Image and Vision Computing最新文献_第5页

Efficient 6DoF pose estimation for multi-instance objects from a single image 单幅图像中多实例对象的高效6DoF姿态估计

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.imavis.2025.105882

Wen-Nung Lie , Lee Aing

引用次数: 0

HEL-Net: Heterogeneous Ensemble Learning for comprehensive diabetic retinopathy multi-lesion segmentation via Mamba-UNet hell - net：基于Mamba-UNet的糖尿病视网膜病变多病灶分割的异构集成学习

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.imavis.2025.105879

Lingyu Wu , Haiying Xia , Shuxiang Song , Yang Lan

{"title":"HEL-Net: Heterogeneous Ensemble Learning for comprehensive diabetic retinopathy multi-lesion segmentation via Mamba-UNet","authors":"Lingyu Wu , Haiying Xia , Shuxiang Song , Yang Lan","doi":"10.1016/j.imavis.2025.105879","DOIUrl":"10.1016/j.imavis.2025.105879","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is the leading cause of blindness in adults with diabetes. Early automated detection of DR lesions is crucial for preventing vision loss and assisting ophthalmologists in treatment. However, accurately segmenting multiple types of DR lesions poses significant challenges due to their large diversity in size, shape, and location, as well as the conflict in feature modeling between local details and long-range dependencies. To address these issues, we propose a novel Heterogeneous Ensemble Learning Network (<em>HEL-Net</em>) specifically designed for four-lesion segmentation. HEL-Net comprises two ensemble stages: the first stage utilizes Mamba-UNet to generate coarse multi-lesion prediction results, which serve as contextual priors for the second stage, forming a multi-perspective lesion navigation strategy. The second stage employs a heterogeneous structure, integrating specialized networks (Mamba-UNet and U-Net) tailored to different lesion characteristics. Mamba-UNet excels in capturing large lesions by modeling long-range dependencies, while U-Net focuses on small lesions with significant local features. The heterogeneous ensemble framework leverages their complementary strengths to promote comprehensive lesion feature learning. Extensive quantitative and qualitative evaluations on two public datasets (IDRiD and DDR) demonstrate that our HEL-Net achieves competitive performance compared to state-of-the-art methods, achieving an mAUPR of 69.52%, mDice of 67.40%, and mIoU of 51.99% on the IDRiD dataset.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105879"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790365","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

LCFusion: Infrared and visible image fusion network based on local contour enhancement LCFusion：基于局部轮廓增强的红外与可见光图像融合网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.imavis.2025.105856

Yitong Yang , Lei Zhu , Xinyang Yao , Hua Wang , Yang Pan , Bo Zhang

{"title":"LCFusion: Infrared and visible image fusion network based on local contour enhancement","authors":"Yitong Yang , Lei Zhu , Xinyang Yao , Hua Wang , Yang Pan , Bo Zhang","doi":"10.1016/j.imavis.2025.105856","DOIUrl":"10.1016/j.imavis.2025.105856","url":null,"abstract":"<div><div>Infrared and visible light image fusion aims to generate integrated representations that synergistically preserve salient thermal targets in the infrared modality and high-resolution textural details in the visible light modality. However, existing methods face two core challenges: First, high-frequency noise in visible images, such as sensor noise and nonuniform illumination artifacts, is often highly coupled with effective textures. Traditional fusion paradigms readily amplify noise interference while enhancing details, leading to structural distortion and visual graininess in fusion results. Second, mainstream approaches predominantly rely on simple aggregation operations like feature stitching or linear weighting, lacking deep modeling of cross-modal semantic correlations. This prevents adaptive interaction and collaborative enhancement of complementary information between modalities, creating a significant trade-off between target saliency and detail preservation. To address these challenges, we propose a dual-branch fusion network based on local contour enhancement. Specifically, it distinguishes and enhances meaningful contour details in a learnable manner while suppressing meaningless noise, thereby purifying the detail information used for fusion at its source. Cross-attention weights are computed based on feature representations extracted from different modal branches, enabling a feature selection mechanism that facilitates dynamic cross-modal interaction between infrared and visible light information. We evaluate our method against 11 state-of-the-art deep learning-based fusion approaches across four benchmark datasets using both subjective assessments and objective metrics. The experimental results demonstrate superior performance on public datasets. Furthermore, YOLOv12-based detection tests reveal that our method achieves higher confidence scores and better overall detection performance compared to other fusion techniques.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105856"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685263","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

