Pattern Analysis and Applications最新文献_第5页

A three-dimensional extension of the slope chain code: analyzing the tortuosity of the flagellar beat of human sperm 斜链代码的三维扩展：分析人类精子鞭毛搏动的曲折性

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-28 DOI: 10.1007/s10044-024-01286-9

Andrés Bribiesca-Sánchez, Adolfo Guzmán, Fernando Montoya, Dan S. Díaz-Guerrero, Haydeé O. Hernández, Paul Hernández-Herrera, Alberto Darszon, Gabriel Corkidi, Ernesto Bribiesca

{"title":"A three-dimensional extension of the slope chain code: analyzing the tortuosity of the flagellar beat of human sperm","authors":"Andrés Bribiesca-Sánchez, Adolfo Guzmán, Fernando Montoya, Dan S. Díaz-Guerrero, Haydeé O. Hernández, Paul Hernández-Herrera, Alberto Darszon, Gabriel Corkidi, Ernesto Bribiesca","doi":"10.1007/s10044-024-01286-9","DOIUrl":"https://doi.org/10.1007/s10044-024-01286-9","url":null,"abstract":"In the realm of 3D image processing, accurately representing the geometric nuances of line curves is crucial. Building upon the foundation set by the slope chain code, which adeptly represents intricate two-dimensional curves using an array capturing the exterior angles at each vertex, this study introduces an innovative 3D encoding method tailored for polygonal curves. This 3D encoding employs parallel slope and torsion chains, ensuring invariance to common transformations like translations, rotations, and uniform scaling, while also demonstrating robustness against mirror imaging and variable starting points. A hallmark feature of this method is its ability to compute tortuosity, a descriptor of curve complexity or winding nature. By applying this technique to biomedical engineering, we delved into the flagellar beat patterns of human sperm. These insights underscore the versatility of our 3D encoding across diverse computer vision applications.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"46 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505886","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

Fine grained dual level attention mechanisms with spacial context information fusion for object detection 细粒度双层注意力机制与空间上下文信息融合用于物体检测

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-28 DOI: 10.1007/s10044-024-01290-z

Haigang Deng, Chuanxu Wang, Chengwei Li, Zhang Hao

{"title":"Fine grained dual level attention mechanisms with spacial context information fusion for object detection","authors":"Haigang Deng, Chuanxu Wang, Chengwei Li, Zhang Hao","doi":"10.1007/s10044-024-01290-z","DOIUrl":"https://doi.org/10.1007/s10044-024-01290-z","url":null,"abstract":"For channel and spatial feature map C×W×H in object detection task, its information fusion usually relies on attention mechanism, that is, all C channels and the entire space W×H are all compressed respectively via average/max pooling, and then their attention weight masks are obtained based on correlation calculation. This coarse-grained global operation ignores the differences among multiple channels and diverse spatial regions, resulting in inaccurate attention weights. In addition, how to mine the contextual information in the space W×H is also a challenge for object recognition and localization. To this end, we propose a Fine-Grained Dual Level Attention Mechanism joint Spacial Context Information Fusion module for object detection (FGDLAM&SCIF). It is a cascaded structure, firstly, we subdivide the feature space W×H into n (optimized as n = 4 in experiments) subspaces and construct a global adaptive pooling and one-dimensional convolution algorithm to effectively extract the feature channel weights on each subspace respectively. Secondly, the C feature channels are divided into n (n = 4) sub-channels, and then a multi-scale module is constructed in the feature space W×H to mine context information. Finally, row and column coding is used to fuse them orthogonally to obtain enhanced features. This module is embeddable, which can be transplanted into any object detection network, such as YOLOv4/v5, PPYOLOE, YOLOX and MobileNet, ResNet as well. Experiments are conducted on the MS COCO 2017 and Pascal VOC 2007 datasets to verify its effectiveness and good portability.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"108 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517459","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 deep graph kernel-based time series classification algorithm 基于深度图核的时间序列分类算法

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-26 DOI: 10.1007/s10044-024-01292-x

Mengping Yu, Huan Huang, Rui Hou, Xiaoxuan Ma, Shuai Yuan

{"title":"A deep graph kernel-based time series classification algorithm","authors":"Mengping Yu, Huan Huang, Rui Hou, Xiaoxuan Ma, Shuai Yuan","doi":"10.1007/s10044-024-01292-x","DOIUrl":"https://doi.org/10.1007/s10044-024-01292-x","url":null,"abstract":"Time series data are sequences of values that are obtained by sampling a signal at a fixed frequency, and time series classification algorithms distinguish time series into different categories. Among many time series classification algorithms, subseries-based algorithms have received widespread attention because of their high accuracy and low computational complexity. However, subseries-based algorithms consider the similarity of subseries only by shape and ignore semantic similarity. Therefore, the purpose of this paper is to determine how to solve the problem that subseries-based time series classification algorithms ignore the semantic similarity between subseries. To address this issue, we introduce the deep graph kernel technique to capture the semantic similarity between subseries. To verify the performance of the method, we test the proposed algorithm on publicly available datasets from the UCR repository and the experimental results prove that the deep graph kernel has an important role in enhancing the accuracy of the algorithm and that the proposed algorithm performs quite well in terms of accuracy and has a considerable advantage over other representative algorithms.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"145 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529259","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

Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel 吸烟-YOLOv8：针对化工厂员工的新型吸烟检测算法

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-24 DOI: 10.1007/s10044-024-01288-7

Zhong Wang, Yi Liu, Lanfang Lei, Peibei Shi

{"title":"Smoking-YOLOv8: a novel smoking detection algorithm for chemical plant personnel","authors":"Zhong Wang, Yi Liu, Lanfang Lei, Peibei Shi","doi":"10.1007/s10044-024-01288-7","DOIUrl":"https://doi.org/10.1007/s10044-024-01288-7","url":null,"abstract":"This study aims to address the challenges of detecting smoking behavior among workers in chemical plant environments. Smoking behavior is difficult to discern in images, with the cigarette occupying only a small pixel area, compounded by the complex background of chemical plants. Traditional models struggle to accurately capture smoking features, leading to feature loss, reduced recognition accuracy, and issues like false positives and missed detections. To overcome these challenges, we have developed a smoking behavior recognition method based on the YOLOv8 model, named Smoking-YOLOv8. Our approach introduces an SD attention mechanism that focuses on the smoking areas within images. By aggregating information from different positions through weighted averaging, it effectively manages long-distance dependencies and suppresses irrelevant background noise, thereby enhancing detection performance. Furthermore, we utilize Wise-IoU as the regression loss for bounding boxes, along with a rational gradient distribution strategy that prioritizes samples of average quality to improve the model’s precision in localization. Finally, the introduction of SPPCSPC and PConv modules in the neck section of the network allows for multi-faceted feature extraction from images, reducing redundant computation and memory access, and effectively extracting spatial features to balance computational load and optimize network architecture. Experimental results on a custom dataset of smoking behavior in chemical plants show that our model outperforms the standard YOLOv8 model in mean Average Precision (mAP@0.5) by 6.18%, surpassing other mainstream models in overall performance.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"56 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505859","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

3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information 3D-Mol：利用三维信息进行分子特性预测的新型对比学习框架

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-21 DOI: 10.1007/s10044-024-01287-8

Taojie Kuang, Yiming Ren, Zhixiang Ren

{"title":"3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information","authors":"Taojie Kuang, Yiming Ren, Zhixiang Ren","doi":"10.1007/s10044-024-01287-8","DOIUrl":"https://doi.org/10.1007/s10044-024-01287-8","url":null,"abstract":"Molecular property prediction, crucial for early drug candidate screening and optimization, has seen advancements with deep learning-based methods. While deep learning-based methods have advanced considerably, they often fall short in fully leveraging 3D spatial information. Specifically, current molecular encoding techniques tend to inadequately extract spatial information, leading to ambiguous representations where a single one might represent multiple distinct molecules. Moreover, existing molecular modeling methods focus predominantly on the most stable 3D conformations, neglecting other viable conformations present in reality. To address these issues, we propose 3D-Mol, a novel approach designed for more accurate spatial structure representation. It deconstructs molecules into three hierarchical graphs to better extract geometric information. Additionally, 3D-Mol leverages contrastive learning for pretraining on 20 million unlabeled data, treating their conformations with identical topological structures as weighted positive pairs and contrasting ones as negatives, based on the similarity of their 3D conformation descriptors and fingerprints. We compare 3D-Mol with various state-of-the-art baselines on 7 benchmarks and demonstrate our outstanding performance.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"80 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505888","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

Low-complexity arrays of patch signature for efficient ancient coin retrieval 用于高效古钱币检索的低复杂度补丁签名阵列

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-19 DOI: 10.1007/s10044-024-01284-x

Florian Lardeux, Petra Gomez-Krämer, Sylvain Marchand

引用次数: 0

Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study 不平衡数据下 COVID-19 胸部 X 光片分类的少量学习：域间与域内研究

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-06-11 DOI: 10.1007/s10044-024-01285-w

Alejandro Galán-Cuenca, Antonio Javier Gallego, Marcelo Saval-Calvo, Antonio Pertusa

{"title":"Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study","authors":"Alejandro Galán-Cuenca, Antonio Javier Gallego, Marcelo Saval-Calvo, Antonio Pertusa","doi":"10.1007/s10044-024-01285-w","DOIUrl":"https://doi.org/10.1007/s10044-024-01285-w","url":null,"abstract":"Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"82 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505889","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

ErfReLU: adaptive activation function for deep neural network ErfReLU：深度神经网络的自适应激活函数

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-05-29 DOI: 10.1007/s10044-024-01277-w

