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A Survey of image Classification algorithms Based on Graph Neural Networks 2nd

A Survey of Image Classification Algorithms Based on Graph N...

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSPandDL 2020

作者： Liu, Chan Cheng, Feiyan School of Information Yunnan Normal University Kunming China

ISBN: (纸本)9789811633904

image classification is an image processing method which can distinguish different objects according to their different features reflected in the image information. A graph neural network (GNN) is a connectivity model that captures graph dependencies through messaging between nodes of a graph. After a systematic study of the graph neural network and its application in image processing, this paper presents the mainstream structure model for image classification based on the graph neural network, and synthetically compares the performance of the more popular graph neural network model by analyzing the application scenarios and solving problems of the model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zero-shot learning

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An image Region-Based image Stitching Algorithm of Smartphones 14

An Image Region-Based Image Stitching Algorithm of Smartphon...

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14th IEEE international conference on advanced Infocomm Technology (ICAIT)

作者： Huo, Xinwei Liu, Junqi Liu, Yusheng Geng, Jiahui Zhang, Lei Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China

ISBN: (数字)9781665471565

ISBN: (纸本)9781665471565

image stitching is an important branch of image processing. In order to obtain clear and complete panoramic images, various image mosaic algorithms are proposed. A new region based stitching algorithm is proposed in this paper. The system consists of a rotating table, a controller, a smartphone and a computer. The object is placed on the rotating table, controlled by the controller to rotate slowly, and the smartphone which placed directly in front of it records the video. The images obtained are extracted frame by frame from the captured video. The still image is determined by comparing the similarity between image matrices. The registration regions can be determined by comparing the number of pixels moved of adjacent photos. The new method is helpful to realize artifact free sub region stitching, and then reduce the stitching error.

关键词： image Stitching image processing Area-Based Methods Panoramic image

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ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama 17th

ResRnnNet: Learning to Reconstruct the 3D Room Layout from a...

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Zhao, Shaonan Li, Wei Univ New South Wales Sch Civil Engn & Environm Sydney NSW 2052 Australia Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789811910579;9789811910562

3D room layout reconstruction from a single RGB panoramic image has been an emerging research topic in recent years. To achieve better prediction accuracy, in this paper, we propose a new approach to predict 3D room layout from a single panoramic image. Our reconstruction flow follows a common framework which is same as LayoutNet [9] and HorizonNet [4];however, we redesign a new deep learning architecture with recurrent neural networks (RNNs) encoder-decoder as an extension for keypoints refinement and use a gradient ascent optimization algorithm to minimize the similar loss. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.

关键词： 3D room layout Deep learning Manhattan world Panorama image

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Proceedings of the 2021 1st international conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

Proceedings of the 2021 1st International Conference on Adva...

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1st IEEE international conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

ISBN: (纸本)9781728157900

The proceedings contain 199 papers. The topics discussed include: evaluating the effectiveness of community detection algorithms for influence maximization in social networks;design and fabrication of Bio-MEMS device for detection of heavy metalion present in potable water;towards prediction of academic school of students for real-time;advanced analysis of cardiac image processing using hybrid approach;a comparative performance analysis of stellar SDN controllers using emulators;an improved image encryption using 2D logistic adjusted sine chaotic map with shuffled index matrix;substituent effect on the chalcones and their DFT studies;and bidirectional DC-DC converter fed BLDC motor in electric vehicle.

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FOVEA: Foveated image Magnification for Autonomous Navigation 18

FOVEA: Foveated Image Magnification for Autonomous Navigatio...

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18th IEEE/CVF international conference on Computer Vision (ICCV)

作者： Thavamani, Chittesh Li, Mengtian Cebron, Nicolas Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9781665428125

Efficient processing of high-resolution video streams is safety-critical for many robotics applications such as autonomous driving. image downsampling is a commonly adopted technique to ensure the latency constraint is met. However, this naive approach greatly restricts an object detector's capability to identify small objects. In this paper, we propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. The magnified regions are those that are believed to have a high probability of containing an object, whose signal can come from a dataset-wide prior or frame-level prior computed from recent object predictions. The magnification is implemented by a KDE-based mapping to transform the bounding boxes into warping parameters, which are then fed into an image sampler with anti-cropping regularization. The detector is then fed with the warped image and we apply a differentiable backward mapping to get bounding box outputs in the original space. Our regional magnification allows algorithms to make better use of high-resolution input without incurring the cost of high-resolution processing. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. Additionally, building on top of the previous state-of-the-art in streaming detection, our method sets a new record for streaming AP on Argoverse-HD (from 17.8 to 23.0 on a GTX 1080 Ti GPU), suggesting that it has achieved a superior accuracy-latency tradeoff.

关键词： Mapping

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Reversible Data Hiding in Color image with Grayscale Invariance using Sorting

Reversible Data Hiding in Color Image with Grayscale Invaria...

