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A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

A Hybrid Model for Natural Face De-Identiation with Adjustab...

引用

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

作者： Wen, Yunqian Liu, Bo Xie, Rong Zhu, Yunhui Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728180687

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, b ecomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing deidentified f ace i mages a ccording t o a s ingle i nput. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing deidentified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.

关键词： privacy protection multi-level features fusion GAN-based face generation randomness

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An Efficient image Retrieval Model with Convolutional Neural Network based Text/image Identification for Copyright Violation Detection

An Efficient Image Retrieval Model with Convolutional Neural...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Harun Ö zden Bü lent Tavlı M. Fatih Demirci &#x0130 leti&#x015F im Teknolojileri Direkt&#x00F6 rl&#x00FC &#x011F &#x00FC Anadolu Ajans&#x0131 Elektrik-Elektronik M&#x00FC hendisli&#x011F i TOBB Ekonomi ve Teknoloji &#x00DC niversitesi Bilgisayar M&#x00FC i B. Ankara Y&#x0131 ld&#x0131 r&#x0131 m Beyaz&#x0131 t &#x00DC

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database.

关键词： visualization Databases image retrieval Organizations Signal processing Convolutional neural networks

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Use of Depth Completion and SLAM Algorithm to Build Dense Maps of Large Scenes 6

Use of Depth Completion and SLAM Algorithm to Build Dense Ma...

引用

6th International conference on Signal and image processing, ICSIP 2021

作者： Jia, Yongxing Li, Mingcan Xu, Fenghui Yu, Jing Army Engineering University of PLA College of Communications Engineering Nanjing China

ISBN: (纸本)9780738133737

Limited by the range of sensor, constructing a dense outdoor map of large scene with a depth camera is difficult. Since the state-of-the-art performance on data fusion for pixel-level distance estimation, this paper proposes a mapping system which employs depth image generated by an unsupervised Lidar-Stereo network to achieve accurate reconstruction. Firstly, in the ORBSLAM2 stereo mode, the system estimates the pose information to finish camera localization. and in another thread LidarStereoNet is used to generate a depth image within visual angle to describe the pixel-level distance. Then, for correcting the completed depth image to truth ground, a nonlinear transformation between the generation and the rendering is performed to restore the accurate distance relationship. Finally, the RGB image, pose information and depth rendering image are entered into the dense mapping module to get a large-scale point cloud model. After running on KITTI dataset, the experiment results show that the system can construct a pretty comprehensive scene model after only one trip, and using the numerical truncation and filtering methods to extract usable areas can make the mapping results more accurate. © 2021 IEEE.

关键词： Mapping

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Perceived image Reconstruction from Human Brain Activity via Time-Series Information Guided Generative Adversarial Networks 27th

Perceived Image Reconstruction from Human Brain Activity via...

引用

27th International conference on Neural Information processing

作者： Huang, Shuo Sun, Liang Yousefnezhad, Muhammad Wang, Meiling Zhang, Daoqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9783030638221;9783030638238

Understanding how human brain works has attracted increasing attentions in both fields of neuroscience and machine learning. Previous studies have used autoencoder and generative adversarial networks (GAN) to improve the quality of perceived image reconstruction from functional Magnetic Resonance Imaging (fMRI) data. However, these methods mainly focus on acquiring relevant features between stimuli images and fMRI while ignoring the time-series information of fMRI, thus leading to sub-optimal performance. To address this issue, in this paper, we develop a time-series information guided GAN method for reconstructing visual stimuli from human brain activities. In addition, to better measure the modal difference, we leverage a pairwise ranking loss to rank the stimuli images and fMRI to ensure strongly associated pairs at the top and weakly related ones at the bottom. Experimental results on real-world datasets suggest that the proposed method achieves better performance in comparison with several state-of-the-art image reconstruction approaches.

关键词： Perceived image reconstruction functional Magnetic Resonance Imaging (fMRI) Long-Short Term Memory (LSTM) Generative adversarial networks (GAN)

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Orthogonal Coded Multi-view Structured Light for Inter-view Interference Elimination

Orthogonal Coded Multi-view Structured Light for Inter-view ...

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

作者： Sun, Zaichao Qian, Guochao Peng, Zhaoyu Dai, Weiju Sun, Dongjun Zhang, Gongyuan Zhang, Nongtao Xu, Jun Wang, Ren Li, Chunlin Yunnan Elect Power Grid Co Ltd Elect Power Res Inst Kunming Yunnan Peoples R China

ISBN: (纸本)9781728180687

Rapid 3D reconstruction of dynamic scenes is very useful in 3D object structure analysis, accident avoidance for UAV, and other visual applications. Against dynamic scenes, coded structured light methods have been proposed to obtain the depth information of an object in 3D world, and most of them are based on spatial codification. A brutal truth is that two or more cameras and projectors from different viewpoints are needed to measure the dynamic scene simultaneously for rapid 3D reconstruction. However, when two traditional patterns, especially the binaries, are mutually overlapped, interference between them arises to a new challenge to 3D reconstruction. Traditional patterns can hardly be separated from each other, which surely influence the quality of the 3D reconstruction. To eliminate the interference problem, we propose a scheme of orthogonal coded multi-view structured light systems, which can obtain accurate of depth maps for a scene. Besides, we also test the stability of the orthogonal patterns by establishing three different scenes and making a comparisons to traditional patterns. New state-of-the-art results can be obtained by our scheme in the experiments.

