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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221-230 订阅

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Rosette Plant Centre Detection and Tracking using YOLO: An Efficient Deep Learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd International conference on Computing and machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer vision image processing Deep Learning Neural Networks

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Automatic Piped and Micro Irrigation Network 2

Automatic Piped and Micro Irrigation Network

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2nd IEEE International conference on Networking and Communications, ICNWC 2024

作者： Ponsudha, P. Shalin, Elton NirmalRaj, D. Rahul, V. Velammal Engineering College Department Of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350365269

This research paper presents cutting-edge technologies and methodologies to enhance precision agriculture and support sustainable farming practices. The study incorporates Satellite image processing for land classification, achieving a remarkable accuracy of 99.7%. The Ultrasonic Pest Repellent system showcases effective pest control with remote capabilities. The C-shaped unit integrates computer vision and machine learning, reducing chemical usage by up to 95%. An IoT-based plant disease detection system achieves superior accuracy in disease classification. The C-shaped Ground Unit addresses challenges faced by Indian farmers, optimizing plant care, nutrient supply, and pest repellence. Together, these innovations contribute to a more sustainable and efficient future for agriculture. © 2024 IEEE.

关键词： Ultrasonic applications

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Towards an image Utility Assessment Framework for machine Perception 30

Towards an Image Utility Assessment Framework for Machine Pe...

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30th European Signal processing conference (EUSIPCO)

作者： Khan, Zohaib Amjad Valenzise, Giuseppe Chetouani, Aladine Dufaux, Frederic Univ Paris Saclay Lab Signaux & Syst Cent Supelec CNRS Gif Sur Yvette France Univ Orleans Lab PRISME Orleans France

ISBN: (纸本)9789082797091

In real-world applications, images and videos used in computer vision algorithms are often distorted due, e.g., to compression and transmission. As a result, they may lose relevant information content, or they may deviate significantly from the original data distribution used to train the machine task, rendering the visual content practically useless with respect to its initial purpose. Evaluating the utility of an image for machine tasks has received little attention so far in the literature. This concept of utility is substantially different from the visual quality typically used in image/video compression, as the latter is related to the perception of the human visual system. In this paper, we propose a definition of utility as the degree of confidence by which a machine task is able to take a decision. In this context, we propose a full-reference utility loss measure: we assume that the decision on the pristine image is correct (reference), and we measure the utility loss as the confidence reduction in the decision due to a noisy input with respect to this reference. We apply this general definition on two specific tasks, classification and object detection, and we study practical solutions to predict utility, as well as the ability of our utility measure to generalize across tasks.

关键词： image utility for machines machine perception task-based assessment image utility assessment

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A Real-Time Multimodal Deep Learning for image-to-Cartoon Conversion 3

A Real-Time Multimodal Deep Learning for Image-to-Cartoon Co...

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3rd International conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Karthik, Raja Pavan Vamsi, Kalla Yadu Reddy, Veeramreddy Sourya Tejarsha Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350343632

In the era of digital imagery, there is a great interest in finding new and creative ways to express ourselves and make our images look beautiful. One such fascinating method is cartoonization, a process that transforms ordinary images into visually appealing cartoon images. This paper explores the integration of cutting-edge computer vision algorithms, traditional image processing methods, and Neural Networks to achieve cartoonization. The main focus is on combining object segmentation with cartoonization in a smooth and seamless way, which offers a unique and innovative approach to improving images. By thoroughly considering various techniques and how they can be used together, our research not only gives a complete understanding of these methods but also highlights how they can transform the field of digital artistry. By exploring the integration between methods, the study sheds light on how these techniques contribute to the evolving landscape of digital artistry. The research suggests that the fusion of computer vision, traditional image processing, and machine Learning techniques holds promising potential for pushing the boundaries of creative expression in the digital realm, offering new ways for creating efficient cartoon images. © 2023 IEEE.

