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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

共 3255 条记录，以下是111-120 订阅

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Role of Thermal images in Various applications of Computer vision 4

Role of Thermal Images in Various Applications of Computer V...

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4th International conference on Intelligent Engineering and Management, ICIEM 2023

作者： Gupta, Ravina Jain, Sarika Kumar, Manoj AIIT Amity University Noida India EIS University of Wollongong Dubai United Arab Emirates

ISBN: (纸本)9798350341126

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. This paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. This study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets. © 2023 IEEE.

关键词： Object detection

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A Systematic Review: How Computer vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd International conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： Computer vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation machine Learning

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No-reference image Quality Metric for NeRF (Neural Radiance Fields) Rendering in Automotive applications 26

No-reference Image Quality Metric for NeRF (Neural Radiance ...

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26th Irish machine vision and image processing conference, IMVIP 2024

作者： Raymond, Mary Sistu, Ganesh Gallagher, Louis Valeo Vision Systems Ireland Maynooth University Ireland

ISBN: (纸本)9781837242672

Neural Radiance Fields (NeRF) rendering is a promising Artificial intelligence (AI) technology for generating photorealistic views, with significant potential for automotive applications. However, traditional metrics such as Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual image Patch Similarity (LPIPS) often fail to evaluate the model's quality for novel viewpoints outside the dataset range, which is crucial for real-life use. This study introduces the Fréchet Inception Distance (FID) as a no-reference image quality metric for novel viewpoints. Our experiments demonstrate that FID aligns well with human quality assessments and is effective in automotive scenarios with fisheye images. The need for further research on FID normalization, the sample sizes of generated viewpoints used to calculate FID, and measures of viewpoint difficulty is highlighted. Adopting FID advances NeRF evaluation, enhancing assessments in real life scenarios within automotive and robotics, and improving autonomous system performance and safety. More results from our experiments are available here https://***/watch?v=Lb8azH79EI0. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Rendering (computer graphics)

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Autonomous Object Detection and Counting using Edge Detection and image processing Algorithms 7

Autonomous Object Detection and Counting using Edge Detectio...

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7th International conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Patil, Swati B. Shimpi, Jay Chandrakant Tanawade, Archana Girish Chavan, Pranali Gajanan Tandulkar, Vrushali Shrimant Information Technology Vishawakarma Institute of Information Technology Pune India T and T Infra Ltd Pune India

ISBN: (纸本)9798350397284

machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques. © 2023 IEEE.

关键词： Cameras

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Identification and Categorization of Paddy Leaf Diseases Using Deep Learning for Efficient Agricultural Practices

Identification and Categorization of Paddy Leaf Diseases Usi...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： George, Annie Sunil Devi, S. Vinita Sushila Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350382693

Plant diseases can be quickly detected to lessen the harm they cause to crops. Convolutional neural networks are widely used in deep learning, specifically for problems related to pattern recognition and machine vision. This paper aims to provide a comprehensive overview of the numerous image processing and machine learning strategies for rice plant disease diagnosis. It does this by examining the various algorithms and machine learning techniques used to diagnose illnesses in rice plants using photographs of diseased rice plants. This study discusses several methods and offers a concise synopsis of important concepts in the fields of machine learning and image processing in relation to the classification and identification of illnesses in rice plants. This study examines and contrasts various algorithmic models and methods for classifying and identifying diseases that impact rice plants. It includes scientific results that over the last five years have been published in international review publications. In addition, this paper presents an overview and analysis of various methods used to identify agricultural illnesses, leaf health, and the image processing techniques used for the same. The framework mentioned here, can be used to more effectively detect and diagnose various rice crop diseases, which can cause the agriculture industry to experience large losses owing to rice plant diseases. These frameworks are efficient enough to safely automate repeated processes, generating enough data for additional study. © 2024 IEEE.

关键词： Convolutional neural networks

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FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC 31

FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC

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2024 International conference on image processing

作者： Wang, Dayong Yu, Junyi Lu, Xin Dufaux, Frederic Guo, Hongwei Guo, Hui Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Honghe Univ Sch Engn Mengzi Yunnan Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.

关键词： VVC SCC content type fast coding mode decision BDPCM

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Macro-Scale Pattern Recognition and Coordinate Identification in Real-time Spatio-temporal Overlap for Photonics Engineering applications 22

Macro-Scale Pattern Recognition and Coordinate Identificatio...

