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Angel girl of visually impaired artists: Painting navigation system for blind or visually impaired painters 34

Angel girl of visually impaired artists: Painting navigation...

引用

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

作者： Liu, Hang Hu, Menghan Zhai, Guangtao Huang, Huijing Zhang, Wa Li, Qingli Tian, Yinghong Shi, Yanling East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing China Shanghai Jiao Tong University Institute of Image Communication and Information Processing China

ISBN: (纸本)9781728137230

For those who love painting but unfortunately have visual impairments, holding a paintbrush to create a work is really a difficult task. For the purpose of solving this problem, a painting navigation system for visually impaired painters is introduced through the live demonstration. When painting, the developed system can endow visually impaired persons with the ability to perceive the surrounding environment, thus helping them realize their dream of painting. To achieve this goal, we designed four main modules viz., QR code based drawing board positioning module, brush real-Time positioning module, color recognition module and human-computer interaction module, and integrated them into the system. In the validation experiments, the blindfolded users can successfully create a painting with the help of the developed navigation system. Moreover, the users told us that this system provided them with good experience. In a way, this painting navigation system can be seen as "angel's eyes" of visually impaired painters. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1. © 2019 IEEE.

关键词： Navigation systems

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Sensitivity-Aware Bit Allocation for Intermediate Deep Feature Compression

Sensitivity-Aware Bit Allocation for Intermediate Deep Featu...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuzhang Hu Sifeng Xia Wenhan Yang Jiaying Liu Peking University

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In this paper, we focus on compressing and transmitting deep intermediate features to support the prosperous applications at the cloud side efficiently, and propose a sensitivity-aware bit allocation algorithm for the deep intermediate feature compression. Considering that different channels' contributions to the final inference result of the deep learning model might differ a lot, we design a channel-wise bit allocation mechanism to maintain the accuracy while trying to reduce the bit-rate cost. The algorithm consists of two passes. In the first pass, only one channel is exposed to compression degradation while other channels are kept as the original ones in order to test this channel's sensitivity to the compression degradation. This process will be repeated until all channels' sensitivity is obtained. Then, in the second pass, bits allocated to each channel will be automatically decided according to the sensitivity obtained in the first pass to make sure that the channel with higher sensitivity can be allocated with more bits to maintain accuracy as much as possible. With the well-designed algorithm, our method surpasses state-of-the-art compression tools with on average 6.4% BD-rate saving.

关键词： Sensitivity image coding Bit rate Loss measurement image reconstruction Video codecs Task analysis

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Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Fully Neural Network Mode Based Intra Prediction of Variable...

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IEEE visual communications and image processing (VCIP)

作者： Heming Sun Lu Yu Jiro Katto Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan JST PRESTO Kawaguchi Saitama Japan Institute of Information and Communication Engineering Zhejiang University Hangzhou China Waseda University Tokyo Japan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4×4 and 8×8, fully connected networks are used, while for large blocks 16×16 and 32×32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

关键词： Encoding Training image coding Complexity theory Proposals Convolutional codes Gain

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Improving Action Recognition with the Graph-Neural-Network-based Interaction Reasoning 34

Improving Action Recognition with the Graph-Neural-Network-b...

引用

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

作者： Luo, Wu Zhang, Chongyang Zhang, Xiaoyun Wu, Haiyan Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Railway Police College Department of Image and Network Investigation Zhengzhou450053 China

ISBN: (纸本)9781728137230

Recent human action recognition methods mainly model a two-stream or 3D convolution deep learning network, with which humans spatial-Temporal features can be exploited and utilized effectively. However, due to the ignoring of interaction exploiting, most of these methods cannot get good enough performance. In this paper, we propose a novel action recognition framework with Graph Convolutional Network (GCN) based Interaction Reasoning: Objects and discriminative scene patches are detected using an object detector and class active mapping (CAM), respectively;and then a GCN is introduced to model the interaction among the detected objects and scene patches. Evaluation of two widely used video action benchmarks shows that the proposed work can achieve comparable performance: The accuracy up to 43.6% at EPIC Kitchen, and 47.0% at VLOG benchmark without using optical flow, respectively. © 2019 IEEE.

