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

共 25558 条记录，以下是401-410 订阅

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SignTrack: Advancements in Real-Time Sign Language processing for Inclusive Computing with optimized AI 13

SignTrack: Advancements in Real-Time Sign Language Processin...

引用

13th International conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Piperakis, Stavros Papatsimouli, Maria Argyriou, Vasilis Sarigiannidis, Panagiotis Fragulis, George F. Univ Western Macedonia Dept Elect & Comp Engn ZEP Campus Kozani 50100 Greece Kingston Univ Dept Networks & Digital Media Kingston England

ISBN: (纸本)9798350385434;9798350385427

visual-based translator systems, which utilize image and video data for real-time translation, represent a captivating research area. While previous research has explored text-based translation, limitations exist in capturing the nuances of human communication. visual-based approaches address this gap by leveraging deep learning and image processing to extract information like objects, faces, and environmental context. This research project investigates the application of deep learning in sign language translation using Automated AI. By analyzing visual data, the system recognizes signs and translates them into spoken languages or generates text descriptions. This technology holds significant promise for various fields - education, entertainment, tourism, and healthcare - and can contribute to the advancement of information technology and artificial intelligence systems.

关键词： Automated AI IoT Sign Language Sign language transcription

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image Compressed Sensing Based on Vision-Inspired Importance Maps

Image Compressed Sensing Based on Vision-Inspired Importance...

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2024 ieee International conference on Imaging Systems and Techniques, IST 2024

作者： Yoshida, Shunra Sun, Zhe Yoshizawa, Shin Michikawa, Takashi Noda, Shigeho Micheletto, Ruggero Yokota, Hideo Yokohama City University Department of Materials System Science 22-2 Seto Kanazawa-ku Yokohama Kanagawa236-0027 Japan Riken Center for Advanced Photonics Riken Image Processing Research Team 2-1 Hirosawa Wako Saitama351-0198 Japan

ISBN: (纸本)9798350378214

This paper presents a novel image compressed sensing framework that leverages human vision-inspired importance maps to guide the compression process. Unlike traditional methods that compress images without considering perceptual relevance, our approach selectively samples pixels based on their visual saliency and geometric significance. The framework begins by generating an importance map that combines visual saliency, obtained through LayerCAM, with geometric features like edge information. These maps dictate the adaptive sampling of pixels, ensuring that the most visually important information is retained. In the decoding stage, the sampled pixels are reconstructed using prior information-based models such as the Deep image Prior and Manifold Modeling in Embedded Space. These models effectively restore the image while preserving its critical visual characteristics. To evaluate the performance of our framework, we introduce two new similarity criteria: Weighted PSNR (WPSNR) and Weighted SSIM (WSSIM), both tailored to assess the perceptual quality of images. Our experimental results demonstrate that the proposed method outperforms conventional image compression techniques, especially in maintaining perceptual quality at low sampling rates. This framework shows great promise for applications in areas where preserving visual fidelity is crucial, such as high-quality image compression and digital brain modeling. The integration of visual importance into the compression process represents a significant advancement in the field of image processing, offering a more efficient and perceptually aware approach to image compression. © 2024 ieee.

关键词： Pixels

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LEARNED image COMPRESSION FOR BOTH HUMANS AND MACHINES VIA DYNAMIC ADAPTATION 31

LEARNED IMAGE COMPRESSION FOR BOTH HUMANS AND MACHINES VIA D...

引用

2024 International conference on image processing

作者： Zhu, Lingyu Li, Binzhe Lu, Riyu Chen, Peilin Mao, Qi Wang, Zhao Yang, Wenhan Wang, Shiqi City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China Commun Univ China Beijing Peoples R China Peking Univ Beijing Peoples R China PengCheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Recent advancements in neural image compression have shown great potential in outperforming conventional standard codecs in terms of both rate-distortion and rate-analysis performance. However, there is an issue of divergent preferences in information preservation or reconstruction in the process of compression for humans and machines, respectively. Compression for humans tends to retain the signal fidelity or perceptual quality of visual appearance while compression for machines requires preserving critical semantic information, resulting in the limitation of the bitstream supporting only a single requirement during the compression. To bridge this gap, we propose a dynamic adaptation approach that generates a single bitstream serving both humans and machines. This approach aims to mitigate the domain gap among tasks, which facilitates maintaining the performance of out-of-scope tasks. Specifically, the proposed method concentrates on learning a dynamic adaptation process, i.e., optimizing the latent representation in the compressed domain in an end-to-end manner while adhering to the rate-performance constraint. Extensive results reveal that our paradigm significantly reduces the domain gap, surpassing existing codecs.

