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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121-130 订阅

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A Microstrip Patch Antenna with High Gain for WiMAX and X-Band applications 4

A Microstrip Patch Antenna with High Gain for WiMAX and X-Ba...

引用

4th international conference on Robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Sultana, Maria Karim, Tofavel Alim, Mohammad Abdul Electrical and Electronic Engineering University of Chittagong Chattogram Bangladesh

ISBN: (纸本)9798331530938

The microstrip patch antenna was specifically designed to be operational at a frequency of 7.8 GHz for applications in WiMAX and X-band satellite communication. The proposed design utilized a patch with optimized dimensions on Rogers RT/duroid substrate with a permittivity of 2.2 and a thickness of 1.5 mm. The patch of the antenna comprised two patch elements that were connected to each other. Adding the second patch enhanced the antenna's performance by improving its return loss to -49.92 dB and VSWR to 1.006 along with achieving an optimal gain of 8.5 dB. The antenna demonstrated a resonant frequency of 7.8 GHz with a stable radiation pattern, low return loss (-49.92 dB), low VSWR, and high gain of about 8.5 dB. © 2025 ieee.

关键词： Slot antennas

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

引用

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

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto V. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 ieee.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

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PhysID: Physics-based Interactive Dynamics from a Single-view image

PhysID: Physics-based Interactive Dynamics from a Single-vie...

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

作者： Gothe, Sourabh Vasant Chattopadhyay, Ayon Kiran, Gunturi Venkata Sai Phani Pratik Agarwal, Vibhav Vachhani, Jayesh Rajkumar Ghosh, Sourav Parameswaranath, V.M. Barath Raj, K.R. Samsung R&D Institute India - Bangalore India

ISBN: (纸本)9798350368741

Transforming static images into interactive experiences remains a challenging task in computer vision. Tackling this challenge holds the potential to elevate mobile user experiences, notably through interactive and AR/VR applications. Current approaches aim to achieve this either using pre-recorded video responses or requiring multi-view images as input. In this paper, we present PhysID, that streamlines the creation of physics-based interactive dynamics from a single-view image by leveraging large generative models for 3D mesh generation and physical property prediction. This significantly reduces the expertise required for engineering-intensive tasks like 3D modeling and intrinsic property calibration, enabling the process to be scaled with minimal manual intervention. We integrate an on-device physics-based engine for physically plausible real-time rendering with user interactions. PhysID represents a leap forward in mobile-based interactive dynamics, offering real-time, non-deterministic interactions and user-personalization with efficient on-device memory consumption. Experiments evaluate the zero-shot capabilities of various Multimodal Large Language Models (MLLMs) on diverse tasks and the performance of 3D reconstruction models. These results demonstrate the cohesive functioning of all modules within the end-to-end framework, contributing to its effectiveness. © 2025 ieee.

关键词： 3D Modelling Diffusion Multimodal Large Language Models Physics-Based Rendering

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

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

作者： Kuschel, Maurice Alkhatib, Amr Hasija, Tanuj Boström, Henrik Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data. © 2025 ieee.

关键词： CEGA Deep Canonical Correlation Analysis Explainable AI Multiview Representation Learning SHAP

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Masked image Pretraining on Language Assisted Representation

Masked Image Pretraining on Language Assisted Representation

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

作者： Hou, Zejiang Kung, Sun-Yuan Princeton University United States

ISBN: (纸本)9798350368741

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose Masked image pretraining on Language Assisted representatioN, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more efficient prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on imageNet-1K dataset with an input resolution of 224×224, MILAN achieves a top-1 accuracy of 85.4% on ViT-Base, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-Base on ADE20K dataset, outperforming previous masked pretraining results by 4 points. © 2025 ieee.

关键词： masked image pretraining vision transformer

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TGDrag: Adding Semantic Control into Point-based image Editing via Text Guidance

TGDrag: Adding Semantic Control into Point-based Image Editi...

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

作者： Lin, Chenhao Zhu, Yanjie Miao, Yingmao Zhao, Zhengyu Liu, Shuai Shen, Chao Xi'an Jiaotong University China

ISBN: (纸本)9798350368741

Controllable image generation has emerged as a cutting-edge subject of interest. Current interactive point-based image editing frameworks, such as DragGAN, achieve impressive results in fine-grained and controllable image editing. However, relying solely on point-based manipulations can lead to unintended outcomes due to the inherent lack of the users' semantic intent. To address this issue, we introduce Text-Guided Drag (TGDrag), a novel approach to adding semantic control into point-based image editing by using text prompts to guide the manipulation of handle and target points. Specifically, we design a channel correlation calculator that adaptively selects channels for the text and points to mitigate the potential influence of semantic control on point control. Furthermore, we introduce a text loss function to minimize the discrepancy between the generated images and the text prompts. Experimental results demonstrate that TGDrag achieves the expected function of semantic control while maintaining effectiveness regarding point control. © 2025 ieee.

关键词： Generative adversarial network Interactive editing Semantic control Text-guided image editing

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An Underwater image Quality Dataset with Renewed Pairwise Voting

An Underwater Image Quality Dataset with Renewed Pairwise Vo...

