Pattern Recognition最新文献_第10页

TLR-3DRN: Unsupervised single-view reconstruction via tri-layer renderer

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-05 DOI: 10.1016/j.patcog.2025.111568

HaoYu Guo , Ying Li , Chunyan Deng

{"title":"TLR-3DRN: Unsupervised single-view reconstruction via tri-layer renderer","authors":"HaoYu Guo , Ying Li , Chunyan Deng","doi":"10.1016/j.patcog.2025.111568","DOIUrl":"10.1016/j.patcog.2025.111568","url":null,"abstract":"<div><div>Single-view three-dimensional (3D) reconstruction is a challenging task in computer vision, focusing on reconstructing 3D objects from a single image. Existing single-view object reconstruction approaches typically rely on viewpoints, silhouettes, multiple views of the same instance, and strategy-specific priors, which are difficult to obtain in the wild. To address this issue, we propose a novel end-to-end single-view reconstruction method based on a tri-layer renderer, named the Tri-Layer Renderer-based 3D Reconstruction Network (TLR-3DRN). TLR-3DRN recovers 3D structures from original image collections without requiring additional supervision, assumptions, or priors. In particular, TLR-3DRN employs a tri-layer renderer that enables the model to extract more 3D details from unprocessed image data. To obtain an optimizable interlayer, we developed a robust interlayer generation network based on a nonparametric memory bank. Notably, we designed a joint optimization strategy for the overall framework. Additionally, a shape and texture consistency loss based on image–text models is proposed to enhance the optimization process. Owing to the aforementioned proposed modules, TLR-3DRN can achieve high-quality, diverse-category reconstruction under completely unsupervised conditions. TLR-3DRN is validated on synthetic datasets and real-world datasets. Experimental results demonstrate that TLR-3DRN outperforms state-of-the-art unsupervised and two-dimensional supervised methods, achieving performance comparable to 3D supervised methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111568"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576953","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

An efficient approach for finger vein verification to solving the biometric recognition technique

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-05 DOI: 10.1016/j.patcog.2025.111563

Dharmalingam Muthusamy , Rakkimuthu Ponnusamy

{"title":"An efficient approach for finger vein verification to solving the biometric recognition technique","authors":"Dharmalingam Muthusamy , Rakkimuthu Ponnusamy","doi":"10.1016/j.patcog.2025.111563","DOIUrl":"10.1016/j.patcog.2025.111563","url":null,"abstract":"<div><div>Vein authentication is a novel biometric method to authenticate a person's individuality. The conventional biometric technique employs shape images and exact segments of finger veins for the verification process. The proposed deep belief structure model aims to improve verification accuracy using a novel Anisotropic Filtered Stromberg Feature Transform based on Tucker's Congruence Deep Belief Structure Learning (AFSFT-TCDBSL) technique. The main aim of the AFSFT-TCDBSL technique is to improve verification accuracy and minimize time consumption. The proposed AFSFT-TCDBSL technique comprises one input layer, three hidden layers, and one output layer. The numbers of images are collected in the input layer, and the input images are pre-processed using anisotropic diffusion filtering in the first hidden layer. Then the pre-processed input images are sent to the next layer, where the feature extraction process is carried out using the Stromberg wavelet transform. Finally, the verification process is performed using Tucker's congruence correlation coefficient. Based on the correlation, the verification results are obtained at the output layer. In this way, accurate finger vein verification is performed with superior accuracy and with a minimum false rate. We performed experimental assessments with different factors, such as the Peak Signal-to-Noise Ratio (PSNR), Finger Vein Verification Accuracy (FVVA), False Positive Rate (FPR), Processing Time (PT), and Feature Extraction Time (FET). The results of the proposed ADFSFT-TCDBSL technique were conducted on 9% of improved peak signal-to-noise ratio and accuracy with a minimum 59% false positive rate and 16% time as well as 19% feature extraction time than the state-of-the-art FVV methods; therefore, it better facilitates the application of real-time finger vein verification.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111563"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610803","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 generically Contrastive Spatiotemporal Representation Enhancement for 3D skeleton action recognition

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-05 DOI: 10.1016/j.patcog.2025.111521

