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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Liping Zhou Zhanhong Huang Zheng Wang Yuxuan Wu Liqun Lin Weiling Chen Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou Fujian China College of Resources and Environment Fujian Agriculture And Forestry University Fuzhou Fujian China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

Inspired by the successful application of visual saliency in image quality evaluation, we propose an image quality metric based on regional saliency pooling. We first introduce the image saliency detection model to obtain regional saliency maps for image sub-patches. Then, the importance of each image sub-patch is calculated using a VGG16 network based on its saliency map. Such importance is referred to as the quality weight, which is also the pooling result in the proposed framework. Finally, the prediction quality is defined as the weighted sum of the Structural SIMilarity (SSIM) of all image sub-patches. In the experimental part, we choose the popular LIVE image quality database. The results show that the performance of our model is highly competitive, which indicates the effectiveness of the proposed saliency pooling strategy.

关键词： Measurement Image quality Wireless communication visualization Databases Performance gain Saliency detection

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Error Resilience of Haptic Data in Interactive Systems

Error Resilience of Haptic Data in Interactive Systems

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Yiwen Xu Yaping Huang Weiling Chen Hong Xue Tiesong Zhao Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

Haptic technology has been widely applied in various fields as a new type of immersive interaction method. In a haptic interaction system, data may be lost due to the fluctuation of network environment, which obviously has negative influence on user's quality of experience. Error resilience is an effective method to solve the above problem. In this paper, we first establish a real haptic interaction system as the platform of the successive experiments. Then, a new quality metric named haptic perception metric (HPM) is proposed. Based on this metric, we design experiments to determine an optimal prediction algorithm for error resilience. Experimental results show that the prediction algorithm can effectively implement error resilience under different packet loss rates. It can also be confirmed that the proposed HPM metric can better reflect the quality of haptic interaction.

关键词： Haptic interfaces Resilience Prediction algorithms Receivers Data compression Packet loss

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HEVC Compression Artifact Reduction with Generative Adversarial Networks

HEVC Compression Artifact Reduction with Generative Adversar...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Shiqi Yu Bolin Chen Yiwen Xu Weiling Chen Zhonghui Chen Tiesong Zhao Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

Video compression technology is significant to video transmission and storage. However, compression artifacts arise in videos. Specifically, coarse quantization eliminates video details and degrades visual quality. Most of artifact reduction methods use filter processing or Mean Square Error (MSE)loss that leads to over-smoothing results. Moreover, most of the methods target to reduce single image compression artifact instead of video artifact. In this paper, we present an adversarial learning method with recurrent framework called Video Artifact Reduction Generative Adversarial Network (VRGAN). Our network contains a generator with recurrent framework that improves video consistency, a dense block that enhances receptive field for large transform unit, and a relativistic discriminator that evaluates the relationship between the generated frames and the original high-quality frames. Our VRGAN is able to generate more realistic videos. The effectiveness in reducing video compression artifacts of the method has been demonstrated qualitatively and quantitatively. The performance comparison with previous works shows the superiority of the proposed method.

关键词： Generators Convolution Gallium nitride visualization Transform coding Estimation Image coding

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Research Progress on Benign and Malignant Lung Nodule Classification Based on Deep Learning

Research Progress on Benign and Malignant Lung Nodule Classi...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Lanlan Li Fusheng Yang Juan Li Bin Xu Dabiao Wang Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou Fujian China The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou Guangdong China College of Mechanical Engineering and Automation Fuzhou University Fuzhou Fujian China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

Lung cancer is a malignant tumor with extremely high morbidity and mortality. The early classification of benign and malignant lung nodules is of great significance for the lung cancer treatment. In recent years, the application of deep learning on the classification of benign and malignant lung nodules has become a new development trend. This article reviewed the latest research in this field in two-dimensional and three-dimensional deep learning, compared the model performances and analyzed the research trend in the next sten.

关键词： Deep learning Wireless communication Solid modeling Malignant tumors Lung Lung cancer Market research

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A CNN-based Cleanliness Evaluation for Bowel Preparation in Colonoscopy

A CNN-based Cleanliness Evaluation for Bowel Preparation in ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Youxing Zhu Yiwen Xu Weiling Chen Tiesong Zhao Shaohua Zheng Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

Colonoscopy is an effective measure for detecting early colorectal lesions. To obtain high-quality pictures during colonoscopy examination, the bowel preparation, or cleansing, is considered as the first and critical step. To date, the evaluation of bowel preparation quality is performed by colonoscopists in most of gastroenterology departments. This manual labeling is time-consuming, uneconomical and sometimes unreliable subject to expertise of physicians. In this work, we propose an automatic evaluation model that labels the cleanliness for bowel preparation in accordance with the Boston Bowel Preparation Scale (BBPS). A compact convolutional neural network (CNN) with 2 Densenet layers which have feature reuse mechanism embedded before the softmax classifier is utilized. The proposed model achieves promising evaluation performance on our dataset as well as on a public dataset named Nerthus where is better than the existing research results. The average correct evaluation ratio is up to 89.58% in 20 individual cases (573 images in total), where each case includes 16 images at least. The method proposed in this paper is considered to be effective and feasible by professional colonoscopists and can be used for clinical auxiliary diagnosis.

