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Programming to Identify Exfoliated 2D Nanosheets Using Machine Learning 1

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10th International conference on communications, Signal processing, and Systems

作者： Li, Zhibo He, Kexin Li, Xiao Zhang, Yinghui Wang, Cheng Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Dept Commun Engn Tianjin 300387 Peoples R China Tianjin Normal Univ Coll Artificial Intelligence Dept Elect & Commun Engn Tianjin 300387 Peoples R China

ISBN: (数字)9789811903908

ISBN: (纸本)9789811903908;9789811903892

Programming through machine learning methods has been gradually replacing many repetitive and tedious works. In the mission of identifying of mechanically exfoliated 2Dmaterials usingmicroscope, which is a laboriouswork inappropriate to be manually executed, the machine learning methods perform attractive potentials in the rapid and accurate targeting of available productions. Based on the algorithm of image segmentation and target positioning, this paper discusses the feasibility of program-controlled searching of 2D nanosheets from miscellaneous cracks. By intruding visual GUI displaying, the program-enabled automatic recognition may emancipate humans from the hard work.

关键词： Machine learning image segmentation visual GUI 2D material Automatic targeting

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ABLATION-CAM++: GROUPED RECURSIVE visual EXPLANATIONS FOR DEEP CONVOLUTIONAL NETWORKS 29

ABLATION-CAM++: GROUPED RECURSIVE VISUAL EXPLANATIONS FOR DE...

引用

29th IEEE International conference on image processing, ICIP 2022

作者： Salama, Ahmed Adly, Noha Torki, Marwan Applied Innovation Center Ministry of Communications and Information Technology Egypt Computer and Systems Engineering Department Alexandria University Egypt

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recently, providing explainable deep learning models has sparked a lot of attention. In this paper, we take a further step in this direction. We introduce a time-efficient method, called Ablation-CAM++, which can generate smooth visual explanations of CNN model predictions. Our approach uses the concept of studying the ablation analysis to determine the importance of activation maps w.r.t. the target class, similar to Ablation-CAM. However, instead of focusing on the individual importance of each activation map, we group activation maps using a clustering technique. Then, we construct a binary tree for each group by recursively splitting these groups, studying the ablation of each subgroup, and applying tree pruning. We perform qualitative and quantitative evaluations of our visual explanations against Ablation-CAM and Grad-CAM. Our approach can provide visual explanations in less than half of the time of Ablation-CAM. Using average drop and average increase evaluation metrics on 2000 images of the imageNet validation set, we provide a comparison of the effect of applying different clustering techniques in our method. © 2022 IEEE.

关键词： Computer vision

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iWave3D: End-to-end Brain image Compression with Trainable 3-D Wavelet Transform

iWave3D: End-to-end Brain Image Compression with Trainable 3...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728185514

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.

关键词： Brain image compression wavelet transform lifting scheme 3-D convolutional neural networks

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Inter Submesh Border Information Coding with Skip Mode in V-DMC

Inter Submesh Border Information Coding with Skip Mode in V-...

引用

IEEE visual communications and image processing (VCIP)

作者： Koki Kishimoto Kei Kawamura Haruhisa Kato KDDI Research Inc. Saitama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Standardization of Dynamic Mesh Coding (V-DMC) has been progressing in MPEG since 2022. The current reference software for V-DMC encodes dynamic meshes by segmenting them into regions (submeshes) and correcting holes occurring at submesh boundaries based on submesh boundary information. However, the encoding performance of submesh boundary information is low in V-DMC because it does not utilize the temporal correlation of submesh boundary information. To address this issue, we propose an inter-coding method for submesh boundary information using reference frame submesh boundary information. Experimental results show that our proposal improves coding performance compared to conventional methods.

关键词： image coding Correlation visual communication Transform coding Standardization Software Proposals

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A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game images

A Multi-dimensional Aesthetic Quality Assessment Model for M...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Wang, Tao Sun, Wei Min, Xiongkuo Lu, Wei Zhang, Zicheng Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.

关键词： mobile game image aesthetic quality assessment multi-task learning deep learning

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A Completed Gaussian Extended Binary Pattern for Texture image Classification 2

A Completed Gaussian Extended Binary Pattern for Texture Ima...

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2nd International conference on Signal processing, Computer Networks and communications, SPCNC 2023

作者： Xu, Xiaochun Lin, Weizheng Chen, Dingrong Su, Nanling Tang, Leinuo Cai, Changxu College of Computer and Control Engineering Minjiang University Fuzhou350108 China International Digital Economy College Minjiang University Fuzhou350108 China

ISBN: (纸本)9798400716430

Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance. © 2023 ACM.

关键词： Feature extraction

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Development of a VS ultrasound diagnostic system with image evaluation functions 11

Development of a VS ultrasound diagnostic system with image ...

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11th IEEE Global conference on Consumer Electronics, GCCE 2022

作者： Zhou, Jiayi Koizumi, Norihiro Nishiyama, Yu Tsumura, Ryosuke Tsukihara, Hiroyuki Matsumoto, Naoki The University of Electro-Communications Department of Mechanical and Intelligent Systems Tokyo Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan The University of Tokyo Graduate School of Engineering Tokyo Japan Nihon University Department of Gastroenterology and Hepatology Tokyo Japan

ISBN: (纸本)9781665492324

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system. © 2022 IEEE.

关键词： visual servoing

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Lossy Coding for Spatially Adaptive Conditioning in Semantic image Communication

Lossy Coding for Spatially Adaptive Conditioning in Semantic...

引用

IEEE visual communications and image processing (VCIP)

作者： Cem Eteke Alexander Griessel Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology Information and Technology Technical University of Munich Munich Germany Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technical University of Munich Munich Germany Chair of Communication Networks Information and Technology Technical University of Munich Munich Germany

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The increasing demand for high-quality, real-time visual communication and the growing user expectations, coupled with limited network resources, necessitate novel approaches to semantic image communication. This paper presents a method to enhance semantic image communication that combines a novel lossy semantic encoding approach with spatially adaptive semantic image synthesis models. By developing a model-agnostic training augmentation strategy, our approach substantially reduces susceptibility to distortion introduced during encoding, effectively eliminating the need for lossless semantic encoding. Comprehensive evaluation across two spatially adaptive conditioning methods and three popular datasets indicates that this approach enhances semantic image communication at very low bit rate regimes.

关键词： Training Adaptation models image coding visual communication image synthesis image communication Semantics Bit rate Distortion Real-time systems

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Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Och, Hannah Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg FAU Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany Leipzig Univ Telecommun Deutsch Telekom AG Inst Commun Engn Gustav Freytag Str 43-45 D-04277 Leipzig Germany

ISBN: (纸本)9781728185514

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： distribution modelling lossless coding screen content coding color image compression soft context formation

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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