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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79605 条记录，以下是91-100 订阅

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Performance Comparison of Different Digital image Filters Used for Biomedical signals 8th

Performance Comparison of Different Digital Image Filters U...

引用

8th international conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Sudagani Jyothi Muthu Krishnammal, P. School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819757855

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. MATLAB is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like mean, median, weighted average, Gaussian, and bilateral in Python to verify their performance in terms of mean square error (MSE) and similarity index (SI), a suitable filter can be selected for biomedical applications by comparing their performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Language Prediction of Twitch Streamers using Graph Convolutional Network 6

Language Prediction of Twitch Streamers using Graph Convolut...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Hasan, Md. Nahid Saha, Nakshi Rahman, Md. Anisur Dhaka International University Dept. of CSE Dhaka1212 Bangladesh Khulna University CSE Discipline Khulna9208 Bangladesh

ISBN: (纸本)9798331506520

Social Network is a common example of the graph structure. There are many algorithms available for predicting different elements of social networks. Language is one of the elements. Prediction problems generally stay within 2D information. Graphs provide more structural information like edge relationship edge feature etc. While predicting social network elements, information about graph structure can be very useful. Graph Convolutional Network gives the opportunity to use graph structure as a piece of new information. Twitch is a popular social media platform where people stream gaming-related content. In our work, we have proposed a Graph Convolutional Network model for predicting the language of Twitch gamers. We also showed that it outperforms other machine learning and deep learning algorithms like Decision trees, Random Forest, Support Vector Machine, Neural Network and others. While other models could only reach 45.8% accuracy, GCN was able to achieve 73.43% by utilizing the graph structure. © 2025 ieee.

关键词： Random forests

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Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for image Restoration Models Compression

Soft Knowledge Distillation with Multi-Dimensional Cross-Net...

引用

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

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

ISBN: (纸本)9798350368741

Transformer-based encoder-decoder models have achieved remarkable success in image-to-image transfer tasks, particularly in image restoration. However, their high computational complexity-manifested in elevated FLOPs and parameter counts-limits their application in real-world scenarios. Existing knowledge distillation methods in image restoration typically employ lightweight student models that directly mimic the intermediate features and reconstruction results of the teacher, overlooking the implicit attention relationships between them. To address this, we propose a Soft Knowledge Distillation (SKD) strategy that incorporates a Multi-dimensional Cross-net Attention (MCA) mechanism for compressing image restoration models. This mechanism facilitates interaction between the student and teacher across both channel and spatial dimensions, enabling the student to implicitly learn the attention matrices. Additionally, we employ a Gaussian kernel function to measure the distance between student and teacher features in kernel space, ensuring stable and efficient feature learning. To further enhance the quality of reconstructed images, we replace the commonly used L1 or KL divergence loss with a contrastive learning loss at the image level. Experiments on three tasks-image deraining, deblurring, and denoising-demonstrate that our SKD strategy significantly reduces computational complexity while maintaining strong image restoration capabilities. © 2025 ieee.

关键词： contrastive learning image restoration Knowledge distillation multi-dimensional cross-net attention

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Fusion Grad-CAM: A Methodology for Generating Unified Attention Maps in Majority Voting Classifiers 6

Fusion Grad-CAM: A Methodology for Generating Unified Attent...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Ntampakis, Nikolaos Diamantaras, Konstantinos Argyriou, Vasileios Sarigianndis, Panagiotis International Hellenic University Greece MetaMind Innovations Kozani Greece Kingston University London London United Kingdom University of Western Macedonia Kozani Greece

ISBN: (纸本)9798331506520

In this work, we introduce a novel methodology called Fusion Grad-CAM for generating attention maps in ensemble classification systems that utilize majority voting. Currently, there is no established technique for producing a consolidated attention map in such systems. Traditional approaches require inspecting individual attention maps from each contributing model, which is inefficient. Our proposed method integrates Grad-CAM produced attention maps by averaging or weighted averaging them based on both the predicted class and the confidence level of each model's decision. This approach offers a holistic representation of the ensemble's decision-making process. We validate our methodology using various combinations of well-known convolutional neural networks (CNNs) pretrained on imageNet, demonstrating the effectiveness and clarity of the resulting attention maps. © 2025 ieee.

关键词： Convolutional neural networks

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Interpreting Deep Neural Network-Based Receiver Under Varying signal-To-Noise Ratios

Interpreting Deep Neural Network-Based Receiver Under Varyin...

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

作者： Tuononen, Marko Korpi, Dani Hautamäki, Ville Nokia Networks Nokia Group Espoo Finland Nokia Bell Labs Nokia Group Espoo Finland School of Computing University of Eastern Finland Joensuu Finland

ISBN: (纸本)9798350368741

We propose a novel method for interpreting neural networks, focusing on convolutional neural network-based receiver model. The method identifies which unit or units of the model contain most (or least) information about the channel parameter(s) of the interest, providing insights at both global and local levels-with global explanations aggregating local ones. Experiments on link-level simulations demonstrate the method's effectiveness in identifying units that contribute most (and least) to signal-to-noise ratio processing. Although we focus on a radio receiver model, the method generalizes to other neural network architectures and applications, offering robust estimation even in high-dimensional settings. © 2025 ieee.

关键词： Convolutional Neural Networks Interpretable Machine Learning Neural Network Interpretation Radio Receiver

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Enhancing Data-Free Class-Incremental Learning via image-Centric Dual Distillation

Enhancing Data-Free Class-Incremental Learning via Image-Cen...