关键词： Learned image compression dynamic adaptation and human-machine collaboration

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Lane Type Classification & Distance Measurement System for Autonomous Vehicle

Lane Type Classification & Distance Measurement System for A...

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31st ieee conference on Signal processing and communications Applications (SIU)

作者： Ozen, Sinan Kaya, Uygar Semiz, Abdurrahim Yalcin, Mustafa Fatih Celebi, Aysun Tasyapi AVL Arastirma Muhendislik Turkiye Istanbul Turkiye Kocaeli Univ Elekt & Haberlesme Muhendisligi Istanbul Turkiye Ozyegin Univ Bilgisayar Muhendisligi Istanbul Turkiye Istanbul Tech Univ Kontrol & Otomasyon Muhendisligi Istanbul Turkiye Yildiz Tekn Univ Kontrol & Otomasyon Muhendisligi Istanbul Turkiye

ISBN: (纸本)9798350343557

In this paper, lane type classification and lane distance measurement system are proposed for autonomous vehicles. In the proposed system, the perspective transformation method is applied to the image taken from the vehicle camera, so that a bird's-eye view is obtained and the parts with stripes are cropped from the image, and then a multi-class classification model is implemented using neural network-based architectures to determine the stripe type. In addition, with the proposed distance measurement system, the distance of the vehicle to the right and left lanes is calculated during autonomous driving, thus ensuring that the vehicle can drive autonomously on the lane. For this study, our own data set has been created.

关键词： deep learning image processing custom dataset lane type classification lane distance measurement

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EvaSR: Rethinking Efficient visual Attention Design for image Super-Resolution

EvaSR: Rethinking Efficient Visual Attention Design for Imag...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wu, Zhijian Zhang, Chenhan Huang, Dingjiang School of Data Science and Engineering East China Normal University Shanghai China Trenton Collegiate Institute ON Canada

ISBN: (纸本)9798350368741

Due to the advantages of long-range modeling via the self-attention mechanism, Transformer has taken various vision tasks by storm, including image super-resolution (SR). In this study, we reveal that the convolutional neural network (CNN) with proper visual attention is a more simple and effective paradigm than Transformer in image SR tasks. We reexamine the successful SR models and discover several key characteristics that contribute to accurate image reconstruction. Built on this recipe, we propose a pure CNN-based SR network using efficient visual attention, dubbed EvaSR. Benefiting from the carefully designed visual attention, our EvaSR can favorably capture both local structure and long-range dependencies, and achieve adaptivity in spatial and channel dimensions while retaining the simplicity and efficiency of CNNs. The experimental results demonstrate that our EvaSR achieves state-of-the-art performance among the existing efficient SR methods. Especially, the tiny version of EvaSR needs 21.4% and 15.2% parameters of IMDN and SMSR with better performance. © 2025 ieee.

关键词： Efficient Network Gated Linear Unit image Super-resolution Transformer

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Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation

Hierarchical Histogram Threshold Segmentation - Auto-termina...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chang, Thomas V. Seibt, Simon Lipinski, Bartosz von Rymon Nuremberg Inst Technol Game Tech Lab Fac Comp Sci Nurnberg Germany

ISBN: (纸本)9798350353013;9798350353006

Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks, offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years, many rely on predefined initialization and termination criteria. In this paper, a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination, outperforming state-of-the-art methods w.r.t. boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence, while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Furthermore, an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.

关键词： Color Histogram Hierarchical Segmentation image Segmentation Superpixel Thresholding

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DEEP FUSION OF VISIBLE AND NEAR INFRARED imageS FOR REGISTRATION AND DEFOGGING USING CROSS MODAL TRANSFORMER 31

DEEP FUSION OF VISIBLE AND NEAR INFRARED IMAGES FOR REGISTRA...

引用

2024 International conference on image processing

作者： Ji, Mengyao Jung, Cheolkon Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

According to Rayleigh scattering, the density of fog is closely related to the distance of the target from the visible light camera. However, the near infrared (NIR) spectral band is robust to the interference problem but has a single channel without color. In this paper, we propose deep fusion of visible (VIS) and near infrared (NIR) images for registration and defogging using cross modal transformer. The proposed fusion network has two main features: 1) Cross modal transformer deals with the channel and spatial redundancy between two source images while retaining their complementary information;2) Since the blue channel in VIS image can represent the amount of fog in the scene according to Rayleigh scattering, we normalize it to obtain a fog weight map. To synthesize training data, the distance between object and camera is required, thus we use depth estimation to obtain it. Then, we assign different amounts of fog to each image to consider various weather conditions. First, we extract multi-scale features from the source images. Second, we use the cross modal transformer to reflect cross-domain interaction between the source images in spatial and channel domains. Third, we generate the fusion weight for the source images according to the fog weight map and reconstruct a fused result based on it. Various experiments on the widely-used benchmark and real-world datasets show that the proposed method generates natural-looking defogging results with clear textures in distant regions while keeping the original tone in near regions. Moreover, the proposed method outperforms state-of-the-art fusion methods in term of both visual quality and quantitative measurements.