Shaojie Zhang, Jianqin Yin, Yonghao Dang

{"title":"A generically Contrastive Spatiotemporal Representation Enhancement for 3D skeleton action recognition","authors":"Shaojie Zhang, Jianqin Yin, Yonghao Dang","doi":"10.1016/j.patcog.2025.111521","DOIUrl":"10.1016/j.patcog.2025.111521","url":null,"abstract":"<div><div>Skeleton-based action recognition is a central task in computer vision and human–robot interaction. However, most previous methods suffer from overlooking the explicit exploitation of the latent data distributions (<em>i.e.</em>, the intra-class variations and inter-class relations), thereby leading to confusion about ambiguous samples and sub-optimum solutions of the skeleton encoders. To mitigate this, we propose a <strong>C</strong>ontrastive <strong>S</strong>patiotemporal <strong>R</strong>epresentation <strong>E</strong>nhancement (CSRE) framework to obtain more discriminative representations from the sequences, which can be incorporated into various previous skeleton encoders and can be removed when testing. Specifically, we decompose the representation into spatial- and temporal-specific features to explore fine-grained motion patterns along the corresponding dimensions. Furthermore, to explicitly exploit the latent data distributions, we employ the attentive features to contrastive learning, which models the cross-sequence semantic relations by pulling together the features from the positive pairs and pushing away the negative pairs. Extensive experiments show that CSRE with five various skeleton encoders (HCN, 2S-AGCN, CTR-GCN, Hyperformer, and BlockGCN) achieves solid improvements on five benchmarks. The code will be released at <span><span>https://github.com/zhshj0110/CSRE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111521"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601393","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

Scientific poster generation: A new dataset and approach

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-05 DOI: 10.1016/j.patcog.2025.111507

Xinyi Zhong , Zusheng Tan , Jing Li , Shen Gao , Jing Ma , Shanshan Feng , Billy Chiu

{"title":"Scientific poster generation: A new dataset and approach","authors":"Xinyi Zhong , Zusheng Tan , Jing Li , Shen Gao , Jing Ma , Shanshan Feng , Billy Chiu","doi":"10.1016/j.patcog.2025.111507","DOIUrl":"10.1016/j.patcog.2025.111507","url":null,"abstract":"<div><div>Automating poster creation from research papers saves scientists time. However, training models for this task is challenging due to limited datasets. Moreover, existing methods are mostly rule/template-based, which lack the flexibility to adapt to different content and design requirements in scientific posters. Our contributions aim to address these issues. We introduce <strong>Sci-PosterLayout</strong>, a dataset comprising 1,226 scientific posters with greater variety in <em>content</em>, <em>layout</em> and <em>domains</em>. Using a template-free method with a seq2seq model and <em>Design Pattern Schema</em> (<strong>DPS</strong>), we learn various content and design patterns for poster layout generation. Evaluations against existing methods and datasets show our approach produces high-quality posters with diverse layouts. Our work seeks to advance research in scientific poster generation by building a new dataset and proposing template-free methods that require minimal human intervention. The Sci-PosterLayout dataset will be publicly available at <span><span>https://github.com/kitman0000/Sci-PosterLayout-Data</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111507"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576951","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

Unsupervised group re-identification from aerial perspective via strategic member harmonization

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-05 DOI: 10.1016/j.patcog.2025.111508

Hongxu Chen , Quan Zhang , Xiaohua Xie , Jianhuang Lai

{"title":"Unsupervised group re-identification from aerial perspective via strategic member harmonization","authors":"Hongxu Chen , Quan Zhang , Xiaohua Xie , Jianhuang Lai","doi":"10.1016/j.patcog.2025.111508","DOIUrl":"10.1016/j.patcog.2025.111508","url":null,"abstract":"<div><div>Group re-identification (G-ReID) aims to match group images of the same identity. Existing G-ReID methods perform well on ground-based datasets, but remain unexplored in aerial perspective. One reason is the significant human effort required for aerial associations and the inability of unsupervised methods to address low-quality aerial pedestrian detection and reduced feature visibility. To address these issues, we propose Strategic Member Harmonization. Strategic members are harmonized to complement potential information lost or destroyed due to low-quality detections or significant member variations, thus forming harmonization groups. Harmonization groups introduce a richer layer of the underlying information, mitigating clustering inaccuracies gradually. To address the lack of aerial G-ReID datasets, we construct a new aerial dataset with 10,168 group images and 653 different group identities. Our approach achieves state-of-the-art performance on our dataset and performs well on other ground-based datasets. Our dataset is available at https://github.com/chen1hx/UAV-Group.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111508"},"PeriodicalIF":7.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592923","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

