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检索条件"任意字段=Conference on Visual Communications and Image Processing 94"

共 3972 条记录，以下是31-40 订阅

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Relation-aware Semantic Alignment Network for Text-to-image Person Retrieval

Relation-aware Semantic Alignment Network for Text-to-Image ...

引用

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

作者： Yong Wu Rongxi Zhou Hongchao Li Ze Zhou Feifei Wei Min Li Guodui He School of Computer and Information Anhui Normal University Wuhu China Guangxi Academy of Agricultural Sciences Nanning China China Communications Service Guangxi Technical Service Branch Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-image Person Retrieval (TIPR) aims to utilize natural language descriptions as queries to retrieve pedestrian images. However, existing methods only concentrated on aligning individual text-image pairs and ignored the specific self-representations within both visible images and textual descriptions of the same identity. This neglects the impact of intra-modal information distribution on TIPR. In this paper, a novel Relation-aware Semantic Alignment Network (RSAN) is proposed to learn reliable and comprehensive semantic visual-textual associations across different modalities. Specifically, A Global Semantic Alignment Matching (GSAM) loss is introduced to enhance the coherence of inter-modality features while preserving intra-modal representations for cross-modal matching. Additionally, an Adapter-assisted Information Aggregation (AIA) module is designed to further complement contextual information fusion between the image features and text embeddings. Extensive experiments conducted on two public benchmark datasets demonstrate the superiority of the proposed RSAN.

关键词： visualization Pedestrians Semantics Natural languages Text to image Coherence Benchmark testing Signal processing Reliability Speech processing

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MAITFuse:Multi-Dimension Adaptive Interaction Transform Network For Infrared-visible image Fusion

MAITFuse:Multi-Dimension Adaptive Interaction Transform Netw...

引用

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

作者： Yabin Sun Wentai Lei Ziyi Zhang Jiongchang Liu Chenxu Li Tao Zhang Artificial Intelligence Jiangnan University Wu Xi China Communications Engineering Central South University Chang Sha China School of Electronic Information Central South University Chang Sha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.

关键词： Adaptive systems Transforms Signal processing Feature extraction Transformers Acoustics Data mining Speech processing image fusion visual perception

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PDCE: Patch-wise Dynamic Curve Estimation for Low-Light image Enhancement

PDCE: Patch-wise Dynamic Curve Estimation for Low-Light Imag...

引用

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

作者： Ruiyuan Chen Zhixin Li Han Zeng Yifan Liu Tao He Tiecheng Song School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement (LLIE) can be reformulated as an image-specific curve estimation (CE) problem. Traditional CE-based methods struggle with issues such as uniform processing across different regions, static parameter estimation, and lack of effective global semantic enhancement. To address these limitations, we propose a novel unsupervised learning framework, Patch-wise Dynamic Curve Estimation (PDCE), which dynamically adjusts and optimizes enhancement curves according to local patch brightness and the iteration process. Specifically, we present a Vision-Language Curve Discriminator (VLCD), which dynamically determines the curve type for each patch, avoiding uniformly applying the curve on the whole image. We introduce a Curve Parameter Estimator (CPE), which dynamically updates curve parameters and adjusts enhancement effects based on the output of the previous iteration. Furthermore, we design a visual State Space-based Semantic Enhancement Module (VSEM), which captures global receptive fields and enriches semantic features through the Mamba-based U-Net architecture. Extensive experimental results show the superiority of our PDCE over state-of-the-art methods for LLIE.

关键词： visualization Parameter estimation Semantics Estimation Speech enhancement Signal processing Iterative methods Unsupervised learning image enhancement Optimization

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A Computer Vision and Vibrohaptic Glove-Based Piano Learning System for the visually Impaired

A Computer Vision and Vibrohaptic Glove-Based Piano Learning...

