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Two stream CNN for muzzle print recognition using gabor filters

Multimedia Tools and Applications 2025年 1-17页

作者： Russel, Newlin Shebiah Selvaraj, Arivazhagan Centre for Image Processing and Pattern Recognition Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India

Muzzle print recognition, an essential aspect of biometric identification for animals, has gained significant attention in recent years. This study introduces a novel method for enhancing the precision and robustness of muzzle print identification through the utilization of a Two-Stream Convolutional Neural Network (CNN) integrated with Gabor Filters. The proposed Two-Stream CNN architecture comprises of two distinct streams, the Spatial Stream, which processes raw muzzle images, and the Texture Stream, dedicated for extracting texture features through Gabor Filters. By integrating feature maps from both streams, the model effectively utilizes the synergistic advantages of spatial and texture information, thereby augmenting the discriminative capabilities of the recognition system. The Gabor Filters applied in the Texture Stream enable the extraction of intricate texture details, making the model highly sensitive to variations in muzzle patterns. By conducting training and fine-tuning procedures on animal muzzle prints in the Beef Cattle Muzzle/Nose print database for individual identification, a recognition accuracy of 98.99% was achieved. Additional performance metrics include a recall of 98.36%, precision of 98.39%, specificity of 100% and F1-Score of 98.18%. The ability of the proposed model to accurately differentiate intricate variations in muzzle prints presents significant prospects for diverse fields of application, such as wildlife preservation, animal monitoring, and security measures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gabor filters

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Robust penta-modal biometric identification through deep learning and weighted score fusion

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Iran Journal of Computer Science 2025年 1-17页

作者： Selvaraj, Arivazhagan Russel, Newlin Shebiah Seenivasan, Mridula Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

Recent advancements in biometric security systems, which are designed to reliably verify an individual’s identity, have garnered significant attention in the fields of security and access control. However, traditional unimodal biometric systems encounter several challenges, including intra-class variability, vulnerability to spoofing, and noise interference, which can undermine their effectiveness. To overcome these limitations, this study introduces a novel penta-modal biometric recognition system that integrates five distinct biometric traits: iris, fingerprint, palm print, ear, and face. The proposed system employs a weighted similarity approach, utilizing Local Binary pattern (LBP) feature maps that are extracted from query images using a Convolutional Neural Network (CNN). These extracted features are then compared against enrolled templates stored in a database. The operation of the system follows a structured five-step process: (1) pre-processing of biometric data, (2) extraction and classification of LBP feature maps via CNN, (3) score computation for each biometric modality, (4) weighted score-level fusion, and (5) final authentication. The performance of the penta-modal biometric system was rigorously evaluated using various public benchmark datasets, including CASIA-FingerprintV5, CASIA-FaceV5, CASIA-PalmprintV1, CASIA-Iris-V3, the AMI ear dataset, and the MEPCO multimodal dataset, as well as synthetically generated multimodal databases. The system achieved an impressive accuracy rate of 99.85% on the virtual databases, demonstrating significantly enhanced security and reliability compared to traditional unimodal systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： CASIA database Convolutional neural network (CNN) Local binary pattern (LBP) Penta-modal Penta-modal Score-level fusion

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Enhancing Foundation Model Robustness for Multi-center Real-World Medical image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical images, CARE 2024 Held in Conjunction with 27th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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Automatic Weight Allocation: optimizing remote sensing image retrieval from contrastive learning perspective

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Multimedia Systems 2025年第3期31卷 1-19页

作者： Wang, Sijia Ge, Yun Liu, Qiyang Zeng, Yan School of Software Nanchang Hangkong University Jiangxi Nanchang330000 China Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Jiangxi Nanchang330063 China

Traditional supervised learning methods achieve remarkable performance in high-resolution remote sensing image retrieval, but are limited by the dependence on large-scale annotated images. Contrastive learning can leverage unlabeled images to learn powerful visual features, demonstrating its potential in many unsupervised tasks. Moreover, hash algorithms show significant potential in the field of image retrieval with their advantages in efficiency and storage. Therefore, we propose the Contrastive Hashing Framework based on Automatic Weight Allocation. The framework employs a two-stage training strategy. In the feature learning stage, we propose the Automatic Weighted Contrastive Loss (AWCLoss). It incorporates Gaussian weighting and dynamic adjustment strategies to improve loss functions, enabling them to focus on the distinctiveness and importance of samples. Gaussian weighting assigns different weight values based on the similarity of sample pairs, enhancing the learning of critical sample pairs. Meanwhile, the dynamic adjustment strategy sets a threshold to identify hard negative samples and then adjusts the weight values to weaken the model from being disturbed by hard negative samples. In the hashing learning stage, a hashing layer is added to the end of the network, which converts high-dimensional representations into hash codes. The Quantization loss is introduced to learn the hash codes so that the semantic similarity structure between data can be preserved in hamming space. Additionally, the AWCLoss is utilized to enhance the discriminative power of the hash codes. Extensive experiments on three remotely sensed datasets UCM, AID and NWPU-RESISC45 have demonstrated the significant superiority of our approach in remote sensing image retrieval. Our source code is available at https://***/WANGSJ77/AWCH. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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ClusVPR: Efficient Visual Place recognition With Clustering-Based Weighted Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1038-1049页

作者： Xu, Yifan Shamsolmoali, Pourya Zareapoor, Masoume Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China East China Normal University School of Communication Shanghai200240 China University of York YorkYO10 5DD United Kingdom

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficient attention to small objects in complex scenes, resulting in recognition deviations. In this article, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on convolutional neural networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called clustering-based weighted transformer network (CWTNet). CWTNet uses the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer, which significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid self-supervised strategy focuses on extracting representative and diverse features from scale-wise image patches rather than from entire images. This approach is essential for capturing a broader range of information required for robust VPR. Extensive experiments on four VPR datasets show our model's superior performance compared to existing models while being less complex. © 2024 IEEE.