RhythmFormer: Extracting patterned rPPG signals based on periodic sparse attention

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-04 DOI: 10.1016/j.patcog.2025.111511

Bochao Zou , Zizheng Guo , Jiansheng Chen , Junbao Zhuo , Weiran Huang , Huimin Ma

{"title":"RhythmFormer: Extracting patterned rPPG signals based on periodic sparse attention","authors":"Bochao Zou , Zizheng Guo , Jiansheng Chen , Junbao Zhuo , Weiran Huang , Huimin Ma","doi":"10.1016/j.patcog.2025.111511","DOIUrl":"10.1016/j.patcog.2025.111511","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications. Due to the periodicity nature of rPPG signals, the long-range dependency capturing capacity of the transformer was assumed to be advantageous for such signals. However, existing methods have not conclusively demonstrated the superior performance of transformers over traditional convolutional neural networks. This may be attributed to the quadratic scaling exhibited by transformer with sequence length, resulting in coarse-grained feature extraction, which in turn affects robustness and generalization. To address that, this paper proposes a periodic sparse attention mechanism based on temporal attention sparsity induced by periodicity. A pre-attention stage is introduced before the conventional attention mechanism. This stage learns periodic patterns to filter out a large number of irrelevant attention computations, thus enabling fine-grained feature extraction. Moreover, to address the issue of fine-grained features being more susceptible to noise interference, a fusion stem is proposed to effectively guide self-attention towards rPPG features. It can be easily integrated into existing methods to enhance their performance. Extensive experiments show that the proposed method achieves state-of-the-art performance in both intra-dataset and cross-dataset evaluations. The codes are available at <span><span>https://github.com/zizheng-guo/RhythmFormer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111511"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592930","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

S2DiNet: Towards lightweight and fast high-resolution dichotomous image segmentation

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-04 DOI: 10.1016/j.patcog.2025.111506

Shuhan Chen , Haonan Tang , Yuan Huang , Lifeng Zhang , Xuelong Hu

{"title":"S2DiNet: Towards lightweight and fast high-resolution dichotomous image segmentation","authors":"Shuhan Chen , Haonan Tang , Yuan Huang , Lifeng Zhang , Xuelong Hu","doi":"10.1016/j.patcog.2025.111506","DOIUrl":"10.1016/j.patcog.2025.111506","url":null,"abstract":"<div><div>The Dichotomous Image Segmentation task aims to achieve ultra-high precision binary segmentation for category-agnostic objects, including salient, camouflaged, structurally complex, or feature-similar entities. Traditional methods designed for low-resolution inputs produce blurred segmentation, failing to meet such critical safety and stability requirements. Although existing DIS methods achieve high accuracy, they are often parameter-heavy and slow, neglecting practical application needs. To address these challenges, this paper proposes a light-weight and fast framework, aims at improving processing efficiency while ensuring accuracy in high-resolution natural scenes. The proposed method utilizes a shared-weight ResNet-18 backbone to process inputs of different scales. A Feature Synchronization module is employed to enhance the correlation between encoded features of different resolutions. To reduce the parameter and increase the inference speed, the number of feature channels are decreased; however, this also resulted in information loss. The Star Fusion module is introduced to mitigate this issue. Furthermore, a Decoupling and Integration Decoder is adopted to progressively decode and fuse the body, detail, and mask features of the object, enhancing feature decoding accuracy. The proposed model runs at <strong>26.3 FPS</strong> with a <strong>48.7 MB</strong> size, reducing parameters by 72.4% and increasing speed by 30.8% compared to baseline method ISNet, while maintaining superior performance. Moreover, it surpasses several existing high-resolution methods in terms of accuracy.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111506"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563018","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

TextDiff: Enhancing scene text image super-resolution with mask-guided residual diffusion models

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-04 DOI: 10.1016/j.patcog.2025.111513

Baolin Liu , Zongyuan Yang , Chinwai Chiu, Yongping Xiong

{"title":"TextDiff: Enhancing scene text image super-resolution with mask-guided residual diffusion models","authors":"Baolin Liu , Zongyuan Yang , Chinwai Chiu, Yongping Xiong","doi":"10.1016/j.patcog.2025.111513","DOIUrl":"10.1016/j.patcog.2025.111513","url":null,"abstract":"<div><div>The goal of scene text image super-resolution (STISR) is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for STISR. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets, with a maximum improvement of 2.0% in recognition accuracy over existing methods while enhancing the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111513"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636762","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

