Pattern Recognition最新文献_第5页

ICV-Net: An identity cost volume network for multi-view stereo depth inference

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-09 DOI: 10.1016/j.patcog.2025.111456

Pengpeng He , Yueju Wang , Yangsen Wen , Yong Hu , Wei He

{"title":"ICV-Net: An identity cost volume network for multi-view stereo depth inference","authors":"Pengpeng He , Yueju Wang , Yangsen Wen , Yong Hu , Wei He","doi":"10.1016/j.patcog.2025.111456","DOIUrl":"10.1016/j.patcog.2025.111456","url":null,"abstract":"<div><div>The construction of 3D cost volumes is essential for deep learning-based Multi-view Stereo (MVS) methods. Although cascade cost volumes alleviate the GPU memory overhead and improve depth inference performance in a coarse-to-fine manner, the cascade cost volumes are still not the optimal solution for the learned MVS methods. In this work, we first propose novel identity cost volumes with the identical cost volume size at each stage, which dramatically decreases memory footprint while clearly improving depth prediction accuracy and inference speed. The depth inference is then formulated as a dense-to-sparse search problem that is solved by performing a classification to locate predicted depth values. Specifically, in the first stage, a dense linear search is adopted to calculate an initial depth map. The depth map is then refined by sampling less depth hypotheses in the following two stages. In the final stage, we exploit a binary search with only two depth hypotheses to obtain the final depth map. Combining identity cost volumes with the dense-to-sparse search strategy, we propose an identity cost volume network for MVS, denoted as ICV-Net. The proposed ICV-Net is validated on competitive benchmarks. Experiments show our method can dramatically reduce the memory consumption and extend the learned MVS to higher-resolution scenes. Moreover, our method achieves state-of-the-art accuracy with less runtime. Particularly, among all the learning-based MVS methods, our method achieves the best accuracy (an accuracy score of 0.286) on DTU benchmark with the least GPU memory (with a testing memory overhead of 1221 MB).</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111456"},"PeriodicalIF":7.5,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

3D Points Splatting for real-time dynamic Hand Reconstruction

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-07 DOI: 10.1016/j.patcog.2025.111426

Zheheng Jiang , Hossein Rahmani , Sue Black , Bryan Williams

{"title":"3D Points Splatting for real-time dynamic Hand Reconstruction","authors":"Zheheng Jiang , Hossein Rahmani , Sue Black , Bryan Williams","doi":"10.1016/j.patcog.2025.111426","DOIUrl":"10.1016/j.patcog.2025.111426","url":null,"abstract":"<div><div>We present 3D Points Splatting Hand Reconstruction (3D-PSHR), a real-time and photo-realistic hand reconstruction approach. We propose a self-adaptive canonical points upsampling strategy to achieve high-resolution hand geometry representation. This is followed by a self-adaptive deformation that deforms the hand from the canonical space to the target pose, adapting to the dynamic changing of canonical points which, in contrast to the common practice of subdividing the MANO model, offers greater flexibility and results in improved geometry fitting. To model texture, we disentangle the appearance color into the intrinsic albedo and pose-aware shading, which are learned through a Context-Attention module. Moreover, our approach allows the geometric and the appearance models to be trained simultaneously in an end-to-end manner. We demonstrate that our method is capable of producing animatable, photorealistic and relightable hand reconstructions using multiple datasets, including monocular videos captured with handheld smartphones and large-scale multi-view videos featuring various hand poses. We also demonstrate that our approach achieves real-time rendering speeds while simultaneously maintaining superior performance compared to existing state-of-the-art methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111426"},"PeriodicalIF":7.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

One-hot constrained symmetric nonnegative matrix factorization for image clustering

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-07 DOI: 10.1016/j.patcog.2025.111427

Jie Li , Chaoqian Li

引用次数: 0

FSENet: Feature suppression and enhancement network for tiny object detection

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-07 DOI: 10.1016/j.patcog.2025.111425