关键词： Cartoonification Cartooning Color Quantization Edge Detection Region-Based Convolutional Neural Networks (R-CNN)

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Event-Based Hand Detection on Neuromorphic Hardware Using a Sigma Delta Neural Network 33rd

Event-Based Hand Detection on Neuromorphic Hardware Using a ...

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33rd International conference on Artificial Neural Networks and machine Learning (ICANN)

作者： Azzalini, Loic Gluege, Stefan Struckmeier, Jens Sandamirskaya, Yulia ZHAW Zurich Univ Appl Sci Winterthur Switzerland WAIYS GmbH Langen Germany

ISBN: (纸本)9783031723582;9783031723599

The development of deep learning (DL) models has dramatically improved marker-free human pose estimation, including an important task of hand tracking. However, for applications in real-time critical and embedded systems, e.g. in robotics or augmented reality, hand tracking based on standard frame-based cameras is too slow and/or power hungry. The latency is limited by the frame rate of the image sensor already, and any subsequent DL processing further increases the latency gap, while requiring substantial power for processing. Dynamic vision sensors, on the other hand, enable sub-millisecond time resolution and output sparse signals that can be processed with an efficient Sigma Delta Neural Network (SDNN) model that preserves the sparsity advantage in the neural network. This paper presents the training and evaluation of a small SDNN for hand detection, based on event data from the DHP19 dataset deployed on Intel's Loihi 2 neuromorphic development board. We found it possible to deploy a hand detection model in neuromorphic hardware backend without a notable performance difference to the original GPU implementation, at an estimated mean dynamic power consumption for the network running on the chip of approximate to 7 mW.

关键词： Event-based vision Neuromorphic Hardware Hand Tracking Sigma Delta Neural Networks

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SAR image Fusion Classification Based on Improved D-S Evidence Theory 8

SAR Image Fusion Classification Based on Improved D-S Eviden...

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2024 8th International conference on machine vision and Information Technology, CMVIT 2024

作者： Liang, Jialu Fang, Hui Zhang, Xinggan School of Electronic Science and Engineering Nanjing University Nanjing China

applications for classifying Synthetic Aperture Radar (SAR) images are critical to environmental monitoring, urban planning, and land resource surveying. Fusion approaches work well for increasing SAR image categorization accuracy. However, effective processing of uncertainty information is frequently overlooked by conventional fusion classification systems. This paper presented a fusion classification approach for SAR images based on improved Dempster-Shafer (D-S) evidence theory, taking into account the uncertainty and possible conflicts in the classifiers' output. First, after adaptively identifying the optimal hyperparameters of models, the SAR data is identified using Gradient Boosting machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF). Different weights are then allocated in accordance with the variations in classifier performance. Ultimately, enhanced D-S evidence theory is used to integrate the output of these classifiers. In addition, uncertainty in classification results is also modeled and visualized. Experiments with Flevoland and Oberpfaffenhofen datasets show that the accuracy of this method is 85.21% and 91.88% respectively, outperforming both the soft voting ensemble classifier (SVE) and D-S evidence theory fusion method. This approach enhances the comprehension and capacity to manage uncertainty in the model output in addition to increasing the accuracy and reliability of the classification of SAR data. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic aperture radar

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Using Monocular Depth Estimation for Distance Estimation in a Moving Vehicle 1

Using Monocular Depth Estimation for Distance Estimation in ...

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1st International conference on Computer Graphics and image processing, CGIP 2023

作者： De Guzman, Lanz Benedict N. Raymond See, Aaron De la Salle University Manufacturing Engineering and Management Department Manila City Philippines Southern Taiwan University of Science and Technology Department of Electrical Engineering Tainan City Taiwan

ISBN: (纸本)9798350333183

Accompanying the increase in demand for autonomous systems and robotic solutions is the increase in the relevance of various depth estimation technologies. Monocular Depth Estimation (MDE) is used to predict distances by generating depth maps using only a single RGB camera. However, without out-of-the-box calibration or ground truth reference for generated depth values from MDE models its use case in practical applications is limited. This research introduces a method of actualizing generated depth map values for different applications. The proposed system involves the utilization of machine vision using YOLO for object detection, followed by the computation of the lens optic algorithms to calculate the distance. Results demonstrated a real-time environment detection and depth estimation solution with more than 90% accuracy for measuring object depth in static environments. Furthermore, the system was also successfully tested in a moving vehicle to provide an estimated distance of surrounding vehicles. In the future, further tests will be done to improve the accuracy and calculation speed for use in car safety. © 2023 IEEE.