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

作者： Shen, Mengjiao Yang, Miao Zhang, Hansen Zhong, Jinyang Qiu, Yuquan Gu, Jinwei Jiangsu Ocean University School of Electronic Engineering Lianyungang China Ganyu Agricultural Development Group Co. Ltd. Lianyungang China

ISBN: (纸本)9798350368741

image datasets with paired mean opinion scores (MOS) enable the quantization of the perceptual differences between images and are of great significance for images taken underwater. In our previous research, a pairwise label underwater image quality subjective ranking (PLUIQR) method was proposed. In this paper, we take a further step by designing a post-reliability verification for the PLUIQR and releasing a publicly accessible underwater image quality dataset called PCUID. For the raw paired voting, steps including a triangular cycle error (TCE) criterion and reprocessing of the dispute data are performed. Meanwhile, the group maximum differentiation (gMAD) method is used to evaluate the performance of underwater quality assessment methods (UIQA) on images with similar quality. That illustrates the proposed dataset enabling UIQA methods to their judgment toward subtle quality differences. The dataset is available at https://***/JOU-UIP/PCUID. © 2025 ieee.

关键词： pairwise voting triangular cycle error underwater image dataset underwater image quality assessment

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A Plug-and-Play Diffusion-Styled Conversion Model for Domain Discrepancies in Medical image Segmentation

A Plug-and-Play Diffusion-Styled Conversion Model for Domain...

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

作者： Liu, Dong Wang, Zhiyong Guo, Linlin Shandong Youth University of Political Science China Shandong Normal University China

ISBN: (纸本)9798350368741

Accurate segmentation is a crucial step in medical image analysis. However, models trained on one dataset often suffer from performance degradation when directly applied to a different domain with a different data distribution due to domain discrepancies. To address this issue, we introduce a novel plug-and-play diffusion-styled domain conversion method, named Diffusion-Styled Domain Conversion (DSDC). Our DSDC framework formulates domain conversion as a gradual transition process from domain A to domain B, inspired by the diffusion process. Unlike traditional diffusion models that iteratively remove noise, our model progressively shifts the data distribution from domain A to domain B. We tested our DSDC method on two retinal vessel segmentation datasets, combining it with seven different segmentation models, and observed an average improvement of 20% in the DICE score and a reduction of 10 mm in the average ASD. © 2025 ieee.

关键词： Diffusion model medical image segmentation

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Self-Supervised Localized Topology Consistency for Noise-Robust Hyperspectral image Classification

Self-Supervised Localized Topology Consistency for Noise-Rob...

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

作者： Wang, Jie Tang, Liaoyuan He, Guanxiong Cao, Zhe Wang, Zheng Wang, Rong Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9798350368741

Label noise in hyperspectral image classification (HIC) can severely degrade model performance by leading to incorrect predictions and overfitting, especially as erroneous labels propagate and compound throughout the training process. To address this, we propose a robust learning framework called Self-Supervised Localized Topology Consistency (SSLTC), which enforces local topology consistency to enhance model resilience against noisy labels. SSLTC captures local topology via a graph-based representation, where nodes represent samples and edges encode pairwise similarities. Predictions are propagated from topologically similar nodes to central nodes, constrained by Kullback-Leibler (KL) divergence to encourage consistent predictions and reduce sensitivity to noisy labels. Additionally, a self-supervised contrastive learning strategy is used to refine spectral-spatial representations in an unsupervised manner, further improving robustness. Extensive experiments on hyperspectral benchmark datasets with varying noise levels demonstrate the superiority of SSLTC in mitigating the adverse effects of label noise compared to state-of-the-art approaches in HIC tasks. © 2025 ieee.

关键词： contrastive learning Hyperspectral image classification (HIC) localized topology consistency noisy labels

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Enhancing Precision in image-Guided Spine Surgery through the Prediction of Occluded Fiducials Utilizing ResNet Architecture

Enhancing Precision in Image-Guided Spine Surgery through th...

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

作者： Durga, R. Darshan, B. Hyagreev, Pranav S. Chakkaravarthy, Abhilash Purayath, Aparna Maik, Vivek Lakshmanan, Manojkumar Sivaprakasam, Mohanasankar Indian Institute of Technology Madras India Department of Electrical Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9798350368741

With advancements in imaging technologies, the C-Arm fluoroscopy imaging modality is essential for intra-operative visualization in image Guided Spine Surgery(IGSS). That necessitates the development of computational techniques for calibrating and correcting distortions in C-Arm images. For this purpose, calibration drums with embedded metallic fiducials are typically attached to the C-Arm detector. Our research introduces a novel algorithm designed to predict occluded distortion fiducials, thereby enhancing precision in IGSS. The fundamental strategy is to minimise reprojection error(RPE) by correcting image distortion using these fiducials. Fiducial occlusion often arises from surgical instrument placement, reduced contrast in the C-Arm machine, pre-existing screws in patients, or deficiencies in imaging algorithms. Our approach employs an in-house dataset to train a ResNet architecture for retrieving occluded fiducials by image registration, achieving similar image detection accuracy between 86% to 90%. Once occluded fiducials are identified, a thin-plate spline fitting algorithm is applied for distortion correction. This method, utilizing retrieved fiducials and spline fitting, resulted in a 9% reduction in reprojection error. © 2025 ieee.

关键词： C-Arm images Calibration Deep learning Distortion Correction Fiducial Prediction image-guided spine surgery ResNet Spline Fitting X-ray images

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