Pattern Recognition Letters最新文献

{"title":"P-RoPE: A polar-based rotary position embedding for polar transformed images in rotation-invariant tasks","authors":"Stavros N. Moutsis ,&nbsp;Konstantinos A. Tsintotas ,&nbsp;Ioannis Kansizoglou ,&nbsp;Antonios Gasteratos","doi":"10.1016/j.patrec.2025.11.037","DOIUrl":"10.1016/j.patrec.2025.11.037","url":null,"abstract":"<div><div>Rotation-invariant frameworks are crucial in many computer vision tasks, such as human action recognition (HAR), especially when applied in real-world scenarios. Since most datasets, including those on fall detection, have been generated in controlled environments with fixed camera angles, heights, and movements, approaches developed to address such tasks tend to fail when individual appearance variations occur. To address this challenge, our study proposes the use of the EVA-02-Ti lightweight vision transformer for processing people’s polar mappings and handling the task of fall detection. In particular, we strive to leverage the transformation’s rotation-invariant characteristic and correctly classify the rotated images. Towards this goal, a polar-based rotary position embedding (P-RoPE), which generates relative positions among polar patches according to <em>r</em> and <em>θ</em> axes instead of the Cartesian <em>x</em> and <em>y</em> axes, is presented. Replacing the original RoPE, we achieve an enhancement of ViT’s performance, as demonstrated in our experimental protocol, while it also outperforms a state-of-the-art approach. An evaluation was conducted on E-FPDS and VFP290k, where training was performed on initial images and testing was performed on the rotated ones. Finally, when assessed on Fashion-MNIST-rot-12k, a standard dataset for rotation-invariant scenarios, P-RoPE again surpasses both the baseline version and another benchmark method.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 23-29"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694688","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}

{"title":"Adaptive recursive channel selection for robust decoding of motor imagery EEG signal in patients with intracerebral hemorrhage","authors":"Shengjie Li ,&nbsp;Jian Shi ,&nbsp;Danyang Chen ,&nbsp;Zheng Zhu ,&nbsp;Feng Hu ,&nbsp;Wei Jiang ,&nbsp;Kai Shu ,&nbsp;Zheng You ,&nbsp;Ping Zhang ,&nbsp;Zhouping Tang","doi":"10.1016/j.patrec.2025.12.004","DOIUrl":"10.1016/j.patrec.2025.12.004","url":null,"abstract":"<div><div>In the study of electroencephalography (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs), neurorehabilitation technologies hold significant potential for recovering from intracerebral hemorrhage (ICH). However, the rehabilitation process is hindered as the clinical practicality of such systems is reduced considerably due to their lengthy setup procedures caused by excessive number of channels. Accordingly, this study proposes a channel selection method based on an adaptive recursive learning framework, which establishes a comprehensive evaluation metric by combining time-frequency domain features. Experimental results demonstrate that, upon using 37.50 % fewer channels, the average accuracy of MI classification increased from 65.44 % to 69.28 % in healthy subjects and from 65.00 % to 67.64 % in patients with ICH. This study presents the pioneering EEG-based MI BCI channel selection process specifically designed for ICH patients, paving the way for personalized rehabilitation protocols and facilitating the translation of neurotechnology into clinical practice.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 95-101"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840467","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}