关键词： Self-supervised learning

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Contour detection network simulating the primary visual pathway

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Digital Signal processing: A Review Journal 2025年 165卷

作者： Chen, Ke Fan, Yingle Fang, Tao Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Zhejiang Hangzhou310018 China

Biological vision exhibits exceptional contour perception capabilities. In view of this, research on contour detection guided by biological vision is gradually gaining attention. Inspired by the transmission and processing of visual signals in the primary visual pathway, this study proposes a lightweight contour detection network called the Primary-Visual-Pathway UNet (PVP-UNet), comprising an encoder and a decoder. Primarily, inspiration from the binocular vision mechanism, we transmit the original image through dual deformable convolution modules that simulate the receptive fields of the left and right retinas in the encoder. Utilizing the characteristic of the optic chiasm, the output features of the retinal layers are split, swapped, and merged for further processing. Subsequently, dilated convolution modules and normal convolution modules are involved to simulate the magnocellular (M) and parvocellular (P) pathways of the lateral geniculate nucleus (LGN), respectively. An inhibition module was designed based on the suppression mechanism of classical/non-classical receptive fields (CRF/NCRF) in the primary visual cortex (V1). Additionally, the interconnection pattern of the inhibition modules is deployed by leveraging the aggregation characteristics of simple cells to complex cells in the V1 layer. Inspired by the feedback mechanism of visual information, the feature fusion module is introduced in the decoder to integrate features from different encoding layers in the reverse direction of signal transmission along the primary visual pathway. Our experiments show that the respective Optimal Dataset Scale (ODS) scores achieve 0.811, 0.756, and 0.896 on BSDS500, NYUD, and BIPED datasets. The experimental results demonstrate that the proposed network effectively suppresses background noise and highlights primary contours, exhibiting excellent detection performance on the tested datasets. The code is available at https://***/k3chencoco/PVP-UNet. © 2025 Elsevie

关键词： Binocular vision

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Reflecting topology consistency and abnormality via learnable attentions for airway labeling

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International Journal of Computer Assisted Radiology and Surgery 2025年 1-9页

作者： Li, Chenyu Zhang, Minghui Zhang, Chuyan Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Shanghai Jiao Tong University Shanghai China

Purpose: Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway labeling is challenging due to significant anatomical variations. Previous methods are prone to generate inconsistent predictions, hindering preoperative planning and intraoperative navigation. This paper aims to enhance topological consistency and improve the detection of abnormal airway branches. Methods: We propose a transformer-based framework incorporating two modules: the soft subtree consistency (SSC) and the abnormal branch saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous features aggregation between normal and abnormal nodes. Results: Evaluated on a challenging dataset characterized by severe airway deformities, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains an 83.7% subsegmental accuracy, along with a 3.1% increase in segmental subtree consistency, a 45.2% increase in abnormal branch recall. Notably, the method demonstrates robust performance in cases with airway deformities, ensuring consistent and accurate labeling. Conclusion: The enhanced topological consistency and robust identification of abnormal branches provided by our method offer an accurate and robust solution for airway labeling, with potential to improve the precision and safety of bronchoscopy procedures. © CARS 2025.

关键词： Airway anatomical labeling Anomaly detection Structural prior Transformer

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Quaternion CNN With Salient Features for Color image Denoising

Quaternion CNN With Salient Features for Color Image Denoisi...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yi Liu Qiyu Jin Jie Yang School of Mathematical Science Inner Mongolia University Hohhot China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep convolutional neural networks have significantly advanced color image denoising. However, existing models often apply grayscale denoising techniques to color images without accounting for inter-channel correlations, resulting in color distortion, detail loss, and visual artifacts. Moreover, these models frequently neglect salient features within convolutional maps. To address these issues, we propose a quaternion CNN model that captures channel correlations and extracts salient features, thereby enhancing color image denoising performance. Specifically, we convert color images into quaternion matrices to better capture these correlations and design a quaternion convolutional network to learn relevant features. Furthermore, an aggregated feature block is introduced to enhance the extraction of salient features and further refine the denoising process. Experimental results on multiple datasets demonstrate that the proposed model achieves superior performance compared to recent state-of-the-art methods.

关键词： Visualization Correlation Acoustic distortion image color analysis Convolution Quaternions Noise reduction Color Feature extraction Convolutional neural networks

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Stock Investor Sentiment Analysis Based on NLP 1st

Stock Investor Sentiment Analysis Based on NLP

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1st International Conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Jingxin Deng, Chao Yang, Jie Shanghai Normal University Tianhua College AI School Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819633487

This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural language processing (NLP), we gather and categorize investor comments from stock forums pertinent to the Shanghai and Shenzhen Indices. Utilizing Support Vector Machines and Logistic Regression within an NLP framework, we develop an automated system that transforms comments into sentiment indicators. These indicators are further quantified into daily sentiment indices following denoising procedures to emphasize persistent market trends. Comparative assessments reveal strong correlations between refined sentiment indices and actual stock index movements, confirming their predictive power for market fluctuations. The study offers investors a sentiment-analytics tool, augmenting the multi-faceted approach to investment strategy formulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Web crawler

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