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Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shiyu Miao Delong Chen Fan Liu Chuanyi Zhang Yanhui Gu Shengjie Guo Jun Zhou College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong SAR School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University Queensland Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction—such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks.

关键词： Training image segmentation Roads Semantics Optical fiber networks Optical imaging Optical sensors Speech processing Optical signal processing Remote sensing

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Spiking Generative Models Based on Variational Autoencoder and Adversarial Training

Spiking Generative Models Based on Variational Autoencoder a...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wenchuan Zhang Jie Zhang Ricky Yuen-Tan Hou Weifeng Su Wentao Fan Department of Computer Science Guangdong Provincial/Zhuhai Key Laboratory IRADS Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Department of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking Neural Networks (SNNs) offer significant potential for energy-efficient computing due to their binary, event-driven properties. However, existing deep generative SNNs often struggle to produce high-quality low-dimensional representations in latent space, adversely affecting the quality of their generated samples. To address this limitation, we introduce a novel generative model that integrates the principles of variational autoencoders (VAEs) with adversarial training techniques. Our model consists of a generative module based entirely on an SNN-structured VAE and a discriminator employing artificial neural networks. This discriminator enhances the training process of our SNN-based generative model by applying adversarial principles similar to those used in Generative Adversarial Networks (GANs). Additionally, we have developed a conditional generation capability within our model, enabling the controlled production of specific images based on label inputs. Experimental evaluations on multiple datasets demonstrate that our model achieves superior image quality compared to existing top-performing SNN-based generative models. The source code of our model is accessible at https://***/zxj8806/SGM-VaGAN.

关键词： Training Adaptation models image synthesis Source coding Autoencoders Spiking neural networks Production signal processing Real-time systems Speech processing

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs xGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, xGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection signal processing algorithms Feature extraction Real-time systems Speech processing

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image Compression Algorithm Based on Region of Interest Extraction for Unmanned Aerial Vehicles Communication

Image Compression Algorithm Based on Region of Interest Extr...

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IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Yanxia Liang Tong Jia xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： image segmentation Visualization image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems image reconstruction Videos

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Frequency-Domain Guided Multiple Parallel Kernels Network for Low-Light Remote Sensing image Enhancement

Frequency-Domain Guided Multiple Parallel Kernels Network fo...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jingxuan Zhou Hao Li Jinlong Wang xiongxin Tang Fanjiang xu Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to dark environments, optical aberrations, etc, the remote sensing images are often submerged under low contrast degradation, which greatly hinders their practical applications for agricultural management and other related tasks. The surface features of remote sensing images are often continuously distributed in space, thus, the sizes of the network’s receptive fields and its ability to learn long-range dependencies are crucial for restoring low-light remote sensing images. Existing methods based on CNN provide limited receptive fields, while Transformer-based methods are constrained by their quadratic computational complexity. To cope with these issues, we propose a novel low-light remote sensing image enhancement network that combines multi-scale receptive fields with frequency-domain attention. Specifically, this network employs multiple parallel kernels of varying sizes to learn multi-scale local features in the spatial domain and complements frequency-domain information to learn global long-range correlations, which achieves local-global feature extraction and further facilitates subsequent degraded images enhancement. We have conducted extensive experiments to demonstrate that our network outperforms existing methods quantitatively and achieves exceptional visual performance, which fully highlights the effectiveness and superiority of our method in enhancing low-light remote sensing images.

关键词： Visualization Frequency-domain analysis Transforms Speech enhancement Feature extraction Transformers image restoration Kernel Remote sensing image enhancement

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Comparative Analysis of Object Detection Accuracy in Diverse Background Conditions

Comparative Analysis of Object Detection Accuracy in Diverse...

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International conference on signal processing and Communication (ICSC)

作者： Aparajita Sheoran Aman Tripathi Ankur Bharadwaj Electronics and Communication Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Object detection models must perform effectively across diverse environmental conditions to support autonomous navigation and surveillance systems. YOLOv5 and MobileNetV2 were evaluated using a custom dataset of 100,000 images captured under varied conditions, including daylight, fog, rain, and nighttime. YOLOv5 achieved an F1 score of 0.89 and a precision of 0.91 in low-light and rainy scenarios, demonstrating robustness in challenging environments. MobileNetV2 delivered faster inference speeds of up to 45 FPS on devices like the Raspberry Pi 5 and Google Coral Dev Board, with an F1 score of 0.82 in clear and foggy conditions, making it a strong candidate for resource-constrained deployments. The evaluation highlights YOLOv5's advanced feature extraction for high-accuracy detection and MobileNetV2's lightweight architecture for computational efficiency. These findings enable informed decision-making when selecting models for edge-based object detection applications, particularly in cost-sensitive and real-world scenarios.

