Journal of Visual Communication and Image Representation最新文献_第2页

Document forgery detection based on spatial-frequency and multi-scale feature network

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104393

Li Li , Yu Bai , Shanqing Zhang , Mahmoud Emam

{"title":"Document forgery detection based on spatial-frequency and multi-scale feature network","authors":"Li Li , Yu Bai , Shanqing Zhang , Mahmoud Emam","doi":"10.1016/j.jvcir.2025.104393","DOIUrl":"10.1016/j.jvcir.2025.104393","url":null,"abstract":"<div><div>Passive image forgery detection is one of the main tasks for digital image forensics. Although it is easy to detect and localize forged regions with high accuracies from tampered images through utilizing the diversity and rich detail features of natural images, detecting tampered regions from a tampered textual document image (photographs) still presents many challenges. These challenges include poor detection results and difficulty of identifying the applied forgery type. In this paper, we propose a robust multi-category tampering detection algorithm based on spatial-frequency(SF) domain and multi-scale feature fusion network. First, we employ frequency domain transform and SF feature fusion strategy to strengthen the network’s ability to discriminate tampered document textures. Secondly, we combine HRNet, attention mechanism and a multi-supervision module to capture the features of the document images at different scales and improve forgery detection results. Furthermore, we design a multi-category detection head module to detect multiple types of forgeries that can improve the generalization ability of the proposed algorithm. Extensive experiments on a constructed dataset based on the public StaVer and SCUT-EnsExam datasets have been conducted. The experimental results show that the proposed algorithm improves F1 score of document images tampering detection by nearly 5.73%, and it’s not only able to localize the tampering location, but also accurately identify the applied tampering type.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104393"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

GMNet: Low overlap point cloud registration based on graph matching

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104400

Lijia Cao , Xueru Wang , Chuandong Guo

引用次数: 0

S2Mix: Style and Semantic Mix for cross-domain 3D model retrieval

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104390

Xinwei Fu , Dan Song , Yue Yang , Yuyi Zhang , Bo Wang

{"title":"S2Mix: Style and Semantic Mix for cross-domain 3D model retrieval","authors":"Xinwei Fu , Dan Song , Yue Yang , Yuyi Zhang , Bo Wang","doi":"10.1016/j.jvcir.2025.104390","DOIUrl":"10.1016/j.jvcir.2025.104390","url":null,"abstract":"<div><div>With the development of deep neural networks and image processing technology, cross-domain 3D model retrieval algorithms based on 2D images have attracted much attention, utilizing visual information from labeled 2D images to assist in processing unlabeled 3D models. Existing unsupervised cross-domain 3D model retrieval algorithm use domain adaptation to narrow the modality gap between 2D images and 3D models. However, these methods overlook specific style visual information between different domains of 2D images and 3D models, which is crucial for reducing the domain distribution discrepancy. To address this issue, this paper proposes a Style and Semantic Mix (S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>Mix) network for cross-domain 3D model retrieval, which fuses style visual information and semantic consistency features between different domains. Specifically, we design a style mix module to perform on shallow feature maps that are closer to the input data, learning 2D image and 3D model features with intermediate domain mixed style to narrow the domain distribution discrepancy. In addition, in order to improve the semantic prediction accuracy of unlabeled samples, a semantic mix module is also designed to operate on deep features, fusing features from reliable unlabeled 3D model and 2D image samples with semantic consistency. Our experiments demonstrate the effectiveness of the proposed S<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>Mix on two commonly-used cross-domain 3D model retrieval datasets MI3DOR-1 and MI3DOR-2.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104390"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Masked facial expression recognition based on temporal overlap module and action unit graph convolutional network

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104398

Zheyuan Zhang , Bingtong Liu , Ju Zhou , Hanpu Wang , Xinyu Liu , Bing Lin , Tong Chen

{"title":"Masked facial expression recognition based on temporal overlap module and action unit graph convolutional network","authors":"Zheyuan Zhang , Bingtong Liu , Ju Zhou , Hanpu Wang , Xinyu Liu , Bing Lin , Tong Chen","doi":"10.1016/j.jvcir.2025.104398","DOIUrl":"10.1016/j.jvcir.2025.104398","url":null,"abstract":"<div><div>Facial expressions may not truly reflect genuine emotions of people . People often use masked facial expressions (MFEs) to hide their genuine emotions. The recognition of MFEs can help reveal these emotions, which has very important practical value in the field of mental health, security and education. However, MFE is very complex and lacks of research, and the existing facial expression recognition algorithms cannot well recognize the MFEs and the hidden genuine emotions at the same time. To obtain better representations of MFE, we first use the transformer model as the basic framework and design the temporal overlap module to enhance temporal receptive field of the tokens, so as to strengthen the capture of muscle movement patterns in MFE sequences. Secondly, we design a graph convolutional network (GCN) with action unit (AU) intensity as node features and the 3D learnable adjacency matrix based on AU activation state to reduce the irrelevant identity information introduced by image input. Finally, we propose a novel end-to-end dual-stream network combining the image stream (transformer) with the AU stream (GCN) for automatic recognition of MFEs. Compared with other methods, our approach has achieved state-of-the-art results on the core tasks of Masked Facial Expression Database (MFED).</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104398"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Transformer-guided exposure-aware fusion for single-shot HDR imaging