Learning multi-granularity representation with transformer for visible-infrared person re-identification

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-04 DOI: 10.1016/j.patcog.2025.111510

Yujian Feng , Feng Chen , Guozi Sun , Fei Wu , Yimu Ji , Tianliang Liu , Shangdong Liu , Xiao-Yuan Jing , Jiebo Luo

{"title":"Learning multi-granularity representation with transformer for visible-infrared person re-identification","authors":"Yujian Feng , Feng Chen , Guozi Sun , Fei Wu , Yimu Ji , Tianliang Liu , Shangdong Liu , Xiao-Yuan Jing , Jiebo Luo","doi":"10.1016/j.patcog.2025.111510","DOIUrl":"10.1016/j.patcog.2025.111510","url":null,"abstract":"<div><div>Visible-infrared person re-identification (VI-ReID) aims to match pedestrian images from visible and near-infrared modalities. The pedestrian images of two modalities contain discriminative features in different sizes and positions, <em>e.g.</em>, the global color of the cloth, the body’s local pose, and the shoe’s pixel size. However, existing methods mainly capture features at a single granularity, ignoring multi-granularity information contributing to pedestrian identification. Therefore, we propose a cross-modality multi-granularity Transformer (CM<sup>2</sup>GT) framework to solve this issue. CM<sup>2</sup>GT learns coarse-to-fine feature representations and integrates discriminative information across various granularities, which alleviates problems of the irrelevant matching and ambiguous alignment caused by matching single granularity features. Specifically, we first design a multi-granularity feature extractor (MGFE) module based on Transformer to capture the global-patch-pixel level features of each modality, which can flexibly represent semantic information at multiple scales. Secondly, a multi-granularity fusion Transformer (MGFT) module mines the hierarchical relationships between multi-granularity features by a saliency-enhanced Transformer, which ensures the identity-wise saliency consistency across different granularities and modalities. Furthermore, to further enhance cross-modality intra-class clustering in latent space, we design a cross-modality nearest-neighbor clustering (CNC) loss function to minimize the distance between the anchor sample and its cross-modality nearest neighbor. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111510"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552625","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

TSTKD: Triple-spike train kernel-driven supervised learning algorithm

IF 7.5 1区计算机科学

Pattern Recognition Pub Date : 2025-03-04 DOI: 10.1016/j.patcog.2025.111525

Guojun Chen , Guoen Wang

{"title":"TSTKD: Triple-spike train kernel-driven supervised learning algorithm","authors":"Guojun Chen , Guoen Wang","doi":"10.1016/j.patcog.2025.111525","DOIUrl":"10.1016/j.patcog.2025.111525","url":null,"abstract":"<div><div>Precise artificial intelligence is one of the most promising research fields, where supervised learning for spiking neurons (SNs) plays an imperative and fundamental role. This study proposes a novel supervised learning algorithm based on triple-spike train kernels to address the shortcomings of the latest learning algorithms, such as local best learning and low learning accuracy. First, we divided the time intervals of the spike trains, including the firing time of the input spikes. Subsequently, we discovered and analyzed the relationship between the firing times of all spikes, added a third spike to solve the existing problem, and constructed a triple-spike-driven (TSD) minimum direct computational unit. In addition to the simple and efficient adjustment of synaptic weights based on pair-spike, TSD maintains a relationship between all useful spikes to approximate the global best learning. Finally, we proposed a triple-spike train kernel driven (TSTKD) supervised learning algorithm to improve the learning performance. Many fundamental experiments were implemented to demonstrate the learning performance, which proved that the successful learning ability and some learning factors of our proposed algorithm in spike train learning. We then verified the positive effect of the TSD on the proposed algorithm. Many experiments also proved the much higher learning accuracy of the proposed state-of-the-art algorithm compared to some of the latest algorithms, especially in the complex spike train learning. In addition, the proposed algorithm is more adaptive to SNs and much better at generalizing, memorizing, and classifying than the corresponding algorithm with pair-spike and some of the latest algorithms. Considering the above experimental results, our study blazes a trail for pattern recognition using spike train supervised learning with global optimization.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"164 ","pages":"Article 111525"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592808","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