关键词： Colonoscopy Colon Training Testing Cancer Convolution

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer operating systems

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Research on Video Motion Characteristics Extraction and Description Based on Human visual Characteristics 2

Research on Video Motion Characteristics Extraction and Desc...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Ying Zhou Yongsheng Liang Wei Liu Lixia Zhao Shenzhen Key Laboratory of Visual Media Processing and Transmission Shenzhen Institute of Information Technology

With the rapid development of Internet and multimedia, there is new challenge on streaming media processing and transmission. On one hand, the demand for the quantity and quality of multimedia resourc

ISBN: (纸本)9781467389808

关键词： visualization Streaming media Video sequences visual perception Market research Standards Complexity theory

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visual Attention and Haptic Control: A Cross-Study

Visual Attention and Haptic Control: A Cross-Study

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Hong Xue Tiesong Zhao Weiling Chen Qian Liu Shaohua Zheng Chang Wen Chen Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Dept. of Computer Science and Technology Dalian University of Technology Dalian China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

The variety of multimedia big data has promoted emerging applications of multiple sensorial media (mulsemedia) types, in which haptic information attracts increasing attentions. Until now, the interaction between haptic signal and conventional audio-visual signals have not been fully investigated. In this work, we make an exploration on the cross-modal interactivity in task-driven scenarios. We first explore the correlation between visual attention and haptic control in three designed tasks: random-trajectory, fixed-trajectory and obstacle-avoidance. Then, we propose a visual-haptic interaction model that estimates kinesthetic position of haptic control with the information of gaze only. By incorporating a Long Short-Term Memory (LSTM) neural network, the proposed model provides effective prediction in the scenarios of fixed-trajectory and obstacle-avoidance, with its performance superior to other selected machine learning-based models. To further examine our model, we execute it in a haptic control task using visual guidance. Implementation results show a high task achievement rate.

关键词： Task analysis Haptic interfaces visualization Correlation Software Tracking Process control

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Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding

arXiv

引用

arXiv 2018年

作者： Shen, Ying Zhang, Qiang Zhang, Jin Huang, Jiyue Lu, Yuming Lei, Kai Peking University SHENZHEN518055 China ShenZhen Key Lab for Visual Media Processing and Streaming Media ShenZhen Institute of Information Technology SHENZHEN518172 China

. Automatic text classification (TC) research can be used for real-world problems such as the classification of in-patient discharge summaries and medical text reports, which is beneficial to make medical documents more understandable to doctors. However, in electronic medical records (EMR), the texts containing sentences are shorter than that in general domain, which leads to the lack of semantic features and the ambiguity of semantic. To tackle this challenge, we propose to add word-cluster embedding to deep neural network for improving short text classification. Concretely, we first use hierarchical agglomerative clustering to cluster the word vectors in the semantic space. Then we calculate the cluster center vector which represents the implicit topic information of words in the cluster. Finally, we expand word vector with cluster center vector, and implement classifiers using CNN and LSTM respectively. To evaluate the performance of our proposed method, we conduct experiments on public data sets TREC and the medical short sentences data sets which is constructed and released by us. The experimental results demonstrate that our proposed method outperforms state-of-the-art baselines in short sentence classification on both medical domain and general domain. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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SALIENCY DETECTION USING TENSOR SPARSE RECONSTRUCTION RESIDUAL ANALYSIS

SALIENCY DETECTION USING TENSOR SPARSE RECONSTRUCTION RESIDU...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Yi Zeng Yi Xu Shanghai Key lab of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479999897

In this paper, a visual saliency detection model based on tensor sparse reconstruction for images is proposed. This algorithm measures saliency value of image regions by the reconstruction residual and performs better on color images than current sparse models. Current sparse models treat a color image as multiple independent channel images and vectorise the image patches, ignoring interrelationship between color channels and spatial correlation between neighbouring pixels. In contrast, the proposed tensor sparse model treats a color image as a 3D array, retaining the spatial color structures entirely during the sparse coding. The proposed saliency detection method is tested on ASD dataset and OSIE dataset and compared with traditional sparse reconstruction based models. The experimental results show that our model achieves higher AUC scores than traditional sparse reconstruction based models.

关键词： tensor tensor orthogonal matching pursuit (TOMP) saliency detection sparse reconstruction residual Tensor color image RESIDUAL Reconstructions region of an image Reconstructive Surgical Procedures images inspection methods

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