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104401

An Gia Vien , Chul Lee

引用次数: 0

BAO: Background-aware activation map optimization for weakly supervised semantic segmentation without background threshold

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104404

Izumi Fujimori , Masaki Oono , Masami Shishibori

{"title":"BAO: Background-aware activation map optimization for weakly supervised semantic segmentation without background threshold","authors":"Izumi Fujimori , Masaki Oono , Masami Shishibori","doi":"10.1016/j.jvcir.2025.104404","DOIUrl":"10.1016/j.jvcir.2025.104404","url":null,"abstract":"<div><div>Weakly supervised semantic segmentation (WSSS), which employs only image-level labels, has attracted significant attention due to its low annotation cost. In WSSS, pseudo-labels are derived from class activation maps (CAMs) generated by convolutional neural networks or vision transformers. However, during the generation of pseudo-labels from CAMs, a background threshold is typically used to define background regions. In WSSS scenarios, pixel-level labels are typically unavailable, which makes it challenging to determine an optimal background threshold. This study proposes a method for generating pseudo-labels without a background threshold. The proposed method generates CAMs that activate background regions from CAMs initially based on foreground objects. These background-activated CAMs are then used to generate pseudo-labels. The pseudo-labels are then used to train the model to distinguish between the foreground and background regions in the newly generated activation maps. During inference, the background activation map obtained via training replaces the background threshold. To validate the effectiveness of the proposed method, we conducted experiments using the PASCAL VOC 2012 and MS COCO 2014 datasets. The results demonstrate that the pseudo-labels generated using the proposed method significantly outperform those generated using conventional background thresholds. The code is available at: <span><span>https://github.com/i2mond/BAO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104404"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143339471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Generalization enhancement strategy based on ensemble learning for open domain image manipulation detection

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-31 DOI: 10.1016/j.jvcir.2025.104396

H. Cheng , L. Niu , Z. Zhang , L. Ye

{"title":"Generalization enhancement strategy based on ensemble learning for open domain image manipulation detection","authors":"H. Cheng , L. Niu , Z. Zhang , L. Ye","doi":"10.1016/j.jvcir.2025.104396","DOIUrl":"10.1016/j.jvcir.2025.104396","url":null,"abstract":"<div><div>Image manipulation detection methods play a pivotal role in safeguarding digital image authenticity and integrity by identifying and locating manipulations. Existing image manipulation detection methods suffer from limited generalization, as it is difficult for existing training datasets to cover different manipulation modalities in the open domain. In this paper, we propose a Generalization Enhancement Strategy (GES) based on data augmentation and ensemble learning. Specifically, the GES consists of two modules, namely an Additive Image Manipulation Data Augmentation(AIM-DA) module and a Mask Confidence Estimate based Ensemble Learning (MCE-EL) module. In order to take full advantage of the limited number of real and manipulated images, the AIM-DA module enriches the diversity of the data by generating manipulated traces accumulatively with different kinds of manipulation methods. The MCE-EL module is designed to improve the accuracy of detection in the open domain, which is based on computing and integrating the evaluation of the confidence level of the output masks from different image manipulation detection models. Our proposed GES can be adapted to existing popular image manipulation detection methods. Extensive subjective and objective experimental results show that the detection F1 score can be improved by up to 34.9%, and the localization F1 score can be improved by up to 11.7%, which validates the effectiveness of our method.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104396"},"PeriodicalIF":2.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143339496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Semantic feature refinement of YOLO for human mask detection in dense crowded

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-30 DOI: 10.1016/j.jvcir.2025.104399