{"title":"Causal-Ex: Causal graph-based micro and macro expression spotting","authors":"Pei-Sze Tan,&nbsp;Sailaja Rajanala,&nbsp;Arghya Pal,&nbsp;Raphaël C.-W. Phan,&nbsp;Huey-Fang Ong","doi":"10.1016/j.patrec.2025.12.002","DOIUrl":"10.1016/j.patrec.2025.12.002","url":null,"abstract":"<div><div>Detecting concealed emotions within apparently normal expressions is crucial for identifying potential mental health issues and facilitating timely support and intervention. The task of spotting macro- and micro-expressions involves predicting the emotional timeline within a video by identifying the onset (i.e., the beginning), apex (the peak of emotion), and offset (the end of emotion) frames of the displayed emotions. More particularly, closely monitoring the key emotion-conveying regions of the face; namely, the foundational muscle-movement cues known as facial action units (AUs)–greatly aids in the clear identification of micro-expressions. One major roadblock is the inadvertent introduction of biases into the training process, which degrades performance regardless of feature quality. Biases are spurious factors that falsely inflate or deflate performance metrics. For instance, the neural networks tend to falsely attribute certain AUs in specific facial regions to particular emotion classes, a phenomenon also termed as Inductive biases. To remove these false attributions, we must identify and mitigate biases that arise from mere correlation between some features and the output class labels. We hence introduce action-unit causal graphs. Unlike the traditional action-unit graph, which connects AUs based solely on spatial adjacency, the causal AU graph is derived from statistical tests and retains edges between AUs only when there is significant evidence that one AU causally influences another. Our model, named <span>Causal-Ex</span> (<strong>Causal</strong>-based <strong>Ex</strong>pression spotting), employs a fast causal inference algorithm to construct a causal graph of facial region of interests (ROIs). This enables us to select causally relevant facial action units in the ROIs. Our work demonstrates improvement in overall F1-scores compared to state-of-the-art approaches with 0.388 on CAS(ME)² and 0.3701 on SAMM-Long Video datasets. Our code can be found at: <span><span>https://github.com/noobasuna/causal_ex.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 52-59"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749054","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}

{"title":"Function-based labels for complementary recommendation: Definition, annotation, and LLM-as-a-Judge","authors":"Chihiro Yamasaki,&nbsp;Kai Sugahara,&nbsp;Yuma Nagi,&nbsp;Kazushi Okamoto","doi":"10.1016/j.patrec.2025.11.042","DOIUrl":"10.1016/j.patrec.2025.11.042","url":null,"abstract":"<div><div>Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferring or evaluating whether two items have a complementary relationship requires complementary relationship labels; however, defining these labels is challenging because of the inherent ambiguity of such relationships. Complementary labels based on user historical behavior logs attempt to capture these relationships, but often produce inconsistent and unreliable results. Recent efforts have introduced large language models (LLMs) to infer these relationships. However, these approaches provide a binary classification without a nuanced understanding of complementary relationships. In this study, we address these challenges by introducing Function-Based Labels (FBLs), a novel definition of complementary relationships independent of user purchase logs and the opaque decision processes of LLMs. We constructed a human-annotated FBLs dataset comprising 2759 item pairs and demonstrated that it covered possible item relationships and minimized ambiguity. We then evaluated whether machine learning methods using annotated FBLs could accurately infer labels for unseen item pairs, and whether LLM-generated complementary labels align with human perception. Among machine learning methods, ModernBERT achieved the highest performance with a Macro-F1 of 0.911, demonstrating accuracy and robustness even under limited supervision. For LLMs, GPT-4o-mini achieved high consistency (0.989) and classification accuracy (0.849) under the detailed FBL definition, while requiring only 1/842 the cost and 1/75 the time of human annotation. Overall, our study presents FBLs as a clear definition of complementary relationships, enabling more accurate inferences and automated labeling of complementary recommendations.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 8-15"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694690","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}

{"title":"DBIDM: Implementing blind image separation through a dual branch interactive diffusion model","authors":"Jiaxin Gong,&nbsp;Jindong Xu,&nbsp;Haoqin Sun","doi":"10.1016/j.patrec.2025.11.038","DOIUrl":"10.1016/j.patrec.2025.11.038","url":null,"abstract":"<div><div>In the field of Blind Image Separation (BIS), typical applications include rain/snow removal and reflection/shadow layer separation. However, the existing BIS methods generally rely on artificial priors or variants based on CNN or GAN, which are difficult to describe the complex and variable feature distribution of the source image in the real scene, resulting in defects such as source image separation estimation bias, texture distortion and artifact residue in the case of strong noise, nonlinear mixing and high coupling of texture details. To address this issue, this paper innovatively introduces diffusion models into BIS and proposes an efficient Dual Branch Interactive Diffusion Model (DBIDM). DBIDM employs a conditional diffusion model to learn the feature distribution of source images and performs an initial reconstruction of source image feature structures. Furthermore, considering that the two source images are mutually coupled with noise, we designed a Wavelet Interactive Decoupling Module (WIDM). This module is integrated into the diffusion denoising process to improve the separation of detailed information in mixed images. Experiments on synthetic datasets containing rain/snow and complex mixed interference demonstrate that the proposed DBIDM method achieves breakthrough performance in both image restoration and blind separation tasks. Specifically, in single-source degraded scenarios, DBIDM reaches optimal levels of 35.0023 dB (PSNR) and 0.9549 (SSIM) in the rain removal task. It outperforms comparison method by an average of 1.2570 dB and 0.0262. For the snow removal task, improvements of 0.9272 dB and 0.0289 over the second-best indicators are also achieved. In complex dual blind separation scenarios, the restored dual-source images significantly surpass other methods in terms of texture fidelity and detail integrity. There are improvements of 4.1249 dB in PSNR and 0.0926 in SSIM. This effectively addresses the issues of information loss and artifact remnants caused by complex coupling interference.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 44-51"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748995","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}

