Clause-phrase multi-task learning for suicidal ideation cause extraction (SICE) in Chinese Weibo text 端到端交互联合模型：中文微博文本自杀意念原因抽取的子句-短语多任务学习

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-02-01 Epub Date: 2025-11-27 DOI: 10.1016/j.patrec.2025.11.036

Qi Fu , Yuhao Zhang , Dexi Liu , Liyuan Zhang , Wenzhong Peng

{"title":"End-to-end interactive joint model: Clause-phrase multi-task learning for suicidal ideation cause extraction (SICE) in Chinese Weibo text","authors":"Qi Fu , Yuhao Zhang , Dexi Liu , Liyuan Zhang , Wenzhong Peng","doi":"10.1016/j.patrec.2025.11.036","DOIUrl":"10.1016/j.patrec.2025.11.036","url":null,"abstract":"<div><div>Suicide prevention has been a critical research focus for governments, mental health professionals, and social work researchers worldwide. With the increasing number of individuals seeking help through social networks and psychological counseling platforms, timely analysis of the causes of Suicidal Ideation (SI) in help-seeking texts can provide scientific evidence and actionable insights for suicide prevention efforts. Existing approaches face challenges: (i) SIC clause extraction is coarse-grained and thus imprecise in localization; (ii) SIC phrase extraction is more precise but inherently harder. To address this, we propose an end-to-end interactive joint model (EIJM) based on a clause-phrase multi-task learning (MTL) framework, where SIC phrase extraction serves as the main task and SIC clause extraction as the auxiliary task. By leveraging joint learning, EIJM enhances extraction accuracy while reducing task difficulty. Experimental results demonstrate that EIJM outperforms the two-stage independent multi-task (2SIM) approach across multiple evaluation metrics. Specifically, in the SIC phrase extraction task, EIJM achieves a 1.1 % improvement in recall over 2SIM without compromising precision. In the SIC clause extraction task, EIJM improves precision, recall, and F1-score by 0.4 %, 0.9 %, and 0.7 %, respectively. Furthermore, in 2SIM, incorporating clause-level representations from the auxiliary task into the main task enhances local matching and fuzzy matching metrics, with the Fuzzy Match method improving the most by 0.9 %. However, it yielded limited improvement in exact matching performance.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 1-7"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TriGAN-SiaMT: A triple-segmentor adversarial network with bounding box priors for semi-supervised brain lesion segmentation TriGAN-SiaMT：一种具有边界盒先验的半监督脑损伤分割的三分段对抗网络

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-02-01 Epub Date: 2025-11-29 DOI: 10.1016/j.patrec.2025.11.032

Mohammad Alshurbaji , Maregu Assefa , Ahmad Obeid , Mohamed L. Seghier , Taimur Hassan , Kamal Taha , Naoufel Werghi

{"title":"TriGAN-SiaMT: A triple-segmentor adversarial network with bounding box priors for semi-supervised brain lesion segmentation","authors":"Mohammad Alshurbaji , Maregu Assefa , Ahmad Obeid , Mohamed L. Seghier , Taimur Hassan , Kamal Taha , Naoufel Werghi","doi":"10.1016/j.patrec.2025.11.032","DOIUrl":"10.1016/j.patrec.2025.11.032","url":null,"abstract":"<div><div>Accurate brain lesion segmentation in MRI is critical for clinical decision-making, but pixel-wise annotations remain costly and time-consuming. We propose TriGAN-SiaMT, a novel semi-supervised segmentation framework that combines adversarial learning, consistency regularization, and bounding box priors. Our architecture comprises three segmentors (<em>S</em><sub>0</sub>, <em>S</em><sub>1</sub>, <em>S</em><sub>2</sub>) and two discriminators (<em>D</em><sub>0</sub>, <em>D</em><sub>1</sub>). It includes: (1) a supervised branch (<em>S</em><sub>0</sub>↔<em>D</em><sub>0</sub>) trained on a small labeled subset; (2) a Siamese branch (<em>S</em><sub>1</sub>↔<em>D</em><sub>1</sub>) with an identical architecture to <em>S</em><sub>0</sub>↔<em>D</em><sub>0</sub>, but trained on unlabeled data; and (3) a teacher branch (<em>S</em><sub>2</sub>) updated via exponential moving average (EMA) from <em>S</em><sub>1</sub>, following the Mean Teacher (MT) paradigm. The teacher <em>S</em><sub>2</sub> generates pseudo-labels to supervise <em>S</em><sub>1</sub>. It also provides soft segmentations to guide <em>D</em><sub>1</sub>, which does not see any labeled data. The model enforces consistency at multiple levels: between <em>S</em><sub>0</sub> and <em>S</em><sub>1</sub> (Siamese consistency), and between <em>S</em><sub>1</sub> and <em>S</em><sub>2</sub> (EMA consistency). Bounding box priors are incorporated as weak supervision for both labeled and unlabeled images, improving lesion localization. Evaluated on the ISLES 2022 and BraTS 2019 datasets, TriGAN-SiaMT achieves DSC scores of 84.80 % and 86.32 %, respectively, using only 5 % labeled data. These results demonstrate strong performance under limited supervision and robust generalization across brain lesions.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 37-43"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving pseudo-labelling for semi-supervised single-class instance segmentation via mask symmetry scoring 基于掩码对称评分的半监督单类实例分割伪标记改进

IF 3.3 3区计算机科学

Pattern Recognition Letters Pub Date : 2026-02-01 Epub Date: 2025-12-02 DOI: 10.1016/j.patrec.2025.11.044

Bradley Hurst , Nicola Bellotto , Petra Bosilj

引用次数: 0