关键词： YOLO Rain Surveillance image edge detection Decision making signal processing Feature extraction Robustness Internet Edge computing

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shun Zou Zhuo Zhang Guangwei Gao College of Artificial Intelligence Nanjing Agricultural University Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba

关键词： Optical filters image segmentation Visualization Convolution Noise Streaming media Feature extraction Retina State-space methods Speech processing

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High-Resolution Speech Restoration with Latent Diffusion Model

High-Resolution Speech Restoration with Latent Diffusion Mod...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Tushar Dhyani Florian Lux Michele Mancusi Giorgio Fabbro Fritz Hohl Ngoc Thang Vu Sony Europe B.V. Stuttgart Germany University of Stuttgart Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Traditional speech enhancement methods often oversimplify the task of restoration by focusing on a single type of distortion. Generative models that handle multiple distortions frequently struggle with phone reconstruction and high-frequency harmonics, leading to breathing and gasping artifacts that reduce the intelligibility of reconstructed speech. These models are also computationally demanding, and many solutions are restricted to producing outputs in the wideband frequency range, which limits their suitability for professional applications. To address these challenges, we propose Hi-ResLDM, a novel generative model based on latent diffusion designed to remove multiple distortions and restore speech recordings to studio quality at a full-band sampling rate of 48kHz. Benchmarked against state-of-the-art methods that leverage GAN and Conditional Flow Matching (CFM) components, Hi-ResLDM demonstrates superior performance in regenerating high-frequency-band details. Hi-ResLDM not only excels in non-instrusive metrics but is also consistently preferred in human evaluation and performs competitively on intrusive evaluations, making it ideal for high-resolution speech restoration.

关键词： Measurement Acoustic distortion Computational modeling Speech enhancement Stability analysis image restoration Recording Iterative methods image reconstruction Wideband

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Clinically Robust Polyp Segmentation: Enhanced Generalization and Perturbation Resistance

Clinically Robust Polyp Segmentation: Enhanced Generalizatio...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shanchuan Wang Fang Yuan Jian-Nan Su Min Gan College of Computer Science and Technology Qingdao University Qingdao China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Colonoscopy is vital for detecting colorectal polyps, which are closely linked to colorectal cancer. Accurate segmentation of polyps in colonoscopic images is essential for diagnosis and surgical planning but is challenging due to variability in polyp size, shape, and unclear boundaries. The Segment Anything Model (SAM) has shown promise in polyp segmentation but relies heavily on user-provided prompts and involves a large number of parameters, limiting its practicality in clinical settings. To address the limitations of SAM in clinical practice, we introduced Low Rank and Perturbation Segment Anything Model (LP-SAM) to improve segmentation accuracy and generalization ability while reducing the parameter count and complexity of user input. LP-SAM showed enhanced generalization and a lightweight design, making it more suitable for clinical applications where precise user input may not always be feasible. Comparative evaluations demonstrate that LP-SAM outperforms state-of-the-art methods on datasets such as CVC-ColonDB, CVC-300, and ETIS.

关键词： Resistance Training image segmentation Adaptation models Accuracy Shape Perturbation methods Surgery Planning Speech processing

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JANE: Joint Angle Networks Assisting 3D Human Pose Estimation

JANE: Joint Angle Networks Assisting 3D Human Pose Estimatio...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jinhuan Wang Yuzhen Zhao xujie Song Wenzhou Chen Qi xuan Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology HangZhou China Department of Computer Science and Technology Hangzhou Dianzi University HangZhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D human pose estimation (HPE) is crucial due to its extensive applications. While current 3D HPE methods focus on human skeleton topology for accuracy, they often overlook joint angle information, which is vital in 2D-to-3D pose lifting. This paper introduces the Joint Angle Network (JANE) model to leverage joint angle features from 2D poses. We propose an angle graph convolution module to extract joint angle features and a multi-stage parallel fusion module to integrate these features with input image data. Experiments on Human3.6M show that our method improves accuracy by 8.7% and 2.6% over non-spatiotemporal baselines using 2D ground truth and 2D pose detectors, respectively.

关键词： Visualization Solid modeling Three-dimensional displays Accuracy Convolution Pose estimation Feature extraction Transformers Topology Speech processing

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