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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

IEEE visual communications and image processing (VCIP)

作者： Chongxin Li Jixiang Luo Wenrui Dai Chenglin Li Junni Zou Hongkai Xiong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： Entropy image coding Context modeling Transforms Entropy coding Decoding Solid modeling

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Wearable visually Assistive Device for Blind People to Appreciate Real-world Scene and Screen image

Wearable Visually Assistive Device for Blind People to Appre...

引用

IEEE visual communications and image processing (VCIP)

作者： Jin Ai Menghan Hu Guodong Li Guangtao Zhai Jian Zhang Qingli Li Wendell Q. Sun University of Shanghai for Science and Technology China East China Normal University China Ministry of Education China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Due to the loss of vision, the appreciation of the realworld scene and the images displayed on the screen becomes almost impossible for blind people. In an effort to meet the needs of the blind community, we develop a wearable visually assistive device to help them perceive images. With the help of various multimedia information processing technologies, the proposed device can first acquire image information through a depth camera, then implement an image-to-text transformation using image caption technology, and finally the obtained text sequence is fed back to the user via voice. In this way, blind people are able to perceive the outside world, thus creating an unprecedented experience for them. The main technical specifications of the system are: distance perception range is 0.1m to 10m; RGB field of view is 69.4°×42.5°×77°; depth field of view is 91.2°×65.5°×100.6°; maximum weight is 3.05kg. Two demo videos of the proposed navigation system which are respectively recorded for real-world scene and screen image are available at: https://***/10.6084/***.12520499.v1.

关键词： Blindness Assistive devices Sun Object detection Man-machine systems Loss measurement Information processing

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Depthwise Separable Convolutional Neural Network for image Forensics 34

Depthwise Separable Convolutional Neural Network for Image F...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Chen, Yifang Peng, Feng Kang, Xiangui Jane Wang, Z. Sun Yat-sen University Guangdong Key Lab of Information Security School of Data and Computer Science China ECE Dept University of British Colombia VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9781728137230

General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensics-related features from image patches with better parameter efficiency. When compared with four state-of-The-Art methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 × 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression. © 2019 IEEE.

关键词： Convolution

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Special Cane with visual Odometry for Real-time Indoor Navigation of Blind People

Special Cane with Visual Odometry for Real-time Indoor Navig...

引用

IEEE visual communications and image processing (VCIP)

作者： Jingyu Tang Menghan Hu Guodong Li Qingli Li Jian Zhang Xiaofeng Zhou Guangtao Zhai Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China Key Laboratory of Artificial Intelligence Ministry of Education China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Indoor navigation is urgently needed by blind people in their everyday lives. In this paper, we design an assistive cane with visual odometry based on actual requirements of the blind to aid them in attaining safe indoor navigation. Compared to the state-of-the-art indoor navigation systems, the proposed device is portable, compact, and adaptable. The main specifications of the system are: the perception range is respectively from 0.10m to 2.10m, and 0.08m to 1.60m for width and length dimensions; the maximum weight is 2.1kg; the detection range is from 0.15m and 3.00m; the cruising ability is about 8h; and the objects whose heights are below 80cm can be detected. The demo video of the proposed navigation system is available at: https://***/10.6084/***.12399572.v1.

关键词： Indoor navigation Real-time systems Blindness Legged locomotion Cameras visual odometry Man-machine systems

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Super Resolution Approach Using Generative Adversarial Network Models for Improving Satellite image Resolution 6th

Super Resolution Approach Using Generative Adversarial Netwo...

引用

6th International conference on Information Management and Big Data, SIMBig 2019

作者： Pineda, Ferdinand Ayma, Victor Aduviri, Robert Beltran, Cesar Pontifical Catholic University of Peru Lima Peru Altiplano National University Puno Peru

ISBN: (纸本)9783030461393

Recently, the number of satellite imaging sensors deployed in space has experienced a considerable increase, but most of these sensors provide low spatial resolution images, and only a small proportion contribute with images at higher resolutions. This work proposes an alternative to improve the spatial resolution of Landsat-8 images to the reference of Sentinel-2 images, by applying a Super Resolution (SR) approach based on the use of Generative Adversarial Network (GAN) models for image processing, as an alternative to traditional methods to achieve higher resolution images, hence, remote sensing applications could take advantage of this new information and improve its outcomes. We used two datasets to train and validate our approach, the first composed by images from the DIV2K open access dataset and the second by images from Sentinel-2 satellite. The experimental results are based on the comparison of the similarity between the Landsat-8 images obtained by the super resolution processing by our approach (for both datasets), against its corresponding reference from Sentinel-2 satellite image, computing the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity (SSIM) as metrics for this purpose. In addition, we present a visual report in order to compare the performance of each trained model, analysis that shows interesting improvements of the resolution of Landsat-8 satellite images. © Springer Nature Switzerland AG 2020.

关键词： Landsat

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Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition

Attribute Mix: Semantic Data Augmentation for Fine Grained R...