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2021 IEEE international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2021

作者： Yuan, Junying Wu, Min-Hao Chang, Ting-Cheng Zhan, Zengrong Chen, Haishan School of Electrical and Computer Engineering Nafang College • Guangzhou Guangzhou China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou China

ISBN: (纸本)9780738145334

Most reversible data hiding (RDH) algorithms for color images directly embed secret information in the luminance channel. As a result, the grayscale version of the color image is usually distorted. Since many color image processing algorithms work on the grayscale version, their performance on marked color images may get seriously impacted. Therefore, RD H with grayscale invariance (RDH-GI) is advanced to eliminate such shortcomings and has received great attention thereafter. However, their performance is still far from satisfactory. This paper presents a novel RDH-GI method by exploiting the pixel clustering technique to segment host pixels into different complexity levels. Data embedding tends to take place in less complex regions, and thus image distortion is reduced while maintaining grayscale invariance. Experiments prove the effectiveness of the proposed work. © 2021 IEEE.

关键词： Pixels

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Rapid Automatic Underwater image Recovery Method Based on Polarimetric Imaging

Rapid Automatic Underwater Image Recovery Method Based on Po...

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2021 international conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and signal processing Technology

作者： Wang, Hongyuan Hu, Haofeng Jiang, Junfeng Xu, Jianuo Liu, Hedong Liu, Tiegen School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin300072 China Ministry of Education Tianjin300072 China School of Marine Science and Technology Tianjin University Tianjin300072 China

ISBN: (纸本)9781510655676

Underwater imaging plays an important role in underwater scientific research, biological monitoring, underwater rescue, and military defense. However, the scattering and absorption of particles in the scattering medium lead to the distortion of the originally ordered wavefront phase and the image degradation. Polarimetric imaging technology has become an effective method for underwater image recovery based on its unique advantages. However, traditional polarimetric techniques require mechanically rotating the linear polarizer to find the two images that are brightest (the backscattered light is maximally through the polarizer) and darkest (the backscattered light is maximally blocked by the polarizer). Most polarimetric imaging techniques combined with conventional methods require the presence of a background region in the image to estimate the characteristics of backscattered light. There are also some polarimetric imaging methods that do not require a background area in the image, but most of them require prior knowledge and complex algorithms. All these greatly affect the real-time application of polarimetric imaging technology. In this paper, a rapid automatic underwater image recovery method based on polarimetric imaging is proposed. We optimize the traditional polarimetric imaging method based on the scattering imaging model and solve the problem that the traditional method requires an unsupervised image quality evaluation index to correct the intermediate parameters and selecting the background region, which realize the scattering suppression for the underwater image without background region. The experimental results show that our method can process a 320,000-pixel image in only 0.007 seconds. The proposed method can be applied to the current polarimetric imaging instrument system and has a good effect on underwater image recovery, and the recovery results are almost without distortion. It has strong robustness and is also applicable to images without backgr

关键词： Underwater imaging

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Intelligent Life System Modelling, image processing and Analysis 1

引用

丛书名： Communications in Computer and Information Science

1000年

作者： Minrui Fei Shiwei Ma Xin Li Luonan Chen

ISBN: (数字)9789811672071

ISBN: (纸本)9789811672064

This three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th international conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021.;The 159 revised papers presented were carefully reviewed and selected from over 430 submissions.;The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data processing, Analysis and Control in Complex Systems; advanced Neural Network Theory and algorithms;;advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.

关键词： Simulation and Modeling Machine Learning Computer Imaging, Vision, Pattern Recognition and Graphics signal, image and Speech processing Information Systems and Communication Service Computer System Implementation

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image CAPTION GENERATOR USING ATTENTION MECHANISM 12

IMAGE CAPTION GENERATOR USING ATTENTION MECHANISM

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12th international conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Agrawal, Vaishnavi Dhekane, Shariva Tuniya, Neha Vyas, Vibha Electronics and Telecommunication Engineering College of Engineering Pune Pune India

ISBN: (纸本)9781728185958

image captioning is used to generate sentences describing the scene captured in the form of images. It identifies objects in the image, performs a few operations, and tries to find the salient features of the image. Once the system identifies this information, it should further generate the most relevant and brief description for the image, which should be both syntactically and semantically correct. With the advancements of Learning techniques, algorithms can generate text in the form of natural sentences that will be able to describe an image in its best form. The natural ability of humans to understand image content and generate descriptive text is a challenging task for a machine to imitate. The applications of image caption generation are extensive and significant. The task involves generating brief captions using various techniques like Natural language processing (NLP), Computer vision (CV), and Deep Learning (DL) techniques. This paper introduces a system that uses an attention mechanism alongside an encoder and a decoder to generate the captions. It uses a pre-trained Convolutional Neural Network (CNN) viz. Inception V3 to extract features of the image and then a Recurrent Neural Network (RNN) viz. GRU to generate a relevant caption. To generate captions, the proposed model uses an attention mechanism that is Bahdanau attention. MSCOCO dataset is used to train the model. The results validate the model's reasonable ability to understand images and generate text. © 2021 IEEE.

关键词： Computer vision

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The Segmentation Algorithm of Marine Warship image Based on Mask RCNN 12

The Segmentation Algorithm of Marine Warship Image Based on ...

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12th international conference on signal processing Systems

作者： Chen, Hongyan Yu, Xinle Commun Univ China Beijing Peoples R China

ISBN: (纸本)9781510642768;9781510642751

This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.

关键词： Mask R-CNN image instance segmentation Data to enhance Res Net JPEG2000

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