引用

International conference on Advanced Communication Technology (ICACT)

作者： Ian Juha Cho Jin Park Hosung Bae Hankuk Academy of Foreign Studies Yongin South Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

The visually impaired are unable to enjoy leisure activities as much as ordinary people due to various limitations. To expand the scope of leisure activities for the visually impaired, we have developed a vibration glove-based system that helps with piano learning. Previous research used 88 infrared light-emitting diodes and gloves with infrared receivers to provide feedback to the user, but this method had many limitations. In particular, the inconvenient user experience and low accuracy were the biggest problems. Our method solves both problems using a camera and an image processing algorithm. As a result of testing the model on 20 piano images, it was shown that all keys were perfectly recognized in 75% of cases, and the gloves could be comfortably used in practice without any difficulty. Thus, our method presents a simpler user experience for the visually impaired, without requiring any special modifications to the piano.

关键词： Vibrations Learning systems Computer vision image recognition visual impairment Receivers Light emitting diodes User experience communications technology Testing

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visual communications and image processing'94. Part 2 (of 3)

Visual Communications and Image Processing'94. Part 2 (of 3)

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visual communications and image processing '94

The topics included are feature and texture analysis and aspects of computer vision;visual communication systems;very low bit-rate coding of video sequences;wavelets;image coding;and filtering, restoration, and enhanc... 详细信息

ISBN: (纸本)081941638X

关键词： image processing

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Learning Features by Minimizing the Interframe Differences 19th

Learning Features by Minimizing the Interframe Differences

引用

19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Zhao, Dong Zhang, Dan School of Computer Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China

ISBN: (纸本)9789819799183

In this paper, we present a novel yet intuitive unsupervised feature learning approach, referred to as Minimizing Interframe Differences (MID). The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of the frames, the differences between the before and after frames can be minimally encoded. MID is implemented with a difference encoding module and a frame reconstruction module. The former tries to minimize the difference between frames, while the latter first maps the difference encoding to optical flow and then realizes frame reconstruction based on the learned optical flow. All the modules are jointly learned in an end-to-end way through the reconstruction loss. We verify the feasibility of MID in the image datasets by replacing the natural transformation of videos to artificially parameterized transformation for images. Experimental results show that MID is able to predict optical flow accurately based on the minimum difference coding and learn a powerful visual representation that greatly enhances other visual tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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visual communications and image processing'94. Part 3 (of 3)

Visual Communications and Image Processing'94. Part 3 (of 3)

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visual communications and image processing '94

The topics included are image coding;biomedical image processing;video coding;wavelet, fractal, and multiresolution image coding;VLSI implementations and system architectures;motion estimation for video coding;televis... 详细信息

ISBN: (纸本)081941638X

关键词： image processing

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visual communications and image processing '94

Visual Communications and Image Processing '94

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visual communications and image processing '94

The topics included are low bit-rate video compression;very low bit-rate coding of video sequences;morphological image processing;vector quantization;filtering , restoration, and enhancement;video coding;fractals and ... 详细信息

ISBN: (纸本)081941638X

关键词： image processing

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Photovoltaic Cell Defect Classification Using Attention U-Net image Segmentation 4th

Photovoltaic Cell Defect Classification Using Attention U-Ne...

引用

4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Alzu’bi, Ahmad Saleh, Zaid Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan Department of Electrical Engineering Jordan University of Science and Technology Irbid22110 Jordan

ISBN: (纸本)9783031821523

The need to automate solar cell quality monitoring techniques is growing as solar power utilization gets significant momentum. However, the detection of internal defects in photovoltaic (PV) cells is still quite a challenge, highlighting the demand for a reliable method to reduce the requirement for manual inspections. This paper presents a deep learning model to automatically detect and classify defective PV cells in Electroluminescence images using attention-based U-Net image segmentation. The proposed deep learning architecture uses ResNet50 and attention U-Net networks to formulate a set of discriminating image features, providing an effective procedure of deep transfer learning. The extracted image descriptors are encoded and learned from image masks and annotations that were reprocessed to improve the generalization capability of the photovoltaic cell classifier. The semantic deep learning model with image augmentation and mask processing provides a baseline to detect and classify any possible faults in photovoltaic cells. The experimental results demonstrate the effectiveness of semantic attention U-Net-Resnet50 deep architecture in classifying PV cells, achieving a precision of 95% and an F1-score of 94%. It also achieved an IoU of 90% for PV image segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

引用

Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

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