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

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STSI: Efficiently Mine Spatio- Temporal Semantic Information between Different Multimodal for Video Captioning

STSI: Efficiently Mine Spatio- Temporal Semantic Information...

引用

IEEE visual communications and image processing (VCIP)

作者： Huiyu Xiong Lanxiao Wang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665475938

As one of the challenging tasks in computer vision, video captioning needs to use natural language to describe the content of video. Video contains complex information, such as semantic information, time information and so on. How to synthesize sentences effectively from rich and different kinds of information is very significant. The existing methods often cannot well integrate the multimodal feature to predict the association between different objects in video. In this paper, we improve the existing encoder-decoder structure and propose a network deeply mining the spatio-temporal correlation between multimodal features. Through the analysis of sentence components, we use spatio-temporal semantic information mining module to fuse the object, 2D and 3D features in both time and space. It is worth mentioning that the word output at the previous time is added as the prediction branch of auxiliary conjunctions. After that, a dynamic gumbel scorer is used to output caption sentences that are more consistent with the facts. The experimental results on two benchmark datasets show that our STSI is superior to the state-of-the-art methods while generating more reasonable and semantic-logical sentences.

关键词： visualization Three-dimensional displays Correlation visual communication Fuses image processing Semantics

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

引用

visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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A Large-scale Sports Tracking Dataset and Progressive Re-detection Based Sports Tracking

A Large-scale Sports Tracking Dataset and Progressive Re-det...

引用

IEEE visual communications and image processing (VCIP)

作者： Han Wang Xiaojun Zhou Qinyu Xu Huaqiang Ren Rong Xie Li Song Shanghai Jiao Tong University MIGU Co. Ltd

ISBN: (纸本)9781665475938

Recent years have witnessed the great progress of visual Object Tracking (VOT) which aims to predict the position of an object in each video frame given only its initial appearance. However, even the state-of-the-art methods are confronted with performance degradation, i.e., the tracker drift problem, in sports video scenes (e.g., soccer, basketball). There are two main causes that should be responsible for the tracker drift problem. First, the object of interest is often occluded by other objects that share a similar appearance. Such severe occlusion prevents the model from distinguishing the correct tracking object from other distractors in the future frames. Second, in sports videos, the objects often move fast from one place to another, which incurs severe blurry visual effects among consecutive frames. To address the issues of the tracker drift problem, we treat VOT as a tracking-by-re-detection task. Specifically, we detect candidate objects within a searching area (determined by object location in the previous frame) in the current frame and develop a progressive algorithm to filter out distractors in the area, which proves robust towards occlusion scenarios and tracker drift problems. Combining the advantages of our settings, the proposed framework method is robust to motion blur and object occlusion issues and achieves state-of-the-art tracking results on our challenging dataset.

关键词： visual communication image processing Manuals Filtering algorithms visual effects Search problems Object tracking

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ICCSPA 2020 - 4th International conference on communications, Signal processing, and their Applications

ICCSPA 2020 - 4th International Conference on Communications...

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4th International conference on communications, Signal processing, and their Applications, ICCSPA 2020

ISBN: (纸本)9781728165356

The proceedings contain 54 papers. The topics discussed include: visual heading estimation for UAVs in indoor environments;fog node optimum placement and configuration technique for VANETs;IoT security in healthcare using AI: a survey;mass flow meter and vehicle information DR land vehicles navigation system in indoor environment;performance analysis of OTFS over multipath channels for visible light communication;polarimetric SAR image denoising using the improved IMMSE filter;DOA estimation in wireless seismic surveys using deep learning;and a succinct review of intelligent computational techniques in green cloud computing.

关键词： Surveys

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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...

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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...

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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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