关键词： Graph neural networks

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Comer-line-prediction based water-Tank detection and localization 34

Comer-line-prediction based water-Tank detection and localiz...

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

作者： Chen, Haoning Zhang, Chongyang Luo, Yan Zhao, Bingkun Bao, Jiahao Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9781728137230

Water tanks on the roof of buildings require regular labor-costing inspection, and object detection can be used to automate the task. Current detection frameworks have several drawbacks when they are applied: (1) The output horizontal rectangular boxes cannot provide arbitrary quadrilateral detection representations;(2) False positive results may easily appear when key-point based models are used. In this paper, we propose a novel detection framework: Corner-Line-Prediction, which generates tight quadrilateral detection results of the tank blocks. Our model is built on key point detection network to detect corner points precisely. And an original line predictor is integrated to recognize unique tank edges, such that numerous false positive detections can be suppressed. Experimental results show that our Corner-Line-Prediction (CLP) framework outperforms state-of-The-Art detection algorithms in average-precision (AP) and produces better localization results, compared with mainstream general detection models. © 2019 IEEE.

关键词： Water tanks

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A Multi-Model Fusion Framework for NIR-to-RGB Translation

A Multi-Model Fusion Framework for NIR-to-RGB Translation

引用

IEEE visual communications and image processing (VCIP)

作者： Longbin Yan Xiuheng Wang Min Zhao Shumin Liu Jie Chen CIAIC School of Marine Science and Technology Northwestern Polytechnical University China Shanghai Shengyao Intelligent Technology Co. Ltd China Blueye Intelligence Zhenjiang China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Near-infrared (NIR) images provide spectral information beyond the visible light spectrum and thus are useful in many applications. However, single-channel NIR images contain less information per pixel than RGB images and lack visibility for human perception. Transforming NIR images to RGB images is necessary for performing further analysis and computer vision tasks. In this work, we propose a novel NIR-to-RGB translation method. It contains two sub-networks and a fusion operator. Specifically, a U-net based neural network is used to learn the texture information while a CycleGAN based neural network is adopted to excavate the color information. Finally, a guided filter based fusion strategy is applied to fuse the outputs of these two neural networks. Experiment results show that our proposed method achieves superior NIR-to-RGB translation performance.

关键词： image color analysis image edge detection Generative adversarial networks Gallium nitride Task analysis Convolution conferences

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Improving Small-Scale Pedestrian Detection Using Informed Context 34

Improving Small-Scale Pedestrian Detection Using Informed Co...

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

作者： Liu, Zexiang Zhang, Chongyang Luo, Yan Chen, Kai Zhou, Qiping Lai, Yunyu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China State Grid Jiangxi Power Co.Ltd Maintenance Branch Nanchang330096 China

ISBN: (纸本)9781728137230

Finding small objects is fundamentally challenging because there is little signal on the object to exploit. For the small-scale pedestrian detection, one must use image evidence beyond the pedestrian extent, which is often formulated as context. Unlike existing object detection methods that use adjacent regions or whole image as the context simply, we focus on more informed contexts exploiting and utilizing to improve small-scale pedestrian detection: firstly, one relationship network is developed to utilize the correlation among pedestrian instances in one image;secondly, two spatial regions, overhead area and feet bottom area, are taken as spatial context to exploit the relevance between pedestrian and scenes;at last, GRU [7] (Gated Recurrent Units) modules are introduced to take encoded contexts as input to guide the feature selection and fusion of each proposal. Instead of getting all of the outputs at once, we also iterate twice to refine the detection incrementally. Comprehensive experiments on Caltech Pedestrian [8] and SJTU-SPID [9] datasets, indicate that, with more informed context, the detection performance can be improved significantly, especially for the small-scale pedestrians. © 2019 IEEE.

关键词： image enhancement

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Comparison of Facial Emotion Recognition Based on image visual Features and EEG Features 4th

Comparison of Facial Emotion Recognition Based on Image Visu...

