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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Research on training image recognition based on deep Convolutional neural networks: A case study of the hand-held insulated ladder recognition model 23

Research on training image recognition based on deep Convolu...

引用

Proceedings of the 2nd International conference on Signal processing, Computer Networks and communications

作者： Chaobing Wei Jiangmen power supply Bureau Guangdong Power Grid Corporation China

ISBN: (纸本)9798400716430

In this paper, we will introduce a series of work required for the design model of the insulation ladder identification technology, including the construction of the model training environment, the preparation of data sets, the training of the model, the visual operation of the model and the recognition result of the model. The identification model of walking insulation ladder in electric power construction will be developed based on PyTorch. We will ensure the safety of distribution network operations. In this paper, based on the existing YOLOv5 based target detection model, an improved design was carried out to enhance the accuracy of its detection of small targets, and the model was lightweight. Finally, the improved model was deployed to the edge device for hardware experiment verification.

关键词： Deep convolutional neural network image recognition Recognition model Walking insulated ladder

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Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for image Denoising

Deep Convolutional Neural Network Based on Multi-Scale Featu...

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

作者： Zhang, Jing Sang, Liu Wan, Zekang Wang, Yuchen Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781728180687

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.

关键词： image denoising multi-scale convolutional network diamond

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Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265

Neural Frank-Wolfe Policy Optimization for Region-of-Interes...

引用

IEEE visual communications and image processing (VCIP)

作者： Yung-Han Ho Chia-Hao Kao Wen-Hsiao Peng Ping-Chun Hsieh National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9781665475938

This paper presents a reinforcement learning (RL) framework that utilizes Frank-Wolfe policy optimization to solve Coding- Tree-Unit (CTU) bit allocation for Region-of-Interest (ROI) intra-frame coding. Most previous RL-based methods employ the single-critic design, where the rewards for distortion minimization and rate regularization are weighted by an empirically chosen hyper-parameter. Recently, the dual-critic design is proposed to update the actor by alternating the rate and distortion critics. However, its convergence is not guaranteed. To address these issues, we introduce Neural Frank-Wolfe Policy Op-timization (NFWPO) in formulating the CTU-level bit allocation as an action-constrained RL problem. In this new framework, we exploit a rate critic to predict a feasible set of actions. With this feasible set, a distortion critic is invoked to update the actor to maximize the ROI-weighted image quality subject to a rate constraint. Experimental results produced with x265 confirm the superiority of the proposed method to the other baselines.

关键词： image quality image coding visual communication Bit rate Rate distortion theory Reinforcement learning Distortion

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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Electrical, Computer, communications and Mechatronics Engineering (ICECCME), International conference on

作者： Marjana Tahmid Samiul Alam Kalim Akram Kazi Muhammad Asif Ashrafi Dept. of CSE East Delta University Chittagong Bangladesh Dept. of EEE East Delta University Chittagong Bangladesh Dept. of CSE Ulm University Ulm Germany

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and Computer Vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.

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Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Quality comparison of 360° 8K images compressed by conventi...

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International conference on Radioelektronika

作者： Jan Kufa Adam Budac Dept. of Radio Electronics Brno Faculty of Electrical Engineering and Communications Czech Republic

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional $(360 ^{\circ}$ field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.

关键词： Deep learning visualization image coding Codecs image resolution Bit rate Transform coding

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Generalized Gaussian Distribution Based Distortion Model for the H.266/VVC Video Coder

Generalized Gaussian Distribution Based Distortion Model for...

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IEEE visual communications and image processing (VCIP)

作者： Hongkui Wang Junhui Liang Li Yu Yiying Gu Haibing Yin School of Communication Engineering Hangzhou Dianzi University Hangzhou China Lishui Institute of Hangzhou Dianzi University Lishui China College of Electronic Information and Communication-s Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665475938

In versatile video coding (VVC), superior coding performance is achieved with incorporating many advanced coding tools. In this paper, a frame-level coding distortion model is proposed for VVC video coders for the first time. In comparison with the transform coefficient distribution (TCD) of High Effective Video Coding (HEVC), the TCD of VVC has a sharper peak. According to this observation, the TCDs of I, B and P frames are modeled by the probability density function (PDF) of generalized Gaussian distribution (GGD) with three fixed shape parameters. The GGD-based distortion model is then derived with a sliding window-based strategy, i.e., the frame-level coding distortion is formulated as the function of the distribution parameter of frame-level TCD and the quantization step. The experimental results show that the proposed model achieves accurate results of distortion estimation for VVC coders.

关键词： image coding Quantization (signal) Laplace equations Shape visual communication Estimation Transforms

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Reduced Reference Quality Assessment for Point Cloud Compression

Reduced Reference Quality Assessment for Point Cloud Compres...

