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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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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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Design and Implementation of image Description Model Using Artificial Intelligence Based Techniques 3rd

Design and Implementation of Image Description Model Using A...

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3rd International conference on Computational Electronics for Wireless communications, ICCWC 2023

作者： Ingale, Sumedh Bamnote, G.R. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9789819719457

The process that produces written descriptions that effectively represent the meaning and context of an image is known as image captioning. To integrate visual and textual data, it needs to blend computer vision and natural language processing methods. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such as long-short-term memory (LSTM) networks, are two methods used for captioning images. High-level visual features from the input image are extracted by the CNN, and the RNN uses those features to generate the matching captions. Residual Network is a deep CNN architecture with exceptional results in a range of computer vision tasks, including the classification of images. ResNet has been used as the foundation for extracting picture features. ResNet and CNN abstracts an image's visual information via feature extraction and uses neural networks with recurrent architecture to provide meaningful, contextually appropriate captions. Machines are now able to comprehend and speak about the visual world in a manner similar to humans because of the integration of computer vision and natural language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Learning Multi-semantic Based on Cross-Attention for image-Text Retrieval 7th

Learning Multi-semantic Based on Cross-Attention for Image-T...

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8th Asia Pacific Web and Web-Age Information Management Joint International conference on Web and Big Data

作者： Lu, Bo Gao, Ying Zhao, Tianbao Yuan, Xia Zhu, Haibin Gan, Lin Duan, Xiaodong Dalian Minzu Univ Dalian 116650 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Cross-modal retrieval is a challenging problem for processing multi-modal information, primarily addressing the semantic gap between different modalities. However, current research mainly focuses on aligning image and text regions, which often ignore the importance of contextual background information. Specially, there are uncertainties among heterogeneous modalities which persist in understanding semantic information of cross-modal retrieval. In this paper, we propose a novel MultiSemantic Based on Cross-Attention Network (MSCA) for cross-modal retrieval tasks. Specifically, a cross-extraction of contextual semantic feature encoder module and a graph structure enhancement module are firstly designed to more effectively address the issue of neglecting contextual background information and to further enhance the fusion of information and features, respectively. Secondly, we effectively solve the cross-modal alignment problem by employing a probability distribution encoder, simultaneously, we integrate three pre-training models, which include image-text contrastive learning, image-text matching loss and masked language model loss for reducing uncertainties among different modalities. We conducted extensive experiments on two large datasets, MS-COCO and Flickr30K. The experimental results show that the MSCA model outperforms other existing methods on both datasets, demonstrating its effectiveness and significant performance advantages.

关键词： image-Text Retrieval Feature Fusion Coarse-Grained Alignment Fine-Grained Alignment

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LIESA: Low-Light image Enhancement with Semantic Awareness 31st

LIESA: Low-Light Image Enhancement with Semantic Awareness

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31st International conference on Multimedia Modeling, MMM 2025

作者： Zhang, Jingyao Hao, Shijie Sun, Fuming Rao, Yuan Hefei University of Technology Hefei China Dalian Minzu University Dalian China Anhui Agricultural University Hefei China

ISBN: (纸本)9789819620609

Low-light image enhancement (LLIE) is a critical low-level image processing task aimed at improving the visual quality of dimly lit images, thereby enhancing user experience and supporting subsequent applications. Recent research has explored incorporating semantic information into LLIE models. However, these methods often suffer from high computational costs during training due to the optimization of semantic-related loss functions or inadequate utilization of semantic features. This paper presents a novel model, Low-light image Enhancement with Semantic Awareness (LIESA), designed to address these limitations. LIESA focuses on adjusting the illumination component of an image and employs a triple-branch encoder-decoder architecture. To effectively integrate semantic and reflectance features into the illumination feature hierarchy, we introduce multiple Lighting Optimization Modules (LOMs) at different stages of the illumination encoder-decoder branch. In LOMs, two sub-modules, Semantic Prior Fusion Module (SFM) and Illumination Attention Module (IAM), are designed to fuse information from the illumination, reflectance and semantic branches. The design of our model facilitates the incorporation of high-level and low-level features across different resolutions, thereby leading to more effective enhancement in terms of improving visibility and preserving visual naturalness. Experimental evaluations on ten public datasets demonstrate LIESA’s superior performance in terms of both visual quality and quantitative metrics compared to state-of-the-art LLIE methods that incorporate semantic information. Ablation studies further corroborate the effectiveness of LIESA’s key components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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6th International image processing and communications conference, IPandC 2014

6th International Image Processing and Communications Confer...

