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VibeGait: Enhancing Structural-Vibration based Gait Recognition using vision

VibeGait: Enhancing Structural-Vibration based Gait Recognit...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chakraborty, Mainak Chandan Mukhopadhyay, Bodhibrata Anchal, Sahil Kar, Subrat Indian Institute of Technology Delhi Delhi India Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9798350368741

Structural vibration-based gait recognition has emerged as a promising soft-biometric modality, particularly for privacy-sensitive monitoring and access control. Despite its potential, current research is largely limited to proof-of-concept studies that rely on hand-crafted features, with minimal exploration of deep learning methodologies. This gap reduces the potential for integrating structural vibration-based gait recognition with existing modalities, such as camera-based systems. In this study, we propose a multi-modal gait recognition system that integrates both vision and structural vibration modalities. We address two key challenges: (a) lack of studies exploring outdoor gait recognition using both vision and structural vibration, and (b) absence of a multi-modal training scheme that combines these two modalities. To tackle the first challenge, we curated a dataset comprising five minutes of walking data from ten individuals captured simultaneously by two cameras and a geophone sensor. To address the second challenge, we developed a joint training framework that uses data from both modalities. Our methods achieve an accuracy of 96.03% (±1.12) using structural vibration signals alone, and this improves to 98.27% (±0.06) when both modalities are combined. © 2025 IEEE.

关键词： Multi-Modal Person Identification Soft-biometrics Structural Vibration

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GLoG-CSUnet: Enhancing vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Ra...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zarch, Niloufar Eghbali Bagher-Ebadian, Hassan Alhanai, Tuka Ghassemi, Mohammad M. Computer Science Department Michigan State University East Lansing United States Department of Radiation Oncology Henry Ford Health Detroit United States Division of Engineering New York University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

vision Transformers (ViTs) have shown promise in medical image semantic segmentation (MISS) by capturing long-range correlations. However, ViTs often struggle to model local spatial information effectively, which is essential for accurately segmenting fine anatomical details, particularly when applied to small datasets without extensive pre-training. We introduce Gabor and Laplacian of Gaussian Convolutional Swin Network (GLoG-CSUnet), a novel architecture enhancing Transformer-based models by incorporating learnable radiomic features. This approach integrates dynamically adaptive Gabor and Laplacian of Gaussian (LoG) filters to capture texture, edge, and boundary information, enhancing the feature representation processed by the Transformer model. Our method uniquely combines the long-range dependency modeling of Transformers with the texture analysis capabilities of Gabor and LoG features. Evaluated on the Synapse multi-organ and ACDC cardiac segmentation datasets, GLoG-CSUnet demonstrates significant improvements over state-of-the-art models, achieving a 1.14% increase in Dice score for Synapse and 0.99% for ACDC, with minimal computational overhead (only 15 and 30 additional parameters, respectively). GLoG-CSUnet's flexible design allows integration with various base models, offering a promising approach for incorporating radiomics-inspired feature extraction in Transformer architectures for medical image analysis. The code implementation is available on GitHub at: https://***/HAAIL/GLoGCSUnet. © 2025 IEEE.

关键词： Gabor Filter Laplacian of Gaussian Medical Image Segmentation Radiomics vision Transformer

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning vision-Language Models Zero-shot Anomaly Detection

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RWKVMatch: vision RWKV-based Multi-scale Feature Matching Network for Unsupervised Deformable Medical Image Registration

RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Ne...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： He, Zixuan Tang, Jing Zhao, Zitong Gong, Zeyu School of Future Technology Huazhong University of Science and Technology Hubei Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368741

Medical image registration is essential for integrating information from diverse imaging modalities for clinical diagnosis and treatment planning. Despite significant advancements, achieving efficient and precise deformable image registration remains a formidable challenge. In this study, we propose a novel medical image registration model, RWKVMatch, which employs global attention and cross-fusion mechanism based on the vision-RWKV module to address complex deformations in medical images effectively. Additionally, the elastic transformation from data augmentation techniques is integrated into the model architecture to enhance its capability to handle multi-scale features and improve robustness to geometric variations in image registration. Experimental evaluations on two medical image registration datasets indicate the effectiveness of our approach, surpassing existing state-of-the-art methods in terms of registration accuracy and computational efficiency. These findings underscore the potential of RWKVMatch as a highly effective tool for medical image registration. © 2025 IEEE.

关键词： deformable image registration elastic transformation multi-scale feature fusion Vison-RWKV

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Joint Underwater Depth Estimation and Dehazing from a Single Image Using Attention U-Net 18th

Joint Underwater Depth Estimation and Dehazing from a Sin...

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18th international Workshop on Design and Architecture for signal and Image processing, DASIP 2025

作者： Nazir, Saqib Asiyabi, Reza Mohammadi Lezoray, Olivier Normandie Univ UNICAEN ENSICAEN CNRS GREYC Caen France School of GeoSciences The University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9783031878961

Underwater imaging presents unique challenges compared to open-air photography, primarily due to diminished visibility and geometric distortions, impeding the development of underwater Computer vision (CV) and robotic vision perception. Previous methods relying on simplified image formation models for image enhancement have often yielded unsatisfactory results. This paper proposes a new deep learning-based architecture for joint depth estimation and dehazing from a single underwater monocular image, seeking to take advantage of the mutual benefits between these two interrelated tasks. The proposed architecture is a Two-Headed Depth Estimation and Dehazing Attention Network (2HDED:AttN) with an end-to-end training approach. Comprehensive experiments on synthetic and real underwater datasets showcase the proposed architecture’s superior performance in jointly addressing underwater depth estimation and image dehazing tasks. The method effectively estimates underwater depth and improves underwater image quality, paving the way for enhanced underwater computer and robotic vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： robot vision

