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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE visual communications and image processing (VCIP)

作者： Xun Zhu Mengqiu Xu Ming Wu Chuang Zhang Bin Zhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18 %, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69 %, respectively.

关键词： image recognition Satellites Costs Annotations visual communication Semantic segmentation Supervised learning

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Deep Reference Frame Interpolation based Inter Prediction Enhancement for Versatile Video Coding

Deep Reference Frame Interpolation based Inter Prediction En...

引用

IEEE visual communications and image processing (VCIP)

作者： Jianghao Jia Zizheng Liu Xiaozhong Xu Shan Liu Zhenzhong Chen School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781665475938

In video coding, bi-directional inter prediction aims to remove temporal redundancy via two-sided previously coded frames as reference. High-quality reference frames are essential to reduce the prediction residuals and improve coding efficiency performance. In this paper, we propose a deep learning-based reference frame interpolation method to enhance bi-prediction by introducing a synthetic frame to reference picture lists. Specifically, reconstructed frames are fed into a well-designed interpolation and filtering network to synthesize a picture which can be regarded as an additional reference of to-be-coded frame. Then, the picture is inserted at the appropriate place in reference picture lists to provide a more reliable reference for subsequent motion estimation and motion compensation. Experimental results show that the proposed method achieves 2.03%/6.96%/6.40% coding efficiency improvements for Y/U/V components under random access configuration, when compared with the VVC reference software VTM-15.0.

关键词： Video coding Interpolation image coding Filtering visual communication Redundancy Bidirectional control

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A Fast Motion Estimation Method With Hamming Distance for LiDAR Point Cloud Compression

A Fast Motion Estimation Method With Hamming Distance for Li...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuhao An Yiting Shao Ge Li Wei Gao Shan Liu School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Media Lab Tencent America Palo Alto CA USA

ISBN: (纸本)9781665475938

With more three-dimensional space information, Light detection and ranging (LiDAR) point clouds, which are promising to play more roles in the future, have an urgent need to be efficiently compressed. There are lots of compression methods based on spatial correlations, whereas few studies consider exploiting temporal correlations. In this paper, we propose a different perspective for the motion estimation. In most previous works, geometric distance between matching points was used as the criterion, which has an expensive computational cost and is not accurate. We first propose the Hamming distance between the octree's nodes, instead of the geometric distance between per point which is a more direct criterion. We have implemented our method in the MPEG (Moving Picture Expert Group) Geometry-based PCC (Point Cloud Compression) inter-exploration (G-PCC Inter-EM). Experimental results show our method can provide the average 3.5 % bitrate savings and 92.5 % encoding speed increase in lossless geometric coding, compared to the G-PCC Inter-EM.

关键词： Point cloud compression MPEG standards Geometry image coding Laser radar Correlation visual communication

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Deep Dilated Convolutional Network for Single image Dehazing 7th

Deep Dilated Convolutional Network for Single Image Dehazing

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Deivalakshmi, S. Sandana, J. Sudaroli Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031314162

The visual quality of the images gets decreased due to bad weather conditions. The image captured under hazy weather conditions have serious attenuation in terms of color and saturation. In addition, these hazy images have very low contrast and the visual quality will be drastically poor. Moreover, object detection in hazy environment is too challenging. So, single image dehazing is a demanding, challenging and ill-posed problem. In this paper, we propose a 9-layer convolutional neural network with deep dilated filters of different dilation rates to achieve an end-to-end mapping from haze image to haze free image. Exponential expansion of receptive field is possible with the dilated filters without increasing the model complexity. Furthermore, the dilated convolutional layers help for efficient model compactness. We did experiments on synthetic dataset and on naturally obtained hazy images. The results show that our network achieves outstanding performance over the existing algorithms in terms of PSNR, SSIM and visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Multimodal Crowd Counting with Mutual Attention Transformers

Multimodal Crowd Counting with Mutual Attention Transformers

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Wu, Zhengtao Liu, Lingbo Zhang, Yang Mao, Mingzhi Lin, Liang Li, Guanbin School of Computer Science and Engineering Sun Yat-sen University China Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Crowd counting is a fundamental yet challenging task that aims to automatically estimate the number of people in crowded scenes. Nowadays, with the rapid development of thermal and depth sensors, thermal images and depth maps become more accessible, which are proven to be beneficial information in boosting the performance of crowd counting. Consequently, we propose a Mutual Attention Transformer (MAT) module to fully leverage the complementary information of different modalities. Specifically, our MAT employs a cross-modal mutual attention mechanism to utilize the features of one modality to enhance the features of the other. Moreover, to improve performance by learning better visual representation and further exploiting modality-wise comple-mentarity, we design a self-supervised pre-training method based on cross-modal image reconstruction. Extensive experiments on two standard benchmarks (i.e., RGBT-CC and ShanghaiTechRGBD) show that the proposed method is effective and universal for multimodal crowd counting, outper-forming previous state-of-the-art methods. © 2022 IEEE.

关键词： image reconstruction

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Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Stereoscopic image reflection removal based on Wasserstein G...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Wang, Xiuyuan Pan, Yikun Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781728180687

Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the-art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.

