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Multi-Dimension Aware Back Projection Network For Scene Text Detection

Multi-Dimension Aware Back Projection Network For Scene Text...

引用

ieee visual communications and image processing (VCIP)

作者： Yizhan Zhao Sumei Li Yongli Chang School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

Recently, scene text detection based on deep learning has progressed substantially. Nevertheless, most previous models with FPN are limited by the drawback of sample interpolation algorithms, which fail to generate high-quality up-sampled features. Accordingly, we propose an end-to-end trainable text detector to alleviate the above dilemma. Specifically, a Back Projection Enhanced Up-sampling (BPEU) block is proposed to alleviate the drawback of sample interpolation algorithms. It significantly enhances the quality of up-sampled features by employing back projection and detail compensation. Further-more, a Multi-Dimensional Attention (MDA) block is devised to learn different knowledge from spatial and channel dimensions, which intelligently selects features to generate more discriminative representations. Experimental results on three benchmarks, ICDAR2015, ICDAR2017- MLT and MSRA-TD500, demonstrate the effectiveness of our method.

关键词： Deep learning Interpolation visual communication Shape image processing Detectors Benchmark testing

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Neural Network based Inter bi-prediction Blending

Neural Network based Inter bi-prediction Blending

引用

ieee visual communications and image processing (VCIP)

作者： Franck Galpin Philippe Bordes Thierry Dumas Pavel Nikitin Fabrice Le Leannec InterDigital Inc.

ISBN: (纸本)9781728173221

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of −1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.

关键词： Video coding Learning systems Quantization (signal) visual communication image processing Neural networks Motion compensation

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Data Transformer for Anomalous Trajectory Detection

Data Transformer for Anomalous Trajectory Detection

引用

ieee visual communications and image processing (VCIP)

作者： Hsuan-Jen Psan Wen-Jiin Tsai National Yang Ming Chiao Tung University HsinChu Taiwan

ISBN: (纸本)9781728173221

Anomaly detection is an important task in many traffic applications. Methods based on deep learning networks reach high accuracy; however, they typically rely on supervised training with large annotated data. Considering that anomalous data are not easy to obtain, we present data transformation methods which convert the data obtained from one intersection to other intersections to mitigate the effort of collecting training data. The proposed methods are demonstrated on the task of anomalous trajectory detection. A General model and a Universal model are proposed. The former focuses on saving data collection effort; the latter further reduces the network training effort. We evaluated the methods on the dataset with trajectories from four intersections in GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, the proposed anomalous trajectory detection still achieves state-of-the-art accuracy.

关键词： Training Deep learning visual communication image processing Training data Data collection Transformers

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Multiple Stream Oriented Siamese Network for RGB-T Tracking 13

Multiple Stream Oriented Siamese Network for RGB-T Tracking

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Wang, Yimo Du, Songlin Zhou, Quan Kang, Bin Southeast University School of Automation Nanjing China Nanjing University of Posts and Telecommunications College of Communications and Information Engineering Nanjing China Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China

ISBN: (纸本)9781665407854

RGB-T tracker owns the capability of fusing two different yet complementary target observations, thus it will become a promising technology to fulfill all-weather tracking. Existing convolutional neural network based tracking methods often consider the multi-source oriented deep feature fusion from global viewpoint, leading to inevitable negative effect when feature maps of the target pair only contain little useful information. To solve this problem, we propose a four-stream oriented Siamese network, named as FS-Siamese, for RGB-T tracking. In particular, we introduce co-attention mechanism in bilinear pooling to explore the partial feature interaction between the RGB and thermal targets. This can effectively avoid uninformed image blocks disturbing feature embedding fusion. To enhance the efficiency of our Siamese network, we also propose an inner product based logistical loss for training the feature embedding and the graph convolutional neural network based bilinear pooling in an end-to-end manner. Extensive experiments on GTOT datasets demonstrate that the proposed method achieves state-of-the-art performance in the task of RGB-T tracking. © 2021 ieee.

关键词： Convolutional neural networks

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Modern Dance Virtual Person Tracking Technology Based on AdaBoost Algorithm

Modern Dance Virtual Person Tracking Technology Based on Ada...

引用

Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Yanxin Zhang Chung-Ang University Seoul South Korea

ISBN: (纸本)9781665491129

With the development of science and technology, more and more high-tech technologies are applied to the tracking system of dance virtual human. The remote dynamic interaction detection method designed based on AdaBoost algorithm is widely used in real life. This paper mainly designs the remote dynamic interaction, dynamic monitoring and auxiliary functions based on AdaBoost algorithm (image processing and matching algorithm). Firstly, digital coding and compression are widely used in video transmission. Secondly, it makes a brief theoretical analysis of AdaBoost and its related mathematical models, and then establishes a virtual human tracking system using MATLAB software, and gives the corresponding experimental results. The final experimental results show that the detection accuracy of the virtual person tracking technology is more than 90%, and the false detection rate is low.

关键词： Wireless communication Humanities image coding Heuristic algorithms Software algorithms Web servers Software

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A Highly Robust Secret Key Reconcile System Based on Cyclic Shift Buffer and HARQ Mechanism

A Highly Robust Secret Key Reconcile System Based on Cyclic ...