Heng Hu, Sibao Chen, Zhihui You, Jin Tang

{"title":"FSENet: Feature suppression and enhancement network for tiny object detection","authors":"Heng Hu, Sibao Chen, Zhihui You, Jin Tang","doi":"10.1016/j.patcog.2025.111425","DOIUrl":"10.1016/j.patcog.2025.111425","url":null,"abstract":"<div><div>Although feature fusion has been widely used to improve detection performance, it can also lead to the mixing of feature information from different layers, which affects detection of tiny objects. To alleviate feature mixing problem, suppress the complex background interference and further improve detection performance, a new feature suppression and enhancement network is designed in this paper. In order to suppress background information and object feature information from non-local feature layers, we propose a feature suppression and enhancement module (FSEM). In FSEM, feature suppression module (FSM) aims to suppress background information and redundant features while emphasizing features of tiny objects. This helps to mitigate blending of irrelevant features and increase focusing on tiny object features. Feature enhancement module (FEM) aims to highlight deep large object feature information by combining it with shallow features. By enhancing features at different scales, FEM helps maintain feature discrimination. FSM adopts a plug-and-play design and can be embedded into detectors with feature fusion capabilities. In addition, we propose an improved Kullback–Leibler divergence (IKLD) as loss function. Distribution shifting convolution (DSConv) is adopted instead of convolution in neck to reduce computational effort. The effectiveness of our method is validated on the AI-TOD, VisDrone and DOTA datasets.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111425"},"PeriodicalIF":7.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Collaboratively enhanced and integrated detail-context information for low-light image enhancement

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-07 DOI: 10.1016/j.patcog.2025.111424

Yuzhen Niu, Xiaofeng Lin, Huangbiao Xu, Rui Xu, Yuzhong Chen

{"title":"Collaboratively enhanced and integrated detail-context information for low-light image enhancement","authors":"Yuzhen Niu, Xiaofeng Lin, Huangbiao Xu, Rui Xu, Yuzhong Chen","doi":"10.1016/j.patcog.2025.111424","DOIUrl":"10.1016/j.patcog.2025.111424","url":null,"abstract":"<div><div>Low-light image enhancement (LLIE) is a challenging task, due to the multiple degradation problems involved, such as low brightness, color distortion, heavy noise, and detail degradation. Existing deep learning-based LLIE methods mainly use encoder–decoder networks or full-resolution networks, which excel at extracting context or detail information, respectively. Since detail and context information are both required for LLIE, existing methods cannot solve all the degradation problems. To solve the above problem, we propose an LLIE method based on collaboratively enhanced and integrated detail-context information (CoEIDC). Specifically, we propose a full-resolution network with two collaborative subnetworks, namely the detail extraction and enhancement subnetwork (DE<sup>2</sup>-Net) and context extraction and enhancement subnetwork (CE<sup>2</sup>-Net). CE<sup>2</sup>-Net extracts context information from the features of DE<sup>2</sup>-Net at different stages through large receptive field convolutions. Moreover, a collaborative attention module (CAM) and a detail-context integration module are proposed to enhance and integrate detail and context information. CAM is reused to enhance the detail features from multi-receptive fields and the context features from multiple stages. Extensive experimental results demonstrate that our method outperforms the state-of-the-art LLIE methods, and is applicable to other image enhancement tasks, such as underwater image enhancement.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111424"},"PeriodicalIF":7.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 novel 6DoF pose estimation method using transformer fusion

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-06 DOI: 10.1016/j.patcog.2025.111413

Huafeng Wang , Haodu Zhang , Wanquan Liu , Zhimin Hu , Haoqi Gao , Weifeng Lv , Xianfeng Gu

引用次数: 0

SS ViT: Observing pathologies of multi-layer perceptron weights and re-setting vision transformer

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-05 DOI: 10.1016/j.patcog.2025.111422

Chao Ning, Hongping Gan

{"title":"SS ViT: Observing pathologies of multi-layer perceptron weights and re-setting vision transformer","authors":"Chao Ning, Hongping Gan","doi":"10.1016/j.patcog.2025.111422","DOIUrl":"10.1016/j.patcog.2025.111422","url":null,"abstract":"<div><div>Vision Transformer (ViT) usually adopts a columnar or hierarchical structure with four stages, where identical block settings are applied within the same stage. To achieve more nuanced configurations for each ViT block, additional search is conducted to explore stronger architectures. However, the search cost is typically expensive and the results may not be transferable to different ViT architectures. In this paper, we present a DFC module, which exploits two lightweight grouped linear (GL) layers to learn the representations of the expansion layer between two fully connected layers and the nonlinear activation of multi-layer perceptron (MLP), respectively. Afterwards, we introduce the DFC module into vanilla ViT and analyze the learned weights of its GL layers. Interestingly, several pathologies arise even though the GL layers share the same initialization strategy. For instance, the GL layer weights display different patterns across various depths, and the GL1 and GL2 weights have different patterns in the same depth. We progressively compare and analyze these pathologies and derive a specific setting (SS) for ViT blocks at different depths. Experimental results demonstrate that SS generically improves the performance of various ViT architectures, not only enhancing accuracy but also reducing inference time and computational complexity. For example, on ImageNet-1k classification task, SS yields a significant 0.8% accuracy improvement, approximately 12.9% faster inference speed, and 25% fewer floating-point operations (FLOPs) on PVTv2 model. The codes and trained models are available at <span><span>https://github.com/ICSResearch/SS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111422"},"PeriodicalIF":7.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Memory-Guided Transformer with group attention for knee MRI diagnosis

