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Proceedings of international conference on advanced Computing Applications 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Jyotsna Kumar Mandal Rajkumar Buyya Debashis De

ISBN: (数字)9789811652073

ISBN: (纸本)9789811652066

This book gathers selected high-quality research papers presented at the 2nd international conference on advanced Computing Applications (ICACA 2021), held virtually during 27––28 March 2021. The book is divided into four sections. These are communication and computing, signal processing and multimedia, computational intelligence and data analytics and decision computing. The topics covered are advanced communication technologies, IoT-based systems and applications, network security and reliability, virtualization technologies, compressed sensors and multimedia applications, signal image and video processing, machine learning, pattern recognitions, intelligent computing, big data analytics, analytics in bio-computing, AI-driven 6G mobile wireless networks and autonomous driving.

关键词： Computational Intelligence Artificial Intelligence signal, image and Speech processing Communications Engineering, Networks Cyber-physical systems, IoT Professional Computing

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Simple Linear Iterative Clustering with Efficiency 1

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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： Liao, Nannan Liu, Hui Li, Cheng Ren, Xia Guo, Baolong Xidian Univ Inst Intelligent Control & Image Engn Xian Peoples R China

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

As an instructive work to generate satisfactory superpixels, simple linear iterative clustering (SLIC), has become fundamental and popular in various computer vision tasks. In this work, the algorithm is reconsidered, and an integrated framework is proposed to further improve the running speed and segmentation performance. In the first stage, a fast convergence strategy for clustering is presented on SLIC to redistribute the seeds efficiently. This is done to initialize a set of clustering centers that is fairly representative of local information on the image plane. Then, a followup work of SLIC termed simple non-iterative clustering (SNIC) is utilized to process more accurate segmentation without any post-processing to enforce connectivity. Experimental results show that the framework could generate a synergetic effect and performs better than previous superpixel algorithms in a limited computational time.

关键词： Clustering Initialization Redistribution Superpixels

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Radar Active Jamming signal Identification Method Based on Time-frequency image Features 2

Radar Active Jamming Signal Identification Method Based on T...

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2nd international conference on image processing and Media Computing, ICIPMC 2023

作者： Hao, Wanbing Feng, Xiaoyi Jiang, Xiaoyue Northwestern Polytechnical University School of Electronics and Information Xi'an China

ISBN: (纸本)9798350326611

In this paper, the Choi-Williams method is used to describe five typical jamming signals, and the grayscale images of the time-frequency two-dimensional distribution of the jamming signals are obtained. The contour information is maximally enhanced by median filtering and image segmentation processing algorithms for the grayscale images. The effective time-frequency domain feature parameter set is extracted and the dimensionality reduction is done. SVM is used to identify typical radar active jamming signals and provide a priori information for radar anti-jamming. This paper verifies the adopted method by MATLAB simulation experiments and analyzes the performance and feasibility of the adopted method. © 2023 IEEE.

关键词： image segmentation

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Deep Learning Model for Detecting and Diagnosing Plant Disease

Deep Learning Model for Detecting and Diagnosing Plant Disea...

引用

2021 international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Moon, Nazmun Nessa Sharmin, Ms. Shayla Hossain, Refath Ara Jahan, Israt Nur, Fernaz Narin Rahman, Md. Mosfikur Daffodil Internationsl University Department of Computer Science and Engineering Dhaka Bangladesh Notre Damn University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665425032

This work deals with some plant disease detection like Potato, Pepper, and Tomato, Rice, etc. based on CNN image processing Technology. An enormous improvement has been made in the field of picture preparation and AI and its application in different parts of designing. The authors have entered the time of digitization. The authors have caught pictures with the assistance of an advanced camera. All the clearer the pictures are better and produce the outcomes. In this report, the authors have done the arrangement of sickness-free, somewhat infected, and sick leaves. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research, and on-farm production. Traditional direct measurement methods are generally simple and reliable, but they are time-consuming, laborious, and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, CNN image processing algorithms are developed to detect disease by identifying the color feature of the defected area. Subsequently, the rotted area was segmented from an image, and the area of the rotted leaf portion was deduced from the Pepper and tomato plant feature data. The results showed a promising performance of this automatic vision-based system it is found 94% accuracy average in practice with easy validation in a large amount of data. © 2021 IEEE.

关键词： image processing

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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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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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Improving the spatial resolution of digital images and video sequences using subpixel scanning

Improving the spatial resolution of digital images and video...

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2021 All-Russian conference with international Participation "Spatial Data processing for Monitoring of Natural and Anthropogenic Processes", SDM 2021

作者： Reznik, Aleksandr L. Soloviev, Aleksandr A. Torgov, Andrey V. Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia

High-performance method for improving the resolution of digital images and video sequences based on minimum-variance signal reconstruction are considered. A distinctive feature of the developed algorithms is that they allow (with the availability of modern computing power) to obtain improved images and video in "real time". © 2021 Copyright for this paper by its authors.

关键词： High-performance algorithms image reconstruction

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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...

引用

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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image CAPTION GENERATOR USING ATTENTION MECHANISM 12

IMAGE CAPTION GENERATOR USING ATTENTION MECHANISM

引用

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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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： image sensors Training Computer vision Pipelines Object detection Computer architecture Dynamic range

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