Privacy-aware knowledge distillation for retinal scans de-identification through adversarial perturbations 通过对抗性扰动进行视网膜扫描去识别的隐私感知知识蒸馏

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-26 DOI: 10.1016/j.imavis.2025.105849

Keramat Allah Ghaffary, Mohsen Ebrahimi Moghaddam

{"title":"Privacy-aware knowledge distillation for retinal scans de-identification through adversarial perturbations","authors":"Keramat Allah Ghaffary, Mohsen Ebrahimi Moghaddam","doi":"10.1016/j.imavis.2025.105849","DOIUrl":"10.1016/j.imavis.2025.105849","url":null,"abstract":"<div><div>The rapid increase in retinal scan datasets over the past several years has played a pivotal role in advancing the deep learning approaches for retinopathy detection. However, given the retinal vessel pattern as an accurate biometric identifier, sharing this sensitive data raises critical ethical and legal considerations pertaining to individual re-identification and privacy violations. To mitigate these concerns, we propose a knowledge distillation-based approach that aims to disrupt identity recognition models by applying imperceptible adversarial perturbations to retinal scans while simultaneously preserving their utility for medical purposes. A multi-objective loss function is utilized for this approach, consisting of several terms that effectively guide the generator part of the student network to learn the effective perturbation needed to balance these conflicting goals. Extensive experiments and explainable analysis are conducted on three public fundus datasets using five deep architectures for identity recognition and retinopathy detection. With an average identity fooling rate of 94.67% and average retinopathy detection accuracy of 97.52% for multiple unseen models, the proposed approach outperforms state-of-the-art methods in balancing between medical utility and enhancing patient privacy.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105849"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685262","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

Gaussian landmarks tracking-based real-time splatting reconstruction model 基于高斯地标跟踪的飞溅实时重建模型

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.imavis.2025.105869

Donglin Zhu, Zhongli Wang, Xiaoyang Fan, Miao Chen, Jiuyu Chen

{"title":"Gaussian landmarks tracking-based real-time splatting reconstruction model","authors":"Donglin Zhu, Zhongli Wang, Xiaoyang Fan, Miao Chen, Jiuyu Chen","doi":"10.1016/j.imavis.2025.105869","DOIUrl":"10.1016/j.imavis.2025.105869","url":null,"abstract":"<div><div>Real-time and high-quality scene reconstruction remains a critical challenge for robotics applications. 3D Gaussian Splatting (3DGS) demonstrates remarkable capabilities in scene rendering. However, its integration with SLAM systems confronts two critical limitations: (1) slow pose tracking caused by full-frame rendering multiple times, and (2) susceptibility to environmental variations such as illumination variations and motion blur. To alleviate these issues, this paper proposes gaussian landmarks-based real-time reconstruction framework — GLT-SLAM, which composes of a ray casting-driven tracking module, a multi-modal keyframe selector, and an incremental geometric–photometric mapping module. To avoid redundant rendering computations, the tracking module achieves efficient 3D-2D correspondence by encoding Gaussian landmark-emitted rays and fusing attention scores. Furthermore, to enhance the framework’s robustness against complex environmental conditions, the keyframe selector balances multiple influencing factors including image quality, tracking uncertainty, information entropy, and feature overlap ratios. Finally, to achieve a compact map representation, the mapping module adds only Gaussian primitives of points, lines, and planes, and performs global map optimization through joint photometric–geometric constraints. Experimental results on the Replica, TUM RGB-D, and BJTU datasets demonstrate that the proposed method achieves a real-time processing rate of over 30 Hz on a platform with an NVIDIA RTX 3090, demonstrating a 19% higher efficiency than the fastest Photo-SLAM method while significantly outperforming other baseline methods in both localization and mapping accuracy. The source code will be available on GitHub.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105869"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737142","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

PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone pcnet3d++：一个基于柱的级联3D目标检测模型，具有增强的2D骨干

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-29 DOI: 10.1016/j.imavis.2025.105854

Thurimerla Prasanth , Ram Prasad Padhy , B. Sivaselvan

{"title":"PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone","authors":"Thurimerla Prasanth , Ram Prasad Padhy , B. Sivaselvan","doi":"10.1016/j.imavis.2025.105854","DOIUrl":"10.1016/j.imavis.2025.105854","url":null,"abstract":"<div><div>Autonomous Vehicles (AVs) depend on sophisticated perception systems to serve as the vital component of intelligent transportation to ensure secure and smooth navigation. Perception is an essential component of AVs and enables real-time analysis and understanding of the environment for effective decision-making. 3D object detection (3D-OD) is crucial among perception tasks as it accurately determines the 3D geometry and spatial positioning of surrounding objects. The commonly used modalities for 3D-OD are camera, LiDAR, and sensor fusion. In this work, we propose a LiDAR-based 3D-OD approach using point cloud data. The proposed model achieves superior performance while maintaining computational efficiency. This approach utilizes Pillar-based LiDAR processing and uses only 2D convolutions. The model pipeline becomes simple and more efficient by employing only 2D convolutions. We propose a Cascaded Convolutional Backbone (CCB) integrated with 1 × 1 convolutions to improve detection accuracy. We combined the fast Pillar-based encoding with our lightweight backbone. The proposed model reduces complexity to make it well-suited for real-time navigation of an AV. We evaluated our model on the official KITTI test server. The model results are decent in 3D and Bird’s Eye View (BEV) detection benchmarks for the car and cyclist classes. The results of our proposed model are featured on the official KITTI leaderboard.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105854"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685261","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

Distinct Polyp Generator Network for polyp segmentation 用于息肉分割的独特息肉生成器网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-29 DOI: 10.1016/j.imavis.2025.105847

Huan Wan , Jing Ai , Jing Liu , Xin Wei , Jinshan Zeng , Jianyi Wan

{"title":"Distinct Polyp Generator Network for polyp segmentation","authors":"Huan Wan , Jing Ai , Jing Liu , Xin Wei , Jinshan Zeng , Jianyi Wan","doi":"10.1016/j.imavis.2025.105847","DOIUrl":"10.1016/j.imavis.2025.105847","url":null,"abstract":"<div><div>Accurate polyp segmentation from the colonoscopy images is crucial for diagnosing and treating colorectal diseases. Although many automatic polyp segmentation models have been proposed and achieved good progress, they still suffer from under-segmentation or over-segmentation problems caused by the characteristics of colonoscopy images: blurred boundaries and widely varied polyp sizes. To address these problems, we propose a novel model, the Distinct Polyp Generator Network (DPG-Net), for polyp segmentation. In DPG-Net, a Feature Progressive Enhancement Module (FPEM) and a Dynamical Aggregation Module (DAM) are developed. The proposed FPEM is responsible for enhancing the polyps and polyp boundaries by jointly utilizing the boundary information and global prior information. Simultaneously, a DAM is developed to integrate all decoding features based on their own traits and detect polyps with various sizes. Finally, accurate segmentation results are obtained. Extensive experiments on five widely used datasets demonstrate that the proposed DPG-Net model is superior to the state-of-the-art models. To evaluate the cross-domain generalization ability, we adopt the proposed DPG-Net for the skin lesion segmentation task. Again, experimental results show that our DPG-Net achieves advanced performance in this task, which verifies the strong generalizability of DPG-Net.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105847"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685260","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