引用

IEEE visual communications and image processing (VCIP)

作者： Hao Li Xiaopeng Zhang Qi Tian Hongkai Xiong Shanghai Jiao Tong University Huawei Inc.

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Collecting fine-grained labels usually requires expert-level domain knowledge and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples. The principle lies in that attribute features are shared among fine-grained sub-categories, and can be seamlessly transferred among images. Toward this goal, we propose an automatic attribute mining approach to discover attributes that belong to the same supercategory, and Attribute Mix is operated by mixing semantically meaningful attribute features from two images. Attribute Mix is a simple but effective data augmentation strategy that can significantly improve the recognition performance without increasing the inference budgets. Extensive experiments and ablation studies show that the proposed method consistently outperforms the state-of-the-art methods on challenging benchmarks including CUB-200-2011, FGVC-Aircraft, and Stanford Cars.

关键词： Training Automobiles Birds Semantics image recognition image color analysis Head

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A QoE-oriented Saliency-Aware Approach for 360-degree Video Transmission 34

A QoE-oriented Saliency-Aware Approach for 360-degree Video ...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Shen, Wang Ding, Lianghui Zhai, Guangtao Cui, Ying Gao, Zhiyong Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai China

ISBN: (纸本)9781728137230

The tradeoff between bandwidth efficiency and quality of experience (QoE) is a key issue in 360 video transmission. In this paper, we propose a QoE-oriented saliency-Aware 360 video transmission framework to balance this tradeoff. The target is to reduce the bandwidth demand without declining the QoE. Specifically, the proposed model is based on the decision-making process. We use Lyapunov optimization to solve the decisionmaking problem. Furthermore, we integrate saliency information into the model to influence the decision policy, so that the model has the advantage of bandwidth efficiency. The simulation results show that the tradeoff parameter of Lyapunov optimization can balance the tradeoff between QoE and bandwidth efficiency, and 360 video saliency entropy limits the upper and lower bounds of QoE and bandwidth efficiency. © 2019 IEEE.

关键词： Video streaming

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Video Super Resolution Using Temporal Encoding ConvLSTM and Multi-Stage Fusion

Video Super Resolution Using Temporal Encoding ConvLSTM and ...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuhang Zhang Zhenzhong Chen Shan Liu Wuhan University Wuhan China Tencent Media Lab Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Video super resolution is a challenging task and has attracted the attention of many researchers in recent years. In this paper, we propose a multi-stage spatio-temporal feature fusion network. Different from existing methods that only aggregate features from temporal branch once at a specified s tage of network, the proposed network is organized in a multi-stage manner so that the temporal correlation in features at different stages of the network can be fully exploited. Furthermore, we propose the temporal encoding convLSTM to effectively capture the temporal information at the end of each stage. Experiments on vid4 and viemo-90K demonstrate the effectiveness of the proposed method.

关键词： Feature extraction Correlation Encoding image reconstruction Training Task analysis Decoding

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Modeling Human Activities via Long Short Term Memory Networks 27

Modeling Human Activities via Long Short Term Memory Network...

引用

27th Signal processing and communications Applications conference (SIU)

作者： Solmaz, Berkan Karaman, Kaan Aselsan Res Ctr Artificial Intelligence & Informat Technol Res Pr Ankara Turkey METU Ankara Ctr Image Anal OGAM Metu Ankara Turkey METU Ankara Elect & Elect Engn Dept Metu Ankara Turkey

ISBN: (纸本)9781728119045

The presence of rapidly increasing visual data adds importance to the computer vision studies for automatic analysis and interpretation of content. Although the nervous and sensory systems in humans easily perform the processes such as understanding and recognizing activities that take place on a stage, these processes are among the most challenging research topics of computer vision. The activities vary according to the number of participants. For instance, a single person can perform activities consisting of various atomic actions. In the scenes with more than one person, interactions occur between people. Since interactions are mutual movements between multiple people, both temporal changes in the scene and the spatial structures need to be modeled for analysis. In this study, long short term memory networks and support vector machines, based on the positions and distances of human body joints, are trained for the automated classification of actions and interactions.

关键词： action recognition human interactions support vector machines long short term memory networks

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Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

Unsupervised Feedforward Feature (UFF) Learning for Point Cl...

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IEEE visual communications and image processing (VCIP)

作者： Min Zhang Pranav Kadam Shan Liu C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles CA USA Tencent Media Lab Tencent America Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification and segmentation of 3D point clouds is proposed in this work. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture. It learns global shape features through the encoder and local point features through the concatenated encoder-decoder architecture. The extracted features of an input point cloud are fed to classifiers f or shape classification and part segmentation. Experiments are conducted to evaluate the performance of the UFF method. For shape classification, the UFF is superior to existing unsupervised methods and on par with state-of-the-art DNNs. For part segmentation, the UFF outperforms semi-supervised methods and performs slightly worse than DNNs.

关键词： image segmentation Three-dimensional displays Correlation Shape visual communication Feature extraction Feedforward neural networks

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