关键词： image fusion cross modal transformer defogging multi-modal images near-infrared registration transfer learning

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Enhancement of Underwater images using Multi-Scale Retinex, CLAHE and Unsharp Masking Techniques 2

Enhancement of Underwater Images using Multi-Scale Retinex, ...

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2nd ieee International conference on Networking and communications, ICNWC 2024

作者： Gamini, Sridevi Reddy Pothamsetti, Bhavya Prasanna Malla, Baby Gayathri Podila, Hanuman Vuda, Manjusree Prasad, Satti Harichandra Aditya Engineering College Dept. of ECE Surampalem India

ISBN: (纸本)9798350365269

Underwater environments present unique challenges for image capture and analysis due to factors like light attenuation, color distortion, and loss of detail. In this study, we propose a comprehensive strategy to underwater image enhancement employing Color Compensation, Detail Sharpening, Auto-level based Multi-Scale Retinex, Contrast Limited Adaptive Histogram Equalization (CLAHE). Additionally, we integrate Unsharp masking technique to further improve image quality and enhance visual clarity. Through experimental validation using real-world underwater imagery datasets, this method demonstrates significant advancements in enhancing underwater scenes, effectively mitigating color distortions and restoring lost details. © 2024 ieee.

关键词： image enhancement

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Blind image Quality Assessment Score for Humanities Online Digital Repositories 9

Blind Image Quality Assessment Score for Humanities Online D...

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9th International conference on Smart and Sustainable Technologies (SpliTech)

作者： Tomasovic, Zeljka Pintaric, Neven Saulig, Nicoletta Univ Zadar Dept Informat Sci Zadar Croatia Univ Pula Fac Engn Pula Croatia

ISBN: (纸本)9798350390797;9789532901351

The proliferation of digital image repositories has revolutionized the accessibility of visual data across various domains, from scientific research to cultural heritage preservation. However, the quality of images within these repositories varies significantly, posing a challenge for users relying on accurate and high-quality visual information. In recent years, a substantial effort has been made to find an objective and blind image quality assessment (IQA) metric that can correlate with perceived quality measurements. Unfortunately, there is still no definite method to asses this problem because of the complex nature of objectively measuring image distortions. In this paper, we present a comprehensive framework for assessing image quality in online repositories for humanities. The approach relies on a combination of known image processing techniques, including histogram analysis, artefact estimation and dynamic range evaluation. Other parameters are also taken into account, such as resolution, dots per inch (DPI), bit depth and format. By automatically grading image quality using these methods, we aim to provide researchers and practitioners within the humanities field, with a reliable tool for evaluating the trustworthiness and suitability of images sourced from national repositories. Through a systematic examination of image quality metrics, the strengths and weaknesses of existing image collections can be highlighted, paving the way for enhanced curation standards and improved access to high-quality visual resources. Methods were tested on images from online repositories in public domain.

关键词： digital object digital image image quality assessment noise artefacts histogram repository

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TRUNCATED WEIGHTED NUCLEAR NORM REGULARIZATION AND SPARSITY FOR image DENOISING 30

TRUNCATED WEIGHTED NUCLEAR NORM REGULARIZATION AND SPARSITY ...

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30th ieee International conference on image processing (ICIP)

作者： Zhang, Ming Yan Zhang, Mingli Zhao, Feng Zhang, Fan Liu, Yepeng Evans, Alan Shandong Technol & Business Univ Yantai 264025 Peoples R China McGill Univ Montreal Neurol Inst Mcgill Ctr Integrat Neuroscience Montreal PQ Canada

ISBN: (纸本)9781728198354

The attribute of signal sparsity is widely used to sparse representaion. The existing nuclear norm minimization and weighted nuclear norm minimization may achieve a suboptimal in real application with the inaccurate approximation of rank function. This paper presents a novel denoising method that preserves fine structures in the image by imposing L-1 norm constraints on the wavelet transform coefficients and low rank on high-frequency components of group similar patches. An efficient proximal operator of Truncated Weighted Nuclear Norm (TWNN) is proposed to accurately recover the underlying high-frequency components of low rank patches. By combining a wavelet domain sparse preservation prior with TWNN, the proposed method significantly improves the reconstruction accuracy, leading to a higher PSNR/SSIM and visual quality than state of the art approaches.

关键词： image denoising wavelet domain sparsity low rank TWNN

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