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RGBD-based Real-time Volumetric Reconstruction System: Architecture Design and Implementation

RGBD-based Real-time Volumetric Reconstruction System: Archi...

引用

IEEE visual communications and image processing (VCIP)

作者： Kai Zhou Shuai Guo Jingchuan Hu Jionghao Wang Qiuwen Wang Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781665475938

With the increasing popularity of commercial depth cameras, 3D reconstruction of dynamic scenes has aroused widespread interest. Although many novel 3D applications have been unlocked, real-time performance is still a big problem. In this paper, a low-cost, real-time system: LiveRecon3D, is presented, with multiple RGB-D cameras connected to one single computer. The goal of the system is to provide an interactive frame rate for 3D content capture and rendering at a reduced cost. In the proposed system, we adopt a scalable volume structure and employ ray casting technique to extract the surface of 3D content. Based on a pipeline design, all the modules in the system run in parallel and are designed to minimize the latency to achieve an interactive frame rate of 30 FPS. At last, experimental results corresponding to implementation with three Kinect v2 cameras are presented to verify the system's effectiveness in terms of visual quality and real-time performance.

关键词： visualization Three-dimensional displays Costs visual communication Pipelines Systems architecture Cameras

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Cross-Layer Feature based Multi-Granularity visual Classification

Cross-Layer Feature based Multi-Granularity Visual Classific...

引用

IEEE visual communications and image processing (VCIP)

作者： Junhan Chen Dongliang Chang Jiyang Xie Ruoyi Du Zhanyu Ma Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

In contrast to traditional fine-grained visual clas-sification, multi-granularity visual classification is no longer limited to identifying the different sub-classes belonging to the same super-class (e.g., bird species, cars, and aircraft models). Instead, it gives a sequence of labels from coarse to fine (e.g., Passeriformes → Corvidae → Fish Crow), which is more convenient in practice. The key to solving this task is how to use the relationships between the different levels of labels to learn feature representations that contain different levels of granularity. Interestingly, the feature pyramid structure naturally implies different granularity of feature representation, with the shallow layers representing coarse-grained features and the deep layers representing fine-grained features. Therefore, in this paper, we exploit this property of the feature pyramid structure to decouple features and obtain feature representations corre-sponding to different granularities. Specifically, we use shallow features for coarse-grained classification and deep features for fine-grained classification. In addition, to enable fine-grained features to enhance the coarse-grained classification, we propose a feature reinforcement module based on the feature pyramid structure, where deep features are first upsampled and then combined with shallow features to make decisions. Experimental results on three widely used fine-grained image classification datasets such as CUB-200-2011, Stanford Cars, and FGVC-Aircraft validate the method's effectiveness. Code available at https://***/PRIS-CV/CGVC.

关键词： visualization Cross layer design Codes visual communication image processing Fish Birds

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Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus images

Improved PSP-Net Segmentation Network for Automatic Detectio...

引用

IEEE visual communications and image processing (VCIP)

作者： Qiuming Liu Shen Wang Yulan Dai Jintao Zhang Yichen Wang Ruoxuan Zhou School of Software Engineering Jiangxi Univ. of Sci. and Tech. Jiangxi China School of Computer Science and Engineering Central South University Changsha China School of Computer Info. Engineering Jiangxi Normal University Jiangxi China Factory and Cultural Communications Nanchang Key lab. of Virtual Digital Jiangxi China

ISBN: (纸本)9781665475938

Proliferative Diabetic Retinopathy (PDR) is a seri-ous retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on im-proved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832,0.9265,0.9897,0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

关键词： visualization Sensitivity image color analysis visual communication Retinopathy Semantic segmentation Neural networks

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No-Reference Stereoscopic image Quality Assessment Based on Convolutional Neural Network with A Long-Term Feature Fusion

No-Reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Sumei Wang, Mingyi Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

With the rapid development of three-dimensional (3D) technology, the effective stereoscopic image quality assessment (SIQA) methods are in great demand. Stereoscopic image contains depth information, making it much more challenging in exploring a reliable SIQA model that fits human visual system. In this paper, a no-reference SIQA method is proposed, which better simulates binocular fusion and binocular rivalry. The proposed method applies convolutional neural network to build a dual-channel model and achieve a long-term process of feature extraction, fusion, and processing. What's more, both high and low frequency information are used effectively. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods and has a promising generalization ability.

关键词： stereoscopic image quality assessment (SIQA) convolution neural network (CNN) human visual system (HVS) binocular fusion

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Dynamic Mesh Commonality Modeling Using the Cuboidal Partitioning

Dynamic Mesh Commonality Modeling Using the Cuboidal Partiti...

