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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

共 6921 条记录，以下是11-20 订阅

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CoBCom 2024 - International conference on Broadband communications for Next Generation Networks and Multimedia Applications, co-located International conference and Workshop on Systems, Signals and image processing, IWSSIP 2024

CoBCom 2024 - International Conference on Broadband Communic...

引用

5th International conference on Broadband communications for Next Generation Networks and Multimedia Applications, CoBCom 2024

ISBN: (纸本)9798350388459

The proceedings contain 21 papers. The topics discussed include: from ray tracing to channel impulse responses: a review on the description of polarimetric time-invariant SISO channels;an efficient algorithm for scheduling aircraft landing problem;modeling and characterization of a compact in line filter with transmission zeros;from concept to implementation: lessons learned in designing and deploying a visible light positioning system;designing an augmented reality teaching module for power consumption in FPGAs;navigating the future: digital twin in maritime industry;measurement of a baby dummy in a car for child presence detection;advancing automotive connectivity: new technologies and security considerations;digital twins to monitor IoT devices for green transformation of university campus;and feasibility study of time synchronization solution for the bistatic synthetic aperture radar using mobile platforms.

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2021 International conference on visual communications and image processing (VCIP) Proceedings

2021 International Conference on Visual Communications and I...

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IEEE visual communications and image processing (VCIP)

2021 International conference on visual communications and image processing (VCIP) Proceedings

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Computer graphics and image processing techniques based on visual communication design 4

Computer graphics and image processing techniques based on v...

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4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Chen, Zhaoguo College of Arts Shandong Agricultural Engineering University Shandong Province Jinan250103 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

To fully harness the capabilities of computer graphics and image processing technologies and elevate the quality of visual communication design, this paper presents a comprehensive suite of innovative methodologies. Firstly, the fundamental principles of primary colors are utilized to formulate a series of vector functions, facilitating the precise calculation of error distances and the establishment of an intricate visual communication zoning model. Subsequently, through the exhaustive extraction of pertinent feature information from graphics and images, we achieve the scientific encoding of coefficient-constrained features and determine the corresponding hyperplanes to acquire the relevant parameters for visual communication. Eventually, the implementation of advanced design image expression algorithms markedly enhances the effectiveness of visual communication image representation. The empirical findings reveal that these visual communication design methods exhibit remarkable reliability and feasibility, effectively boosting image recognition accuracy across four distinct image categories with varying levels of complexity, thereby yielding exceptional visual communication outcomes. © 2025 SPIE.

关键词： Design visual communications visualization Computer graphics image processing image storage Color image segmentation image enhancement

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Hybrid Feature Approach for Enhancing Zero-Shot image Classification 4th

Hybrid Feature Approach for Enhancing Zero-Shot Image Classi...

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4th International conference on Artificial Intelligence and Knowledge processing

作者： Khanam, Shaista Sonar, Poonam N. Mumbai Univ Mumbai Maharashtra India

ISBN: (纸本)9783031734762;9783031734779

Zero-shot learning (ZSL) directs the challenge of classifying unseen test images without explicit training on those samples. ZSL can identify and classify unlabeled images available in abundance by learning from visual and semantic embedding vectors (feature vectors). Information-enriched visual features extracted from images play a crucial role in ZSL. This paper proposes a hybrid feature approach that integrates low-level (LL), and high-level (HL) features extracted from images. Gray Level Co-occurrence Matrix (GLCM) and Gabor features are employed to obtain LL texture features, while HL features are derived from the ResNet-50 model, renowned for capturing complex hierarchical representations. These hybrid visual features are then mapped with semantic features using linear mapping, where the semantic features are embedding vectors of labels generated by the fastText model. Experiments on the AWA2 and SUN datasets are conducted in a bid to evaluate the proposed approach's effectiveness. The hybrid feature approach has demonstrated enhanced quality in zero-shot image classification, effectively classifying images that the model has not seen during training.

关键词： Hybrid feature Zero-shot image classification GLCM

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Sequential Structured Fusion of image and Text for Enhanced Multimodal Abstractive Summarization 13th

Sequential Structured Fusion of Image and Text for Enhanced ...

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13th International conference on Natural Language processing and Chinese Computing

作者： He, Rui Qi, Minjie Wang, Hongling Wang, Zhongqing Soochow Univ Nat Language Proc Lab Suzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

The Multimodal Abstractive Summarization task aims to generate a concise summary using given multimodal data (textual and visual). Existing related research is still simple splicing and blending of information from multiple modalities, without considering the interaction between image and corresponding text and the contextual structural relationship of the image and text. We believe that these existing models can't fully integrate multimodal information and leverage the Transformer's ability to process sequential data. To this end, for MAS task, we use image captions that are highly correlated with the image for image fusion;and design image-text alignment tasks to improve the effectiveness of visual modalities in embedding text summary tasks;and propose a sequential structured image-text fusion method to enhance the model's ability of sequences semantic understanding. Through these methods, we can give full play to the contribution of visual modality information to the summary task to enhance the MAS model, thereby generating more accurate summaries. We conducted experiments on related dataset and found that ROUGE-1, ROUGE-2, and ROUGE-L improved by 1.34, 1.64, and 1.32 compared to the baseline model. Additionally, we contributed a large-scale sequential structured multimodal abstractive summarization dataset.