关键词： Reflection removal GAN stereoscopic images

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Global Homography Motion Compensation for Versatile Video Coding

Global Homography Motion Compensation for Versatile Video Co...

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IEEE visual communications and image processing (VCIP)

作者： Yao Li Zhuoyuan Li Li Li Dong Liu Houqiang Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781665475938

In Versatile Video Coding (VVC), local affine motion compensation (LAMC) is adopted to handle complex motions, such as rotation and zooming. However, it is inefficient to use LAMC to handle the global motion due to the following two reasons. First, the use of LAMC may lead to some extra bit cost on the affine motion model parameters. Second, the precision of LAMC is restricted by the MV precision of the control points. Therefore, in this paper, we propose a global homography motion compensation (GHMC) framework to better characterize the global motion. For each coding block, an extra mode is added to perform motion compensation based on an 8-parameter global homography motion model. In addition, an extrapolation scheme is designed to derive the parameters from reference frames to save the bit cost for signaling them. The proposed framework is implemented into the VVC reference software VTM-6.0. Experimental results show that, on average, 0.69% and 0.66% BD-rate reduction is achieved under Low Delay P and Low Delay B configurations, respectively, for sequences with rich complex global motions.

关键词： Video coding Extrapolation Adaptation models Costs image coding visual communication Motion compensation

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Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared image Colorization

Learning From Paired and Unpaired Data: Alternately Trained ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Yang, Zaifeng Chen, Zhenghua ASTAR Inst High Performance Comp Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781728180687

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 IEEE International conference on visual communications and image processing (VCIP). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.

关键词： VCIP challenge 2020 NIR image colorization CycleGAN UNet paired/unpaired images data augmentation

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Robust Dynamic Background Modeling for Foreground Estimation

Robust Dynamic Background Modeling for Foreground Estimation

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IEEE visual communications and image processing (VCIP)

作者： Qian Ning Fangfang Wu Weisheng Dong Jinjian Wu Guangming Shi Xin Li Xidian University West Virginia University

ISBN: (纸本)9781665475938

Separating the background and foreground components from video frames is important to many tasks in computer vision and multimedia. As of today, robust principal component analysis (RPCA) has shown highly promising performance with the assumption that the background is low-rank and the foreground is sparse. However, existing RPCA-based methods have overlooked the uncertainty that some parts of the background (e.g., moving leaves in a dynamic background) or even the whole background (e.g., camera jittering) can be moving, which violates the low-rank assumption. To address this issue, we propose a novel enhanced RPCA framework (called ERPCA) by robustly modeling the dynamic background. Different from traditional RPCA framework, the background is decomposed into a low-rank component and a sparse component in the proposed ERPCA framework. Specifically, the sparse parts including foreground and dynamic parts of the background are modeled by Gaussian scale mixture (GSM) model. Moreover, those sparse components are further constrained by temporal consistency using nonzeromeans Gaussian models; the correspondences between sparse pixels in adjacent frames are explored by optical flow. Experimental results on 40 real videos demonstrate the superiority of our proposed method, with better average results than current state-of-the-art foreground estimation methods.

关键词： GSM Uncertainty Correlation visual communication image processing Estimation Streaming media

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Indoor Smart Walker Based on Artificial Vision for the Autonomous Movement of Seniors with Mobility Problems and Reduced Vision 2nd

Indoor Smart Walker Based on Artificial Vision for the Auton...

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2nd International conference on Smart Technologies, Systems and Applications (SmartTech-IC)

作者： Guamushig-Laica, Jose Jerez-Gavilanez, Miguel Mendoza-Chipantasi, Dario Marino-Salguero, Jessica Univ Fuerzas Armadas ESPE Quijano & Ordonez Latacunga 050102 Ecuador Tech Univ Darmstadt Dolivostr 15 D-64293 Darmstadt Germany

ISBN: (纸本)9783030991708;9783030991692

The usual solution to mobility problems for the elderly lies in the use of conventional canes and walkers. However, when the older adult also has vision problems, these devices are not sufficient to provide safe walking, and the senior suffers from constant falls. This article proposes a smart walker based on artificial vision for indoor use in nursing homes that safely enables autonomous mobility for visually impaired seniors. The walker has been equipped with activation switches on the handlebars, ultrasonic sensors, a Kinect visual sensor, traction motors, and a controller. The control algorithm is based on reactive navigation, odometry and artificial vision and is designed to avoid obstacles and detect dangerous situations such as stairs. The control unit is a Raspberry Pi running Raspbian and relies on message passing between processes and visualization. The prototype of the smart walker is implemented in the nursing home "Luis Maldonado Tamayo" (Pujili-Ecuador), where tests are being carried out with elderly persons who still have motor skills and some partial vision impairment. The walker can effectively detect obstacles in a distance range from 50 - 86 cm. Almost 90% of small, 96% medium, and 98% dynamics obstacles are avoided. The demonstration video is available at https://***/MJzvzIily4k.

关键词： Intelligent walker Senior - mobility Machine vision image processing Mobile robotics

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