Dan Zhang , Qiong Gao , Zhenyu Chen , Zifan Lin

{"title":"Semantic feature refinement of YOLO for human mask detection in dense crowded","authors":"Dan Zhang , Qiong Gao , Zhenyu Chen , Zifan Lin","doi":"10.1016/j.jvcir.2025.104399","DOIUrl":"10.1016/j.jvcir.2025.104399","url":null,"abstract":"<div><div>Due to varying scenes, changes in lighting, crowd density, and the ambiguity or small size of targets, issues often arise in mask detection regarding reduced accuracy and recall rates. To address these challenges, we developed a dataset covering diverse mask categories (CM-D) and designed the YOLO-SFR convolutional network (Semantic Feature Refinement of YOLO). To mitigate the impact of lighting and scene variability on network performance, we introduced the Direct Input Head (DIH). This method enhances the backbone’s ability to filter out light noise by directly incorporating backbone features into the objective function. To address distortion in detecting small and blurry targets during forward propagation, we devised the Progressive Multi-Scale Fusion Module (PMFM). This module integrates multi-scale features from the backbone to minimize feature loss associated with small or blurry targets. We proposed the Shunt Transit Feature Extraction Structure (STFES) to enhance the network’s discriminative capability for dense targets. Extensive experiments on CM-D, which requires less emphasis on high-level features, and MD-3, which demands more sophisticated feature handling, demonstrate that our approach outperforms existing state-of-the-art methods in mask detection. On CM-D, the Ap50 reaches as high as 0.934, and the Ap reaches 0.668. On MD-3, the Ap50 reaches as high as 0.915, and the Ap reaches 0.635.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104399"},"PeriodicalIF":2.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143339497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A Visible-Infrared person re-identification algorithm based on skeleton Insight Criss-Cross network

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-28 DOI: 10.1016/j.jvcir.2025.104395

Pan Jiaxing , Zhang Baohua , Zhang Jiale , Gu Yu , Shan Chongrui , Sun Yanxia , Wu Dongyang

{"title":"A Visible-Infrared person re-identification algorithm based on skeleton Insight Criss-Cross network","authors":"Pan Jiaxing , Zhang Baohua , Zhang Jiale , Gu Yu , Shan Chongrui , Sun Yanxia , Wu Dongyang","doi":"10.1016/j.jvcir.2025.104395","DOIUrl":"10.1016/j.jvcir.2025.104395","url":null,"abstract":"<div><div>There are significant inter-class differences in the cross-modal feature space. If the pedestrian skeleton information is used as the discrimination basis for cross-modal person re-identification, the problem of mismatch between the skeleton features and the ID attributes is inevitable. In order to solve the above problems, this paper proposes a novel Skeleton Insight Criss-Cross Network (SI-CCN), which consists of a Skeleton Insight Module (SIM) and a Criss-Cross Module (CCM). The former uses the skeleton hierarchical mechanism to extract the key skeleton information of the pedestrian limb area, obtain the characteristics of the skeleton key points at the pixel level, and the skeleton key points are used as the graph nodes to construct the skeleton posture structure of the pedestrian. And as a result, the SIM module can not only accurately capture the spatial information of various parts of the pedestrian, but also maintain the relative positional relationship between the key points of the skeleton to form a complete skeleton structure. The latter cooperatively optimizes the characteristics of high-dimensional skeleton and low-dimensional identity identification by using a cross-learning mechanism. In order to effectively capture the diverse skeleton posture, the attention distribution of the two in the feature extraction process is dynamically adjusted to integrate identity details at the same time, and the consistency of cross-modal features is improved. The experiments on the two cross-modal person re-identification data sets of SYSU-MM01 and RegDB show that the Rank-1 and mAP of the SI-CCN on the SYSU-MM01 data set are 81.94% and 76.92%, respectively, and the Rank-1 and mAP on the RegDB data set are 95.49% and 95.67%, respectively. The proposed method has better performance than that of the recent representative methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104395"},"PeriodicalIF":2.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Visual object tracking based on adaptive deblurring integrating motion blur perception

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-01-27 DOI: 10.1016/j.jvcir.2025.104388

Lifan Sun , Baocheng Gong , Jianfeng Liu , Dan Gao

{"title":"Visual object tracking based on adaptive deblurring integrating motion blur perception","authors":"Lifan Sun , Baocheng Gong , Jianfeng Liu , Dan Gao","doi":"10.1016/j.jvcir.2025.104388","DOIUrl":"10.1016/j.jvcir.2025.104388","url":null,"abstract":"<div><div>Visual object tracking in motion-blurred scenes is crucial for applications such as traffic monitoring and navigation, including intelligent video surveillance, robotic vision navigation, and automated driving. Existing tracking algorithms primarily cater to sharp images, exhibiting significant performance degradation in motion-blurred scenes. Image degradation and decreased contrast resulting from motion blur compromise feature extraction quality. This paper proposes a visual object tracking algorithm, SiamADP, based on adaptive deblurring and integrating motion blur perception. First, the proposed algorithm employs a blur perception mechanism to detect whether the input image is severely blurred. After that, an effective motion blur removal network is used to generate blur-free images, facilitating rich and useful feature information extraction. Given the scarcity of motion blur datasets for object tracking evaluation, four test datasets are proposed: three synthetic datasets and a manually collected and labeled real motion blur dataset. Comparative experiments with existing trackers demonstrate the effectiveness and robustness of SiamADP in motion blur scenarios, validating its performance.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"107 ","pages":"Article 104388"},"PeriodicalIF":2.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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