引用

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

作者： Fu, Feifei Lu, Zhiwu Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9798350368741

In Data-free Class-Incremental Learning (DFCIL), catastrophic forgetting is a significant challenge due to the lack of access to previous task image data. Recent approaches using model inversion have made progress in addressing this issue, yet the suboptimal application of knowledge distillation hampers new task learning, limiting overall model performance. To overcome this, we propose a novel method incorporating image-centric dual distillation, designed to retain more old knowledge while facilitating new knowledge acquisition, thus enhancing DFCIL performance. Specifically, we first introduce a weak-constraint relation distillation strategy to preserve old knowledge while promoting the assimilation of new knowledge by learning the relationships among intra-class samples. Then, to further enhance the preservation of old knowledge and refine the integration of new knowledge, we introduce a low-level feature distillation strategy to retain foundational general knowledge by leveraging semantic information from shallow network layers. Extensive experiments show the effectiveness of our method. © 2025 ieee.

关键词： Catastrophic forgetting Data-free class-incremental learning Knowledge distillation

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Efficient Quality Controllable Neural image Compression based on QD-Model

Efficient Quality Controllable Neural Image Compression base...

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

作者： Wang, Shaokang Xiang, Guoqing Xu, Jinchang Zhang, Shanghang Xie, Xiaodong School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (纸本)9798350368741

Neural image compression has achieved significant advancements, consistently outperforming traditional codecs in terms of performance. However, research on quality control algorithms for neural image compression is still lacking. In this paper, we propose a framework designed to control the quality of compressed images through a one-pass pre-analysis. First, we construct a foundational relationship between the quantization factor and compression distortion, utilizing variable rate neural image compression as the basis for quality control. Second, we introduce the image Content-Compression features-based Distortion Estimation Network (C2DEN) to efficiently fit the sample-adaptive Quantization-Distortion (QD) model. Leveraging the QD model, we convert the target quality into a quantization factor to control the compression model, enabling quality-controllable compression of samples. Experimental results show that the average quality errors on four different datasets are only 0.89%, 1.79%, 1.73%, and 1.61%. Compared with existing control methods, our method reduces the algorithm time complexity by 98.58%, 98.52%, 98.85%, and 98.50% while ensuring accuracy, which further demonstrates the superiority of our method. © 2025 ieee.

关键词： linear model Neural image compression quality control quantization factor

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Segmentation of Ship Propeller Cavitation Area Using image-Based Anomaly Detection Network 6

Segmentation of Ship Propeller Cavitation Area Using Image-B...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Jeong, Wonje Shin, Yong-Jin Park, Soon-Yong Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Samsung Heavy Industries Co. Ltd. Daejeon34051 Korea Republic of

ISBN: (纸本)9798331506520

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. The pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer Networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. The proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision. © 2025 ieee.

关键词： Cavitation

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MINR: Efficient Implicit Neural Representations for Multi-image Encoding

MINR: Efficient Implicit Neural Representations for Multi-Im...

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

作者： Zhou, Wenyong Wu, Taiqiang Liu, Zhengwu Cheng, Yuxin Zhang, Chen Wong, Ngai Department of EEE The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Implicit Neural Representations (INRs) aim to parameterize discrete signals through implicit continuous functions. However, formulating each image with a separate neural network (typically, a Multi-Layer Perceptron (MLP)) leads to computational and storage inefficiencies when encoding multi-images. To address this issue, we propose MINR, sharing specific layers to encode multi-image efficiently. We first compare the layer-wise weight distributions for several trained INRs and find that corresponding intermediate layers follow highly similar distribution patterns. Motivated by this, we share these intermediate layers across multiple images while preserving the input and output layers as input-specific. In addition, we design an extra novel projection layer for each image to capture its unique features. Experimental results on image reconstruction and super-resolution tasks demonstrate that MINR can save up to 60% parameters while maintaining comparable performance. Particularly, MINR scales effectively to handle 100 images, maintaining an average peak signal-to-noise ratio (PSNR) of 34 dB. Further analysis of various backbones proves the robustness of the proposed MINR. © 2025 ieee.

关键词： Implicit Neural Representation Layer Sharing Model Efficiency Multi-image Encoding

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GENIE: Socially Unbiased Generative Text-to-image Editing

GENIE: Socially Unbiased Generative Text-to-Image Editing

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

作者： Lau, Julia K. Phan, Raphaël C.-W. Rajanala, Sailaja Cox, Ingemar J. Pal, Arghya Monash University Malaysia campus Malaysia University College London United Kingdom

ISBN: (纸本)9798350368741

Generative diffusion models often exhibit societal biases in sensitive personal attributes such as age, gender, and race. In this work, we describe GENIE - a method to reduce such biases in a variety of classifier-free diffusion models used for image editing. Our method implicitly incorporates debiasing terms together with the user's explicit edit instruction to reduce bias. This automatic method relieves the user from needing to modify edit instructions in order to avoid bias. Further, no additional training is needed. Experimental results are provided based on modifications to four diffusion models, namely InstructPix2Pix, Stable Diffusion 1.5, Stable Diffusion 2.1, and Stable Diffusion XL. We show that, on average, bias is reduced by 31% in gender, 15% in age, 39% in race. © 2025 ieee.

关键词： Debiasing Societal Biases Text-to-image

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