Combining short-term and long-term memory for robust visual tracking 结合短期和长期记忆，实现强大的视觉跟踪

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-29 DOI: 10.1016/j.imavis.2025.105850

Zifan Rui , Xiaoxiao Wang , Yiteng Yang , Guang Han

引用次数: 0

A deformable registration framework for brain MR images based on a dual-channel fusion strategy using GMamba 一种基于双通道融合策略的脑磁共振图像形变配准框架

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-07 DOI: 10.1016/j.imavis.2025.105868

Liwei Deng , Songyu Chen , Xin Yang , Sijuan Huang , Jing Wang

{"title":"A deformable registration framework for brain MR images based on a dual-channel fusion strategy using GMamba","authors":"Liwei Deng , Songyu Chen , Xin Yang , Sijuan Huang , Jing Wang","doi":"10.1016/j.imavis.2025.105868","DOIUrl":"10.1016/j.imavis.2025.105868","url":null,"abstract":"<div><div>Medical image registration has important applications in medical image analysis. Although deep learning-based registration methods are widely recognized, there is still performance improvement space for existing algorithms due to the complex physiological structure of brain images. In this paper, we aim to propose a deformable medical image registration method that is highly accurate and capable of handling complex physiological structures. Therefore, we propose DFMNet, a dual-channel fusion method based on GMamba, to achieve accurate brain MRI image registration. Compared with state-of-the-art networks like TransMorph, DFMNet has a dual-channel network structure with different fusion strategies. We propose the GMamba block to efficiently capture the remote dependencies in moving and fixed image features. Meanwhile, we propose a context extraction channel to enhance the texture structure of the image content. In addition, we designed a weighted fusion block to help the features of the two channels can be fused efficiently. Extensive experiments on three public brain datasets demonstrate the effectiveness of DFMNet. The experimental results demonstrate that DFMNet outperforms multiple current state-of-the-art deformable registration methods in structural registration of brain images.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105868"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737137","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

PASS: Peer-agreement based sample selection for training with instance dependent noisy labels PASS：基于同行协议的样本选择，用于与实例相关的噪声标签的训练

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.imavis.2025.105877

Arpit Garg , Cuong Nguyen , Rafael Felix , Thanh-Toan Do , Gustavo Carneiro

{"title":"PASS: Peer-agreement based sample selection for training with instance dependent noisy labels","authors":"Arpit Garg , Cuong Nguyen , Rafael Felix , Thanh-Toan Do , Gustavo Carneiro","doi":"10.1016/j.imavis.2025.105877","DOIUrl":"10.1016/j.imavis.2025.105877","url":null,"abstract":"<div><div>Deep learning encounters significant challenges in the form of noisy-label samples, which can cause the overfitting of trained models. A primary challenge in learning with noisy-label (LNL) techniques is their ability to differentiate between hard samples (clean-label samples near the decision boundary) and instance-dependent noisy (IDN) label samples to allow these samples to be treated differently during training. Existing methodologies to identify IDN samples, including the small-loss hypothesis and feature-based selection, have demonstrated limited efficacy, thus impeding their effectiveness in dealing with real-world label noise. We present Peer-Agreement-based Sample Selection (PASS), a novel approach that utilises three classifiers, where a consensus-driven agreement between two models accurately differentiates between clean and noisy-label IDN samples to train the third model. In contrast to current techniques, PASS is specifically designed to address the complexities of IDN, where noise patterns are correlated with instance features. Our approach seamlessly integrates with existing LNL algorithms to enhance the accuracy of detecting both noisy and clean samples. Comprehensive experiments conducted on simulated benchmarks (CIFAR-100 and Red mini-ImageNet) and real-world datasets (Animal-10N, CIFAR-N, Clothing1M, and mini-WebVision) demonstrated that PASS substantially improved the performance of multiple state-of-the-art methods. This technique achieves superior classification accuracy, particularly in scenarios with high noise levels.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105877"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790213","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