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IEEE visual communications and image processing (VCIP)

作者： Yipeng Liu Qi Yang Yiling Xu Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

In this paper, we propose a reduced reference (RR) point cloud quality assessment (PCQA) model named R-PCQA to quantify the distortions introduced by the lossy compression. Specifically, we use the attribute and geometry quantization steps of different compression methods (i.e., V-PCC, G-PCC and AVS) to infer the point cloud quality, assuming that the point clouds have no other distortions before compression. First, we analyze the compression distortion of point clouds under separate attribute compression and geometry compression to avoid their mutual masking, for which we consider 5 point clouds as references to generate a compression dataset (PCCQA) containing independent attribute compression and geometry compression samples. Then, we develop the proposed R-PCQA via fitting the relationship between the quantization steps and the perceptual quality. We evaluate the performance of R-PCQA on both the established dataset and another independent dataset. The results demonstrate that the proposed R-PCQA can exhibit reliable performance and high generalization ability.

关键词： Point cloud compression Geometry image coding Quantization (signal) visual communication Fitting Distortion

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BAM: A Bidirectional Attention Module for Masked Face Recognition

BAM: A Bidirectional Attention Module for Masked Face Recogn...

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IEEE visual communications and image processing (VCIP)

作者： M. Saad Shakeel School of Automation Guangdong University of Petrochemical Technology Maoming China

ISBN: (纸本)9781665475938

Masked Face Recognition (MFR) is a recent addition to the directory of existing challenges in facial biometrics. Due to the limited exposure of facial regions due to mask-occlusion, it is essential to exploit the available non-occluded regions as much as possible for identity feature learning. Aiming to address this issue, we propose a dual-branch bidirectional attention module (BAM), which consists of a spatial attention block (SAB) and a channel attention block (CAB) in each branch. In the first stage, the SAB performs bidirectional interactions between the original feature map and its augmented version to highlight informative spatial locations for feature learning. The learned bidirectional spatial attention maps are then passed through a channel attention block (CAB) to assign high weights to only informative feature channels. Finally, the channel-wise calibrated feature responses are fused to generate a final attention-aware feature representation for MFR. Extensive experiments indicate that our proposed BAM is superior to various state-of-the-art methods in terms of recognizing mask-occluded face images under complex facial variations.

关键词： Representation learning image recognition visual communication Face recognition Computational modeling Performance gain Benchmark testing

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FPX-NVC: An FPGA-Accelerated P-frame Based Neural Video Coding System

FPX-NVC: An FPGA-Accelerated P-frame Based Neural Video Codi...

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IEEE visual communications and image processing (VCIP)

作者： Chuanmin Jia Xinyu Hang Wenbo Liu Shanshe Wang Siwei Ma Peking University Beijing China Beijing Boya Realscene Technologies Co. Ltd. Beijing China

ISBN: (纸本)9781665475938

Hardware-oriented neural video coding (NVC) has been an emerging research topic recently. In this work, we present a P-frame based NVC system by leveraging the parallel neural inference capability of resource-constrained embed platforms, such as field programmable gate array (FPGA). The proposed system, namely FPX-NVC, consists of multiple resolution video capture, reconfigurable and integerized neural video codec and display. Our neural codec enjoys the learned unidirectional inter-predictive coding by jointly optimizing the implicit motion representation (IMR), feature domain motion compensation, and the corresponding prediction error compression, realizing end-to-end on-board encoding and decoding. Our system supports end-to-end video data operation flow from capturing to neural coding and display. To our knowledge, the FPX-NVC is the first inter-predictive coding (P-frame) based hardware NVC system.

关键词： Video coding image coding visual communication Market research Hardware Motion compensation Systems support

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DE-CrossDet: Divisible and Extensible Crossline Representation for Object Detection

DE-CrossDet: Divisible and Extensible Crossline Representati...

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IEEE visual communications and image processing (VCIP)

作者： Hefei Mei Hongliang Li Heqian Qiu Jianhua Cui Longrong Yang University of Electronic Science and Technology of China

ISBN: (纸本)9781665475938

Object detection aims to localize and classify objects. Suitable object representation plays an important role in accurate detection. Because a complete crossline inevitably passes through the noise of backgrounds or other objects, object features directly extracted by the whole crossline are often confused. In this paper, we present a new feature extraction method, DE-Crossline, which can enhance the original crossline representation to capture more accurate object information. Specifically, we divide the crossline into several segments, each of which extracts the maximum activation key point respectively to reduce the impact of noise mentioned above. Furthermore, considering various shapes and sizes of objects, we design a Deformable Width Extension Module to learn a suitable width of each crossline, so as to capture richer object information. Extensive experiments prove the effectiveness of our proposed method. The total performance of our proposed detector can reach 49.0% AP, using ResNet-101 as backbone on the MS-COCO dataset.

关键词： image segmentation Shape visual communication Object detection Detectors Feature extraction Data mining

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