{"title":"Towards robust and reliable multi-modal 3D segmentation of multiple sclerosis lesions","authors":"Edoardo Coppola ,&nbsp;Mattia Savardi ,&nbsp;Alberto Signoroni","doi":"10.1016/j.patrec.2025.12.008","DOIUrl":"10.1016/j.patrec.2025.12.008","url":null,"abstract":"<div><div>Accurate 3D segmentation of multiple sclerosis lesions is critical for clinical practice, yet existing approaches face key limitations: many models rely on 2D architectures or partial modality combinations, while others struggle to generalise across scanners and protocols. Although large-scale, multi-site training can improve robustness, its data demands are often prohibitive. To address these challenges, we propose a 3D multi-modal network that simultaneously processes T1-weighted, T2-weighted, and FLAIR scans, leveraging full cross-modal interactions and volumetric context to achieve state-of-the-art performance across four diverse public datasets. To tackle data scarcity, we quantify the <em>minimal</em> fine-tuning effort needed to adapt to individual unseen datasets and reformulate the few-shot learning paradigm at an “instance-per-dataset” level (rather than traditional “instance-per-class”), enabling the quantification of the <em>minimal</em> fine-tuning effort to adapt to <em>multiple</em> unseen sources simultaneously. Finally, we introduce <em>Latent Distance Analysis</em>, a novel label-free reliability estimation technique that anticipates potential distribution shifts and supports any form of test-time adaptation, thereby strengthening efficient robustness and physicians’ trust.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 115-122"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840468","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}

{"title":"Bimodal beta mixture distribution for enhanced OOD inner-differentiation in multi-class text classification","authors":"Camilo Maldonado ,&nbsp;Carlos Valle ,&nbsp;Héctor Allende","doi":"10.1016/j.patrec.2025.11.015","DOIUrl":"10.1016/j.patrec.2025.11.015","url":null,"abstract":"<div><div>Text classification models often struggle with out-of-distribution (OOD) data due to temporal variability and the closed-world assumption. While distinguishing in-distribution from OOD data is well-studied, differentiating between valuable near-OOD data (potential new classes) and anomalous far-OOD data remains challenging. We propose BBMOE, a method that fine-tunes pre-trained models using labeled OOD data with a bimodal Beta mixture distribution regularization. This approach leverages the Beta distribution’s bounded nature and shape flexibility to enhance near-OOD versus far-OOD differentiation without compromising multi-class classification or OOD detection capabilities. Experiments with RoBERTa demonstrate improved OOD differentiation, while Low-Rank Adaptation (LoRA) reduces training time by about 32% while maintaining performance. We also analyze the relationship between class count and detection performance, and compare Total Variation distance with KL divergence. Results using AUROC and FPR@95 metrics confirm the method’s robustness for real-world text classification applications.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 158-164"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938875","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}