引用

4th International conference on Cognitive Systems and Information processing (ICCSIP)

作者： Long, Yanfang Wanzeng, Kong B. Ling, Wenfen Yang, Can Zhu, Jieyong Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9789811379864;9789811379857

Automatic facial emotion recognition plays an important role in human-computer interaction. Although humans can recognize emotions with little or no effort, reliable emotion recognition by machines is always a challenge. To explore the doubt that whether machines can discriminate better than humans or not, we proposed two different ideas about facial emotion recognition. One is based on image visual features, the other is based on EEG signals which were recorded when the subject is watching facial emotion pictures. Correspondingly, the Deep Convolutional Neural Network (DCNN) model is adopted to enable the machine to learn visual features from facial emotion pictures automatically. The Gated Recurrent Unit (GRU) model is used to extract specific emotional EEG features from EEG signals. These two methods were verified on the Chinese Facial Affective Picture System (CFAPS) and our developed Emotion EEG data (EMOT), and the recognition performance based on EEG features was found to be significantly better than the image visual.

关键词： Deep learning EEG Facial emotion recognition

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Motion Estimation for Spike Camera Data Sequence via Spike Interval Analysis

Motion Estimation for Spike Camera Data Sequence via Spike I...

引用

IEEE visual communications and image processing (VCIP)

作者： Jing Zhao Ruiqin Xiong Rui Zhao Jin Wang Siwei Ma Tiejun Huang Institute of Digital Media Peking University Beijing China Beijing University of Technology Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

With the development of emerging computer vision applications, there is an increasing demand for capturing the scenes with high-speed motion. Recently, a novel retina-inspired spike camera has shown great potential for recording the dynamic scenes at high temporal resolution. Different from the conventional digital cameras that capture the visual scene by a single snapshot, the spike camera monitors the incoming light persistently, with each pixel producing a continuous stream of spikes. Recovering the motion process from the spike data sequence is an important problem to study for the spike camera, as it is the foundation of many other tasks, such as image reconstruction, object tracking and object detection. In this paper, we carefully analyze the characteristics of spike data and develop a motion estimation algorithm to recover the continuous high-speed motion process from the spike camera data sequence. Based on the assumption that the spike intervals passed by the same motion trajectories usually have the similar spike densities, we establish a data term constraint to model the temporal consistency of spike intervals. In addition, we integrate a local smoothness constraint with the proposed data term constraint to further improve the estimation accuracy. Experimental results demonstrate that our proposed algorithm can recover high-speed motion process from the captured spike data, and the recovered motion information is beneficial for i mage reconstruction.

关键词： Cameras Motion estimation Trajectory image reconstruction Data models Estimation Dynamics

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Interpretable and Effective Learning for 3D Point Cloud Registration, Classification and Segmentation

Interpretable and Effective Learning for 3D Point Cloud Regi...

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IEEE visual communications and image processing (VCIP)

作者： C.-C. Jay Kuo The University of Southern California

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

3D point cloud analysis and processing find numerous applications in computer-aided design, 3D printing, autonomous driving, etc. Most state-of-the-art point cloud processing methods are based on convolutional neural networks (CNNs). Although they outperform traditional methods in terms of accuracy, they demand heavy supervision and higher training complexity. Besides, they lack mathematical transparency. In this talk, I will present three interpretable and effective machine learning methods for 3D point cloud registration, classification and segmentation, respectively. First, an unsupervised registration method that extracts salient points for matching is presented. Second, an unambiguous way to order points sequentially in a point cloud set is developed. Then, their spatial coordinates can be treated as geometric attributes of 1 D data array. This idea facilitates the classification task. Third, for the segmentation task, we show how to leverage prior knowledge on point clouds to derive an intuitive and effective segmentation method. Extensive experiments are conducted to demonstrate the performance of the three new methods. I will also provide performance benchmarking between these interpretable methods and deep learning methods.

关键词： Three-dimensional displays Task analysis visualization Training Three-dimensional printing Speech processing Multimedia computing

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