关键词： Computer vision

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MUWS 2024: The 3rd International Workshop on Multimodal Human Understanding for the Web and Social Media 24

MUWS 2024: The 3rd International Workshop on Multimodal Huma...

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4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Kastner, Marc A. Cheema, Gullal S. Hakimov, Sherzod Garcia, Noa Kyoto Univ Kyoto Japan Leibniz Univ Hannover L3S Res Ctr Hannover Germany Univ Potsdam Potsdam Germany Osaka Univ Osaka Japan

ISBN: (纸本)9798400706028

Multimodal human understanding and analysis are emerging research areas that cut through several disciplines like Computer vision (CV), Natural Language processing (NLP), Speech processing, Human-Computer Interaction (HCI), and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights on perceptual understanding through signs and symbols across multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media. The theme of the MUWS workshop, multimodal human understanding, includes various interdisciplinary challenges related to social bias analyses, multimodal representation learning, detection of human impressions or sentiment, hate speech, sarcasm in multimodal data, multimodal rhetoric and semantics, and related topics. The MUWS workshop is an interactive event and includes keynotes by relevant experts, a poster session, research presentations and discussion.

关键词： multimodality machine learning image-text relations social media web human understanding semiotics

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Research on Water Conservancy Project Monitoring and Intelligent Identification Method Based on Computer vision Technology

Research on Water Conservancy Project Monitoring and Intelli...

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2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhu, Jin Yang, Feng Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Zhejiang Hangzhou310009 China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou310020 China

ISBN: (纸本)9798350369212

Monitoring and maintenance of water resources projects is essential to ensure project safety and environmental protection. Traditional monitoring methods often rely on manual inspections and sensor data, but these methods suffer from high cost, low efficiency and limited monitoring range. With the rapid development of computer vision technology, its potential for application in water conservancy engineering has gradually emerged. The purpose of this paper is to study the water conservancy project monitoring and intelligent identification methods based on computer vision technology. We first outline the basic concepts of computer vision and its application scenarios in water conservancy engineering, including structural health monitoring, environmental monitoring and disaster management. Then, the specific applications of image processing techniques, machine learning and deep learning in intelligent recognition are described in detail, especially how these techniques can be utilized to achieve an efficient and accurate real-time monitoring system. Through several case studies, this paper demonstrates the effects and advantages of the application of computer vision technology in actual water conservancy projects. Finally, it summarizes the main findings of the current study and looks forward to the future technological development and application prospects, pointing out the direction of further research. The findings of this paper show that computer vision-based monitoring and intelligent identification methods not only improve the accuracy and efficiency of monitoring, but also provide new technical means for the management and maintenance of water conservancy projects. © 2024 IEEE.

关键词： Environmental monitoring

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New Indicators and Standards for Measuring of the End Mill's Helical Groove by image processing 10

New Indicators and Standards for Measuring of the End Mill's...

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conference on Optical Metrology and Inspection for Industrial applications X

作者： Pivkin, Petr M. Ershov, Artem A. Grechishnikov, Vladimir A. Kuznetsov, Vladimir A. Nazarenko, Ekaterina S. Malysheva, Elena Yu. Nadykto, Alexey B. Moscow State Univ Technol STANKIN Lab Micromachining Technol Moscow 127055 Russia Moscow State Univ Technol STANKIN Dept Cutting Tools & Machining Technol Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

关键词： image processing standards for measuring cutting tools optical metrology new indicators helical groove Haar-like feature

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