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6th International image processing and communications conference, IPandC 2014

ISBN: (纸本)9783319106618

The proceedings contain 39 papers. The special focus in this conference is on image processing and communications. The topics include: Two-Dimensional hidden Markov models in road signs recognition;evaluating the mutual position of objects on the visual scene using morphological processing and reasoning;clustering-based retrieval of similar outfits based on clothes visual characteristics;improving shape retrieval and classification rates through low-dimensional features fusion;accelerating the 3D random walker image segmentation algorithm by image graph reduction and GPU computing;computed tomography images denoising with Markov random field model parametrized by Prewitt mask;Gaussian mixture model based non-local means technique for mixed noise suppression in color images;robust image retrieval based on mixture modeling of weighted Spatio-color information;noise reduction in ultrasound images based on the concept of local neighbourhood exploration;Viterbi algorithm for noise line following robots;hybrid shape descriptors for an improved weld defect retrieval in radiographic testing;on the usefulness of combined metrics for 3D image quality assessment;image processing with process migration;comparison of assessment regularity methods dedicated to isotropic cells structures analysis;a texture-based energy for active contour image segmentation;object localization and detection using variance filter;the impact of the image feature detector and descriptor choice on visual SLAM accuracy and shape versus texture.

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A Comparative Study of Inference Models Based on image Recognition 7th

A Comparative Study of Inference Models Based on Image Recog...

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7th International conference on Wireless communications, Networking and Applications, WCNA 2023

作者： Zhao, Deling Internet Information Research Institute Communication University of China Beijing China

ISBN: (纸本)9789819624089

Due to the limitation of web-side processing capabilities, the existing mainstream inference models adopt the client-to-server operation mode, and the emergence of WebAssembly has brought opportunities for the client to run the inference model alone for image recognition, and soon, the browser will fully support the standard. Therefore, on the basis of image recognition, this paper runs the mainstream inference model on the client and server side, and then compares the inference rate of different inference models at different batch sizes and the inference efficiency of the same inference model on the client and server side, conducts a large number of comparative experiments to record the experimental results, and then uses the method of averaging the experimental data to obtain a set of reliable experimental conclusions. Experimental results show that the performance of server-side TensorRT is better than other models under the same configuration conditions, ONNXRuntime is better than PyTorch in small batches, and PyTorch's performance improvement in accuracy has more room for optimization. The results of web-side experiments show that the inference model has a large difference in GPU usage, ONNXRuntime shows a greater performance advantage in the efficiency of GPU inference, while TensorRT will show better performance with the increase of batch. Compared with server-side inference, web-side inference shows stronger stability because it relies less on network transmission, so it can be preferred to direct inference when the batch is small, and server inference is preferred when the client configuration does not meet the conditions. © The Author(s) 2025.

关键词： Graphics processing unit

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Applying Digital Restoration Techniques in Preservation of Ancient Murals Using Diffusion-Based Inpainting 16th

Applying Digital Restoration Techniques in Preservation of A...

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16th International conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Tint, Khant Khant Win Tin, Mie Mie Zin, Thi Thi Tin, Pyke Pyin Oo Lwin Myanmar Graduate School of Engineering University of Miyazaki 1-1 Gakuen Kibanadai-Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615308

Nowadays, with the assistance of advanced technology, everything is constantly evolving in every sector, such as transportation, economics, medication, and so on. Also, in the sector of cultural heritage, image inpainting technology has become very popular as an effective way that allows conservators to digitally restore damaged murals without physically altering the original artwork, preserving its integrity and historical authenticity. In this paper, an image inpainting framework is proposed to restore the ancient murals of Myanmar from AD 1800–1900. The framework can be subdivided into crack removal and lacuna removal. The identification of crack and lacuna damage is automatically done with segmentation and image processing methods. Crack damage is reconstructed with pixel neighboring transfer, while lacuna reconstruction is applied with coherent transport and patch-based nearest neighbor similarity color filling methods. The accuracy is tested with the damage ratio analysis, and the experimental result demonstrates that the framework can deliver satisfactory visual results in the reconstruction process of the murals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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