Ashish Rajanand, Pradeep Singh

{"title":"ErfReLU: adaptive activation function for deep neural network","authors":"Ashish Rajanand, Pradeep Singh","doi":"10.1007/s10044-024-01277-w","DOIUrl":"https://doi.org/10.1007/s10044-024-01277-w","url":null,"abstract":"Recent research has found that the activation function (AF) plays a significant role in introducing non-linearity to enhance the performance of deep learning networks. Researchers recently started developing activation functions that can be trained throughout the learning process, known as trainable, or adaptive activation functions (AAF). Research on AAF that enhances the outcomes is still in its early stages. In this paper, a novel activation function ‘ErfReLU’ has been developed based on the erf function and ReLU. This function leverages the advantages of both the Rectified Linear Unit (ReLU) and the error function (erf). A comprehensive overview of activation functions like Sigmoid, ReLU, Tanh, and their properties have been briefly explained. Adaptive activation functions like Tanhsoft1, Tanhsoft2, Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and Serf is also presented. Lastly, comparative performance analysis of 9 trainable activation functions namely Tanhsoft1, Tanhsoft2, Tanhsoft3, TanhLU, SAAF, ErfAct, Pserf, Smish, and Serf with the proposed one has been performed. These activation functions are used in MobileNet, VGG16, and ResNet models and their performance is evaluated on benchmark datasets such as CIFAR-10, MNIST, and FMNIST.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"34 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195554","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

MAC: a meta-learning approach for feature learning and recombination MAC：用于特征学习和重组的元学习方法

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-05-14 DOI: 10.1007/s10044-024-01271-2

Sambhavi Tiwari, Manas Gogoi, Shekhar Verma, Krishna Pratap Singh

{"title":"MAC: a meta-learning approach for feature learning and recombination","authors":"Sambhavi Tiwari, Manas Gogoi, Shekhar Verma, Krishna Pratap Singh","doi":"10.1007/s10044-024-01271-2","DOIUrl":"https://doi.org/10.1007/s10044-024-01271-2","url":null,"abstract":"Optimization-based meta-learning aims to learn a meta-initialization that can adapt quickly a new unseen task within a few gradient updates. Model Agnostic Meta-Learning (MAML) is a benchmark meta-learning algorithm comprising two optimization loops. The outer loop leads to the meta initialization and the inner loop is dedicated to learning a new task quickly. ANIL (almost no inner loop) algorithm emphasized that adaptation to new tasks reuses the meta-initialization features instead of rapidly learning changes in representations. This obviates the need for rapid learning. In this work, we propose that contrary to ANIL, learning new features may be needed during meta-testing. A new unseen task from a non-similar distribution would necessitate rapid learning in addition to the reuse and recombination of existing features. We invoke the width-depth duality of neural networks, wherein we increase the width of the network by adding additional connection units (ACUs). The ACUs enable the learning of new atomic features in the meta-testing task, and the associated increased width facilitates information propagation in the forward pass. The newly learned features combine with existing features in the last layer for meta-learning. Experimental results confirm our observations. The proposed MAC method outperformed the existing ANIL algorithm for non-similar task distribution by (approx) 12% (5-shot task setting).\u0000","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"29 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942553","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

BNPSIW: BRBS-based NSST-PZMs domain statistical image watermarking BNPSIW：基于 BRBS 的 NSST-PZMs 域统计图像水印技术

IF 3.9 4区计算机科学

Pattern Analysis and Applications Pub Date : 2024-05-13 DOI: 10.1007/s10044-024-01274-z

Panpan Niu, Yinghong He, Wei Guo, Xiangyang Wang

{"title":"BNPSIW: BRBS-based NSST-PZMs domain statistical image watermarking","authors":"Panpan Niu, Yinghong He, Wei Guo, Xiangyang Wang","doi":"10.1007/s10044-024-01274-z","DOIUrl":"https://doi.org/10.1007/s10044-024-01274-z","url":null,"abstract":"Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using bivariate Birnbaum–Saunders (BRBS) distribution model, we present a statistical image watermark scheme in nonsubsampled shearlet transform (NSST)-pseudo Zernike moments (PZMs) magnitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and extraction. NSST is firstly performed on host image to obtain the frequency subbands, and the NSST subbands are divided into non overlapping blocks. Then, the significant high-entropy NSST domain blocks are selected. Meanwhile, for each selected NSST coefficient block, PZMs are calculated to obtain the NSST-PZMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSST-PZMs. In order to decode accurately watermark signal, the statistical characteristics of NSST-PZMs magnitudes are analyzed in detail. Then, NSST-PZMs magnitudes are described statistically by BRBS distribution, which can simultaneously capture the marginal distribution and strong dependencies of NSST-PZMs magnitudes. Also, BRBS statistical model parameters are estimated accurately by modified closed-form maximum likelihood estimator (MML). Finally, a statistical watermark decoder based on BRBS distribution and maximum likelihood (ML) decision rule is developed in NSST-PZMS magnitude hybrid domain. Extensive experimental results show the superiority of the proposed image watermark decoder over some state-of-the-art statistical watermarking methods and deep learning approaches.","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"64 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140927570","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