关键词： Multimodal Abstractive Summarization image-Text Fusion Sequential Structured Corpus image Caption

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A zero-reference nighttime road image enhancement method

A zero-reference nighttime road image enhancement method

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2024 International conference on Computer Vision and image processing, CVIP 2024

作者： Xu, Kun Zhang, Yeran Li, Jiahao Cheng, Xin Liu, Zhanwen School of Information Engineering Chang'an University Xi’an710018 China

ISBN: (纸本)9781510688384

The majority of existing low-light image enhancement methods are based on uniform low illumination, they are prone to issues such as overexposure, dark area noise amplification, when applied to nighttime road images with various lighting and high noise levels. This paper proposes a zero-reference two-stage nighttime road image enhancement method. In the first stage, a lightweight visual attention network (LVAN) is developed to generate the dark-aware attention map, effectively avoiding the problems of overexposure and underexposure. The second stage is enhanced with a zero-reference depth image enhancement network in CIELab color space (ZeroDIE_Lab), which enhance image brightness significantly, suppresses noise and artefacts effectively while maintaining color consistency. In comparison with established low-light image enhancement approaches, such as Zero-DCE, the experimental results demonstrate that the proposed method not only enhances the visual clarity and structural details of the image, but also exhibits notable advantages in quantitative evaluation metrics, including image quality assessment and computational complex. © 2025 SPIE.

关键词： attention map CIELab color space lightweight nighttime road images zero-reference

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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th International conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： Machine Learning Deep Learning Computer Vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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A Study on image Reconstruction Based on Decoding fMRI Through Extracting image Depth Features 5th

A Study on Image Reconstruction Based on Decoding fMRI Throu...

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5th International conference on Neural Computing for Advanced Applications (NCAA)

作者： Deng, Xin Bao, Feiyang Liu, Bin Li, Yijia Zhang, Lianhua Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

visualizing perceived content through functional magnetic resonance imaging (fMRI) analysis is a captivating research area in brain decoding. Previous studies have primarily focused on restoring either high-level semantic features or low-level semantic features from fMRI data, but rarely achieved effective restoration of both. This study proposes a novel approach for decoding the visual cortex activity measured by fMRI into the layered visual features that share the hierarchical information from the corresponding images. By iteratively optimizing the relationship between the layered visual features and the image's depth features extracted by a visual transformer, the method in this research significantly improves the reconstruction of the image's deep features. Furthermore, by incorporating the prior natural image information through a deep generator network, this work enhances the reconstruction process, resulting in richer semantic details. Experimental results verify the effectiveness of our methodology in restoring both high-level and low-level semantic features of the perceived images, ultimately enhancing the overall visual fidelity of the reconstructed image. Importantly, our model demonstrates successful generalization to reconstruct artificial shapes, indicating that the performance of our model is not simply achieved by relying on extensive sample datasets. These findings prove the efficacy of the method in effectively reconstructing the perceived content based on the hierarchical neural representations, providing a new method to study the brain's underlying mechanisms.

关键词： image reconstruction Neural decoding Functional magnetic resonance imaging Feature extraction visual Transformer

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Proceedings of the 2024 6th International conference on Video, Signal and image processing, VSIP 2024

Proceedings of the 2024 6th International Conference on Vide...

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6th International conference on Video, Signal and image processing, VSIP 2024

ISBN: (纸本)9798400709647

The proceedings contain 26 papers. The topics discussed include: underwater aquaculture object contour detection dataset with benchmarks;the detection of foreign objects on coal mine conveyor belts based on semi-supervised learning;abnormal cable detection at the bottom of high-speed train using improved Yolov8;research on image edge detection algorithms based on fractional-order differentiation;weakly supervised salient object detection via hybrid pseudo labels;region feature-guided automated seed selection region growing method for chip-surface-defect detection;enhanced multi-modal heart segmentation approach based on SWIN-transformer for small datasets;blind image quality assessment based on human visual perception;and hybrid loss and 3D convolutional multilayer perceptron for hyperspectral images classification with noisy labels.

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Underwater image Denoising and Enhancement via Diffusion Models and Attention Mechanisms 24

Underwater Image Denoising and Enhancement via Diffusion Mod...

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4th International conference on Signal processing and Communication Technology, SPCT 2024

作者： Chen, Wei Gong, Chengxin Liu, Yanghe Wu, Bodong Zhu, Minrui Moscow State University Moscow Russia Shenzhen MSU-BIT University Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Underwater images are widely used in marine science, ocean engineering, and underwater robotics. However, challenges such as insufficient lighting, scattering, and absorption often degrade image quality, limiting their effectiveness. To address this, we propose AquaDiffusionNet, a model combining Diffusion Models and Attention Mechanisms for underwater image denoising and enhancement. Built on the U-Net architecture, it removes complex noise using diffusion models and captures critical features through multi-head attention mechanisms. AquaDiffusionNet achieves simultaneous denoising and detail enhancement, improving image contrast and clarity. Experiments on public datasets like UIEB show significant performance gains, with PSNR increased by 3.0 dB and SSIM by 0.07, outperforming traditional methods. This model enhances visual quality and provides superior input for subsequent image analysis, driving advancements in underwater vision technology. © 2024 Copyright held by the owner/author(s).

关键词： Underwater imaging

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