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-05 DOI: 10.1016/j.patcog.2025.111417

Rui Huang , Zonghai Huang , Hantang Zhou , Qiang Zhai , Fengjun Mu , Huayi Zhan , Hong Cheng , Xiao Yang

{"title":"Memory-Guided Transformer with group attention for knee MRI diagnosis","authors":"Rui Huang , Zonghai Huang , Hantang Zhou , Qiang Zhai , Fengjun Mu , Huayi Zhan , Hong Cheng , Xiao Yang","doi":"10.1016/j.patcog.2025.111417","DOIUrl":"10.1016/j.patcog.2025.111417","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) plays an important role in the diagnosis of knee injuries, due to its detailed information, which greatly enhances physicians’ diagnostic accuracy. However, the complex image information also makes it difficult for physicians to interpret MRI. There is an urgent need for a computer-assisted method to help physicians extract key information from MRIs and make diagnostic decisions. Knee MRI includes features across three different levels: anatomical plane-level, dataset-level, and case-level. In this paper, we approach the intelligent diagnosis of knee injuries as an interpretable MRI classification task, using a three-stage Memory-Guided Transformer (MGT) for implementation. The first stage focuses on extracting anatomical plane-level and dataset-level features through group attention and cross-attention, which are then stored in the memory matrix. In the second stage, the trained memory matrix guides the extraction of case-level features from different anatomical planes for each case. Finally, the probability of knee injury is determined using linear regression. The MGT was trained with the publicly available MRNet dataset. Compared with the original optimal model PERMIT, it shows a 5.7% improvement in the Youden index. A high level of consistency was observed between the physician-labeled diagnostic regions and the regions identified by group attention. Visualization of the trained memory revealed specific patterns, with column 62 corresponding to healthy subjects and column 81 to patients. These results demonstrate that MGT can effectively assist physicians in diagnosing knee injuries while offering excellent interpretability.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111417"},"PeriodicalIF":7.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Active learning for cross-modal cardiac segmentation with sparse annotation

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-04 DOI: 10.1016/j.patcog.2025.111403

Zihang Chen , Weijie Zhao , Jingyang Liu , Puguang Xie , Siyu Hou , Yongjian Nian , Xiaochao Yang , Ruiyan Ma , Haiyan Ding , Jingjing Xiao

{"title":"Active learning for cross-modal cardiac segmentation with sparse annotation","authors":"Zihang Chen , Weijie Zhao , Jingyang Liu , Puguang Xie , Siyu Hou , Yongjian Nian , Xiaochao Yang , Ruiyan Ma , Haiyan Ding , Jingjing Xiao","doi":"10.1016/j.patcog.2025.111403","DOIUrl":"10.1016/j.patcog.2025.111403","url":null,"abstract":"<div><div>This work presents a new dual-domain active learning method for cross-modal cardiac image segmentation with sparse annotations. Our network uses tilted Variational Auto-Encoders (tVAE) to extract and align invariant features from different domains. A proposed innovative Category Diversity Maximization approach that calculates statistical information regarding categories within a region reflects category diversity. The Uncertainty Region Selection Strategy is devised to measure the uncertainty of each predicted pixel. By jointly using these two methodologies, we identify risky areas for future annotation in active learning. The method was benchmarked against leading algorithms using two public cardiac datasets. In the MS-CMRSeg bSSFP to LGE segmentation task, our method achieved a DSC of 87.2% with just six-pixel annotations, surpassing the best results from the MS-CMRSeg Challenge 2019. In the MM-WHS dataset, our method using only 0.1% of annotations achieved a DSC of 91.8% for CT to MR and 88.9% for MR to CT, surpassing fully supervised models.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"162 ","pages":"Article 111403"},"PeriodicalIF":7.5,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Incremental clustering based on Wasserstein distance between histogram models

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-02-04 DOI: 10.1016/j.patcog.2025.111414

Xiaotong Qian , Guénaël Cabanes , Parisa Rastin , Mohamed Alae Guidani , Ghassen Marrakchi , Marianne Clausel , Nistor Grozavu

引用次数: 0