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KABON: Knowledge Aggregation with vision-Language Model for Black-Box Open-Set Domain Adaptation

KABON: Knowledge Aggregation with Vision-Language Model for ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zeng, Zhixin Zhang, Yusen Wang, Ji College of Computer Science and Technology National University of Defense Technology China

ISBN: (纸本)9798350368741

In this paper, we aim to tackle the challenging Black-Box Open-Set Domain Adaptation (BB-OSDA) task. BB-OSDA enables conducting Open-Set Domain Adaptation (OSDA) with solely a black-box source model, broadening the application scope of OSDA. Inspired by the significant success of pre-trained large vision-language (ViL) models in various applications, we propose a novel method, termed Knowledge Aggregation for Black-box Open-set domain adaptatioN (KABON), which leverages the power of ViL models to solve the BB-OSDA problem. Specifically, we first devise a novel knowledge aggregation approach to harness both the generic knowledge from the ViL model and the task-specific knowledge from the black-box source model. Subsequently, we utilize a Gaussian Mixture Model (GMM) with entropy criterion to divide samples of target domain into shared or novel classes. Furthermore, a self-correction strategy is proposed to refine the division of shared and novel classes. Finally, we leverage the divided samples through entropy min-max learning to simultaneously achieve shared classes adaptation and novel classes detection. Experiments conducted on multiple benchmark datasets demonstrate the effectiveness of our proposed method. © 2025 IEEE.

关键词： Black-box domain adaptation Novel classes Open-set domain adaptation

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Exploring Graph-aware Reasoning and Bidirectional Selection for vision-Language Navigation

Exploring Graph-aware Reasoning and Bidirectional Selection ...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhou, Dongming Deng, Jinsheng Pang, Zhengbin Li, Wei School of Computer Science National University of Defense Technology Deya Road Hunan Changsha410003 China College of Advanced Interdisciplinary Studies National University of Defense Technology Hunan Changsha410073 China School of Computer and Electronic Information Guangxi University University Road Guangxi Nanning530004 China

ISBN: (纸本)9798350368741

Inspired by structured state space models and graph neural network modeling, we proposes a novel graph-aware reasoning (GAR) model to effectively solve the problem between memory utilization efficiency and reasoning navigation. First, we introduce graph networks into the navigation framework to enhance the model modeling ability for long sequence dependencies. It is integration graph neural networks into the state space to ensures that the agent can accurately find topological paths based on instructions and environmental interactions. Then, we design a bidirectional selective state space model to enhance the agent-aware of spatial information. When without relying on the attention network, we capture the context information in the image through position embedding. Finally, we fuison the features in the bidirectional state space and effectively compresses and improves the fine-grained image features by a residual connection methods. Our experimental results on R2R and REVERIE datasets show that GAR reduces GPU memory usage by 4.45% compared with the DUET baseline model. © 2025 IEEE.

关键词： Graph Neural Network Multi-modal Reasoning State Space Model vision-language Navigation

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Research on image optimization of graphic design based on 3D laser vision technology 2

Research on image optimization of graphic design based on 3D...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yang, Dianqing Liu, Renjing Visual Design College Keimyung University Dalseo-gu Daegu704-701 Korea Republic of

ISBN: (纸本)9781510689053

To prevent image distortion, this paper explores methods for enhancing and optimizing graphic design images using 3D laser vision technology. The process involves collecting graphic design image data, mapping 3D laser information onto the surface of the image to form an information map, and utilizing this data for locally weighted complexity processing. A histogram of the graphic design image is constructed, followed by scanning and grayscale transformation to eliminate noise. The image histogram is then segmented using a block-based local enhancement method to achieve the optimization and enhancement of the graphic design image. Experimental results show that under different noise standard deviations, the average peak signal-to-noise ratio (PSNR) of the proposed method is 36.62 dB, and the average structural similarity index (SSIM) is 0.9526. This method effectively reduces noise and enhances the visual quality of graphic design images, improving the ability of the human eye to discriminate information in these images. © 2025 SPIE.

关键词： Image enhancement

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Fourth international conference on Computer vision, Application, and Algorithm, CVAA 2024

Fourth International Conference on Computer Vision, Applicat...

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4th international conference on Computer vision, Application, and Algorithm, CVAA 2024

ISBN: (纸本)9781510687615

The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and image processing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.

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When Does Visual Prompting Outperform Linear Probing for vision-Language Models? A Likelihood Perspective

When Does Visual Prompting Outperform Linear Probing for Vis...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Tsao, Hsi-Ai Hsiung, Lei Chen, Pin-Yu Ho, Tsung-Yi National Tsing Hua University Hsinchu Taiwan Dartmouth College HanoverNH United States IBM Research Yorktown HeightsNY United States The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)9798350368741

Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can significantly improve the performance of out-of-distribution tasks. On the other hand, linear probing, a standard transfer learning method, can sometimes become the best approach. We propose a log-likelihood ratio (LLR) approach to analyze the comparative benefits of visual prompting and linear probing. By employing the LLR score alongside resource-efficient visual prompts approximations, our cost-effective measure attains up to a 100-fold reduction in run time compared to full training, while achieving prediction accuracies up to 91%. The source code is available at VP-LLR. © 2025 IEEE.

关键词： transfer learning visual prompting

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