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Haojie Han Linning Peng Hua Fu Yexia Cheng Ye Tian Shen He School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Department of Security Technology Research Institute of China Mobile Communications Corporation Beijing China

Secret keys generated from wireless channel can provide high level of security. The error correction code (ECC) based information reconciliation method owns the benefits of high security and efficiency. In this paper, we designed a highly robust secret key reconcile system using low-density parity-check (LDPC) codes to correct disagreed key bits. In this system, we cache some high-quality secret key bits in a cyclic shift buffer (CSB) for compensation, which can effectively reduce the over-all key disagreement rate (KDR). Combined with a simple hybrid automatic repeat request (HARQ) implementation, this system significantly improves the correction success rate of information reconciliation. We built a realtime secret key generation system using universal software radio peripheral (USRP). Extensive experiments were carried out in different channel state information (CSI) scenarios. The proposed system can improve the success rate of secret key generation effectively in all experiment scenarios, especially in the case of low USRP Tx/Rx gain setups.

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visually Optimized Two-Pass Rate Control for Video Coding Using the Low-Complexity XPSNR Model

Visually Optimized Two-Pass Rate Control for Video Coding Us...

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ieee visual communications and image processing (VCIP)

作者： Christian R. Helmrich Ivan Zupancic Jens Brandenburg Valeri George Adam Wieckowski Benjamin Bross Video Communication and Applications Group Fraunhofer Heinrich Hertz Institute (HHI) Berlin Germany

ISBN: (纸本)9781728173221

Two-pass rate control (RC) schemes have proven useful for generating low-bitrate video-on-demand or streaming catalogs. visually optimized encoding particularly using latest-generation coding standards like Versatile Video Coding (VVC), however, is still a subject of intensive study. This paper describes the two-pass RC method integrated into version 1 of VVenC, an open VVC encoding software. The RC design is based on a novel two-step rate-quantization parameter (R-QP) model to derive the second-pass coding parameters, and it uses the low-complexity XPSNR visual distortion measure to provide numerically as well as visually stable, perceptually R-D optimized encoding results. Random-access evaluation experiments confirm the improved objective as well as subjective performance of our RC solution.

关键词： Video coding visualization image coding visual communication Distortion Software Numerical models

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2021 ieee 3rd International conference on Advanced Trends in Information Theory, ATIT 2021 - Proceedings

2021 IEEE 3rd International Conference on Advanced Trends in...

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3rd ieee International conference on Advanced Trends in Information Theory, ATIT 2021

ISBN: (纸本)9781665438452

The proceedings contain 57 papers. The topics discussed include: text information source modeling for learning monitoring system;providing a desired quality of BPG compressed images for FSIM metric;a model for managing the functional services of a common information space for defense forces based on smart technology;marker information coding for structural clustering of spectral space;web application critical resources protection;model of steganographic system depending on indirect conditional dependencies;method of determining the required number of database nodes in a distributed data processing system;and analysis of noisy image lossy compression by bpg using visual quality metrics.

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SalGFCN: Graph Based Fully Convolutional Network for Panoramic Saliency Prediction

SalGFCN: Graph Based Fully Convolutional Network for Panoram...

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ieee visual communications and image processing (VCIP)

作者： Yiwei Yang Yucheng Zhu Zhongpai Gao Guangtao Zhai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

The saliency prediction of panoramic images is dramatically affected by the distortion caused by non-Euclidean geometry characteristic. Traditional CNN based saliency pre-diction algorithms for 2D images are no longer suitable for 360-degree images. Intuitively, we propose a graph based fully convolutional network for saliency prediction of 360-degree images, which can reasonably map panoramic pixels to spherical graph data structures for representation. The saliency prediction network is based on residual U-Net architecture, with dilated graph convolutions and attention mechanism in the bottleneck. Furthermore, we design a fully convolutional layer for graph pooling and unpooling operations in spherical graph space to retain node-to-node features. Experimental results show that our proposed method outperforms other state-of-the-art saliency models on the large-scale dataset.

关键词： Geometry visual communication image processing Distributed databases Predictive models Prediction algorithms Distortion

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Learning Joint Appearance and Shape Co-Representations for Co-Saliency Detection

Learning Joint Appearance and Shape Co-Representations for C...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guanting Guo Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing leading Co-saliency Detection (CoD) framework aims to segment the co-salient objects by learning the consensus visual representation of the foreground objects. However, despite different categories, some distractors may have similar appearance to the co-salient objects, such as Apples vs. Bananas have similar color and textures. This makes it challenging to distinguish the distractors only through learning the co-salient object appearance representations. To address this issue, we propose a joint appearance and shape co-representation learner for CoD, dubbed as ASCoD. The ASCoD is composed of a Co-Appearance learning Module (CoAM) and a Co-Shape learning Module (CoSM). The CoAM first learns a co-salient object appearance embedding that encodes the global cross-image and spatial context information. Then, this embedding is set as a co-appearance prototype, which guides the model to enhance the features to highlight the co-salient object regions. Afterwards, we design the CoSM that is a cross-attention module, among which the key and the value encode the shape information from a set of salient tokens dynamically selected by a Co-Shape Prototype generation Module (CSPM). Finally, through jointly optimizing the cascaded CoAM and CoSM, the optimal appearance and shape co-representations are achieved, marrying the merits of both appearance and shape co-representations that are not only robust to co-salient objects appearance variations, but also can well discriminate the co-salient objects from the distractors with similar appearance. Extensive evaluations on three challenging benchmarks including CoCA, CoSOD3k and CoSal2015, demonstrate superiority of the ASCoD to a variety of state-of-the-art CoD methods.

关键词： visualization Shape Prototypes Color Signal processing Benchmark testing Robustness Acoustics Speech processing

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