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IEEE visual communications and image processing (VCIP)

作者： Ashek Ahmmed Manoranjan Paul Manzur Murshed Mark Pickering School of Engineering and Information Technology University of New South Wales Australia School of Computing and Mathematics Charles Sturt University Australia School of Engineering Information Technology and Physical Sciences Federation University Australia

ISBN: (纸本)9781665475938

For 3D object representation, volumetric contents like meshes and point clouds provide suitable formats. However, a dynamic mesh sequence may require significantly large amount of data because it consists of information that varies with time. Hence, for the facilitation of storage and transmission of such content, efficient compression technologies are required. MPEG has started standardization activities aiming to develop a mesh compression standard that would be able to handle dynamic meshes with time varying connectivity information and time varying attribute maps. The attribute maps are features associated with the mesh surface and stored as 2D images/videos. In this paper, we propose to capture the commonality information in the dynamic mesh attribute maps using the cuboidal partitioning algorithm. This algorithm is capable of modeling both the global and local commonality within an image in a compact and computationally efficient way. Experimental results show that the proposed approach can outperform the anchor HEVC codec, suggested by MPEG to encode such sequences, with a bit rate savings of up to 3.66%.

关键词： image coding Three-dimensional displays Codecs visual communication Heuristic algorithms Computational modeling Bit rate

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NIR image colorization with graph-convolutional neural networks

NIR image colorization with graph-convolutional neural netwo...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Valsesia, Diego Fracastoro, Giulia Magli, Enrico Politecn Torino Turin Italy

ISBN: (纸本)9781728180687

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.

关键词： NIR colorization graph neural network

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Flocking Birds of a Feather Together: Dual-step GAN Distillation via Realer-Fake Samples

Flocking Birds of a Feather Together: Dual-step GAN Distilla...

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IEEE visual communications and image processing (VCIP)

作者： Jingwen Ye Zunlei Feng Xinchao Wang National University of Singapore Zhejiang University

ISBN: (纸本)9781665475938

Recent Generative Adversarial Networks (GAN) models deliver visually authentic images and find their applications in various applications. Due to their heavy size, however, it remains cumbersome to deploy state-of-the-art GAN on the edge side, such as mobile terminals. In this paper, we propose a practical and very effective approach for distilling GAN models, so as to produce competent student models that come with smaller sizes. Unlike other convolutional neural network models whose final output spans a small or reasonably-sized space, based on which knowledge distillation is carried out, GAN produces images as output that span an enormous space, making the distillation much more challenging. To this end, we propose an effective dual-step distillation approach tailored for GAN. Instead of directly enforcing the student output distribution to imitate that of the teacher, we partition the generated images into two sets, the “realer” one and the “fake” one, based on the image quality, and sequentially minimize the discrepancy first between fake images of the student and the teacher, and then between realer ones. This seemingly simple strategy turns out highly effective in terms of exploring output space structure and further the knowledge transfer. Experimental results have shown that the proposed method can be easily generalized and demonstrate the learned portable generator produces competitive or even better results both in quality and quantity.

关键词： Training Knowledge engineering image quality visual communication image processing image edge detection Generative adversarial networks

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Center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation

Center-free intuitionistic fuzzy c-means clustering algorith...

引用

Natural Language processing (ICNLP), International conference on

作者： Lan Rong Wang Shumin He Hu Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to address the issue that the center-free fuzzy c-means (CFFCM) clustering algorithm does not consider the texture features and spatial information of pixels, and the time complexity is too high, a center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation is proposed. In the proposed algorithm, the voting model is used to generate intuitionistic fuzzy sets (IFS), and the generated hesitation degree and membership degree are combined with spatial information to design a spatial intuitionistic membership degree similarity model. This model can deal with the similarity between pixels and classes in gray information, so the segmentation efficiency is improved. At the same time, the intuitionistic fuzzy local binary pattern (IFLBP) operator is used to extract the image texture information and introduce it into the objective function. Spatial membership similarity model is used to process texture information and improve the segmentation accuracy of the algorithm. The results of simulation experiment show that the proposed has advantages in both visual effect and evaluation index.

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Exploring image-Based Approaches for Effective Synthetic Time-Series Data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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7th International conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 IEEE.

关键词： Photointerpretation

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UGNet: Underexposed images Enhancement Network based on Global Illumination Estimation

UGNet: Underexposed Images Enhancement Network based on Glob...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Fang, Yuan Zhu, Wenzhe Zhu, Qing Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781728180687

This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.

关键词： underexposed enhancement global illumination estimation image restoration smooth dilated convolution

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