关键词： image reconstruction

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A Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral images

A Robust Multilinear Mixing Model with l2,1 norm for Unmixin...

引用

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

作者： Li, Minglei Zhu, Fei Guo, Alan J. X. Tianjin Univ Ctr Appl Math Tianjin Peoples R China

ISBN: (纸本)9781728180687

The unmixing of hyperspectral data is a hot topic in the field of r emote s ensing. H owever, in p resence o f various types of noise, especially the noisy channels, the performance of unmixing approaches is seriously deteriorated. To enhance the robustness of the unmixing method is a subject worth studying. This paper presents a robust unmixing method based on the recently- proposed multilinear mixing model, where the l(2,1) norm is adopted in the loss function to suppress the influence of noise. The sparseness of abundance is also considered to improve the parameter estimation. The resulting optimization problem is solved by the alternating direction multiplier method (ADMM). Experiments on both synthetic and real images demonstrate the performance of the proposed unmixing strategy.

关键词： hyperspectral image nonlinear unmixing alternating direction method of multipliers robust l(2,1) norm

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Deep Blind Video Quality Assessment for User Generated Videos

Deep Blind Video Quality Assessment for User Generated Video...

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

作者： Tang, Jiapeng Dong, Yu Xie, Rong Gu, Xiao Song, Li Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

As short video industry grows up, quality assessment of user generated videos has become a hot issue. Existing no reference video quality assessment methods are not suitable for this type of application scenario since they are aimed at synthetic videos. In this paper, we propose a novel deep blind quality assessment model for user generated videos according to content variety and temporal memory effect. Content-aware features of frames are extracted through deep neural network, and a patch-based method is adopted to obtain frame quality score. Moreover, we propose a temporal memory-based pooling model considering temporal memory effect to predict video quality. Experimental results conducted on KoNViD-1k and LIVE-VQC databases demonstrate that the performance of our proposed method outperforms other state-of-the-art ones, and the comparative analysis proves the efficiency of o ur t emporal p ooling model.

关键词： video quality assessment user generated videos no reference

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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 International conference on visual communications and image processing (VCIP)

作者： Ai, Jin Hu, Menghan Li, Guodong Zhai, Guangtao Zhang, Jian Li, Qingli Sun, Wendell Q. Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Articial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Due to the loss of vision, the appreciation of the real-world 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 degrees x42.5 degrees x77 degrees;depth field of view is 91.2 degrees x65.5 degrees x 100.6 degrees;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 system vision disorder

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Mining Larger Class Activation Map with Common Attribute Labels

Mining Larger Class Activation Map with Common Attribute Lab...

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

作者： Zhang, Runtong Meng, Fanman Li, Hongliang Wu, Qingbo Ngan, King N. Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728180687

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.

关键词： image classification class activation map common attribute label

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Machine Vision Aided Adaptive Beamforming Decision for IRS-Assisted Wireless Networks

Machine Vision Aided Adaptive Beamforming Decision for IRS-A...

引用

European conference on Networks and communications (EuCNC)

作者： M. Munawar M. Guenach I. Moerman IMEC Leuven IDLab Ghent University

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

This study leverages machine vision to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless networks. Instead of depending on traditional schemes such as alternating optimization or semidefinite relaxation to maximize signal strength in an IRS-assisted network, which are computationally expensive and impractical, we use visual data to make low-complexity beamforming decisions for users. Our approach involves employing a ceiling camera with a fish-eye view, covering a wide communication area. The user within the network is initially detected using the YOLOv2 object detection method. Subsequently, we propose closed-form analytical expressions to determine the distances between the access point (AP), user, and IRS. Accounting for the non-uniform nature of the fish-eye image, we introduce a novel method to determine non-uniform pixel weightages using trigonometric techniques. Based on the calculated distances, we make beamforming decisions depending on the user’s proximity to the AP or IRS. The proposed method significantly reduces computational complexity, making it nearly independent of the number of reflecting elements at the IRS. Simulation results indicate that the proposed approach exhibits extremely lower computational costs compared not only to conventional schemes such as alternating optimization and semidefinite relaxation-based convex solvers but also to low-complexity heuristic schemes.

关键词： Degradation visualization Array signal processing Wireless networks Machine vision Simulation Object detection

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