{"title":"MUSIC: Multi-coil unified sparsity regularization using inter-slice correlation for arterial spin labeling MRI denoising","authors":"Hangfan Liu ,&nbsp;Bo Li ,&nbsp;Yiran Li ,&nbsp;Manuel Taso ,&nbsp;Dylan Tisdall ,&nbsp;Yulin Chang ,&nbsp;John A Detre ,&nbsp;Ze Wang","doi":"10.1016/j.patrec.2025.12.009","DOIUrl":"10.1016/j.patrec.2025.12.009","url":null,"abstract":"<div><div>Arterial spin labeling (ASL) perfusion MRI stands as the sole non-invasive method to quantify regional cerebral blood flow (CBF), a crucial physiological parameter. However, ASL MRI typically suffers from a relatively low signal-to-noise ratio. In this study, we introduce a novel ASL denoising approach termed Multi-coil Unified Sparsity regularization using Inter-slice Correlation (MUSIC). While MRI, including ASL data, is routinely captured using multi-channel coils, existing denoising techniques are tailored for coil-combined data, overlooking inherent multi-channel correlations. MUSIC capitalizes on the fact that multi-channel images are primarily distinguished by coil sensitivity weighting and random noise, resulting in an intrinsic low-rank structure within the stacked multi-channel data matrix. This low rankness can be further enhanced by grouping highly correlated slices. Our approach involves adapting regularization to each slice individually, forming potentially low-rank matrices by stacking vectorized slices selected from different channels based on their Euclidean distance from the current slice under processing. Matrix rank is then approximated using the logarithm-determinant of the covariance matrix. Importantly, MUSIC operates directly on complex data, eliminating the need for separating magnitude and phase or dividing real and imaginary data, thereby minimizing information loss. The degree of low-rank regularization is controlled by the estimated noise level, achieving a balance between noise reduction and texture preservation. Experimental validation on real-world imaging data demonstrates the efficacy of MUSIC in significantly enhancing ASL perfusion quality. By effectively suppressing noise while retaining essential textural information, MUSIC holds promise for improving the utility and accuracy of ASL perfusion MRI, thus advancing neuroimaging research and clinical diagnoses.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 142-148"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938874","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}

{"title":"Enhancing precipitation detection: A multi-sensor approach using conditional GANs and recurrent networks","authors":"Pablo Negri ,&nbsp;Daniel Acevedo ,&nbsp;Juan Ruiz ,&nbsp;Sergio Gonzalez ,&nbsp;Luciano Vidal ,&nbsp;Alejo Silvarrey ,&nbsp;Maria Gabriela Nicora","doi":"10.1016/j.patrec.2025.05.022","DOIUrl":"10.1016/j.patrec.2025.05.022","url":null,"abstract":"<div><div>The advent of automatic precipitation detection with high-frequency data at very low spatial resolution (4 km) renders the satellite infrared brightness temperature (IR-BT) sensor a promising variable. Nevertheless, this approach must confront the inherent simplicity of this variable, where there is not always a strong correlation with convective precipitation, and the very low number of rain events occurring in nature, presenting an imbalanced problem. This paper proposes a novel approach to identify rainfall that integrates the IR-BT variable with lightning activity, defined as the number of detected lightning flashes per unit of time and space. The approach utilizes a recurrent neural network to estimate a binary output and a conditional GAN (cGAN) framework, which enhances the training and performance of this imbalanced problem. Inverse Dice loss, an alternative loss function, is employed to enhance the convergence and results of our framework: PD-GAN. Tests have shown that integrating sensors and the proposed architecture leads to positive outcomes, including a reduction in false alarms and an enhancement in the overlap of positive events.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 150-157"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938965","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}

{"title":"DECFusion: A lightweight decomposition fusion method for luminance artifact removal in infrared and visible images","authors":"Quanquan Xiao ,&nbsp;Haiyan Jin ,&nbsp;Haonan Su ,&nbsp;Yuanlin Zhang","doi":"10.1016/j.patrec.2025.11.034","DOIUrl":"10.1016/j.patrec.2025.11.034","url":null,"abstract":"<div><div>Infrared and visible image fusion is a current research hotspot in the field of multimodal image fusion, which aims to improve the perception and understanding of the scene through effective fusion. However, current deep learning-based fusion methods often fail to fully consider the difference between visible light brightness and thermal information of infrared images, resulting in brightness artifacts in the generated fused images, which seriously affects the visual effect of the fused images. To solve this problem, we propose a lightweight infrared and visible image decomposition fusion method (DECFusion). The method decomposes the luminance information of the visible image and the thermal information of the infrared image into illumination and reflection components through a learnable lightweight network, and adaptively adjusts the illumination component to remove unnecessary luminance interference. In the reconstruction stage, we combine the Retinex theory to reconstruct the image. Experiments show that the fused images generated by our method not only avoid the generation of luminance artifacts, but also are more lightweight and outperform the current state-of-the-art infrared and visible image fusion methods in terms of the visual quality of the fused images. Our code is available at <span><span>https://github.com/tianzhiya/DECFusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"200 ","pages":"Pages 67-73"},"PeriodicalIF":3.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749050","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}