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22nd IFAC conference on Technology, Culture and International Stability (TECIS)

作者： Al-Juboori, Haider South East Technol Univ Fac Engn Dept Elect Engn & Commun 806 Killeshin BldgKilkenny Rd Carlow R93 V960 Ireland

The significance of high-speed machine vision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various intriguing applications in ultralow-latency machine vision processing. However, the low frame rate of image sensors-which usually operate at tens of hertz-fundamentally limits the processing rate. The paper will conceptualize and develop the computerized pattern recognition technique that can be applied to investigate light beam profiles and extract the desired information according to the purpose required in this case study. In the current work, the automatic detection and inspection of laser spots were designed to perform analysis and alignment for laser beam in comparison with the electron spot beam using the LabVIEW graphical programming environment, especially when the laser and electron beams overlap. This is one of the important steps for realizing the fundamental aim of test-FEL to produce short wavelengths with the second, third, and fifth harmonics at 131.5, 88, and 53 nm, respectively. The tentative version of the program achieved the elementary purpose, which fulfilled the accurate transversal alignment of the ultrashort laser pulses with the electron beam in the system of the FEL test facility at MAX-Lab, in addition to studying the beam's stability and jittering range. Copyright (C) 2024 The Authors.

关键词： intelligent systems pattern matching real-time tracking computer vision concepts supporting control automation and semi-robotic systems

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Efficient Approximate Vedic Multiplier: Design, Analysis, and Application in image Blending 2

Efficient Approximate Vedic Multiplier: Design, Analysis, an...

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2nd IEEE International conference on Computer vision and machine Intelligence, CVMI 2023

作者： Gupta, Sanjiv Kumar Yadav, Nilesh Kumar Dhawan, Amit Tiwari, Manish Jha, Sumit Kumar Motilal Nehru National Institute of Technology Department of Electronics and Communication Engineering Uttar Pradesh Allahabad211004 India

ISBN: (纸本)9798350305142

Approximate computing has become a widely recognized method for designing energy-efficient arithmetic architectures in the context of error-tolerant applications. This paper presents the design and analysis of a 4-bit approximate Vedic multiplier (AVMT) using the Urdhva Tiryagbhyam method. This Vedic approach, involving vertical and crosswise steps, outperforms traditional multiplication in terms of efficiency. An approximate 2-bit multiplier (AVM2) is designed, and an AVMT is proposed using AVM2. The proposed architecture has better propagation delay and less area utilization compared to other conventional multipliers. AVMT has an 11% reduction in area consumption and a 12% increase in processing speed compared to the exact Vedic multiplier. To assess its practicality in real-world scenarios, the proposed multiplier is integrated into an image-blending application. The results indicate that the system achieves a Structural Similarity Index (SSIM) average value of 0.91, which proves to be suitable for error-resilient image processing applications. © 2023 IEEE.

关键词： Energy efficiency

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780426

The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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Make a Long image Short: Adaptive Token Length for vision Transformers

Make a Long Image Short: Adaptive Token Length for Vision Tr...

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5th International Workshop on Learning with Imbalanced Domains - Theory and applications / European conference on machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zhou, Qiqi Zhu, Yichen Shanghai Univ Elect Power Shanghai Peoples R China Midea Grp Shanghai Peoples R China

ISBN: (纸本)9783031434143;9783031434150

The vision transformer is a model that breaks down each image into a sequence of tokens with a fixed length and processes them similarly to words in natural language processing. Although increasing the number of tokens typically results in better performance, it also leads to a considerable increase in computational cost. Motivated by the saying "A picture is worth a thousand words," we propose an innovative approach to accelerate the ViT model by shortening long images. Specifically, we introduce a method for adaptively assigning token length for each image at test time to accelerate inference speed. First, we train a Resizable-ViT (ReViT) model capable of processing input with diverse token lengths. Next, we extract token-length labels from ReViT that indicate the minimum number of tokens required to achieve accurate predictions. We then use these labels to train a lightweight Token-Length Assigner (TLA) that allocates the optimal token length for each image during inference. The TLA enables ReViT to process images with the minimum sufficient number of tokens, reducing token numbers in the ViT model and improving inference speed. Our approach is general and compatible with modern vision transformer architectures, significantly reducing computational costs. We verified the effectiveness of our methods on multiple representative ViT models on image classification and action recognition.

关键词： vision transformer token compression

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