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

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Deep Learning based image classification for Automated Face Spoofing Detection using machine Learning: Convolutional Neural Network 2

Deep Learning based Image classification for Automated Face ...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Kumar, Biresh Manisha, Kumari Sinha, Anurag Kumar, Abhishek Kumar, Jeevan Amity Univ Amity Inst Informat Technol Ranchi Bihar India IGNOU Sch Comp & Informat Sci New Delhi India Jharkhand Univ Technol Comp Sci & Engn Ranchi Bihar India RVSCET RVS Coll Engn & Technol Jamshedpur Bihar India

ISBN: (纸本)9798350395334;9798350395327

Facial recognition technology has gained widespread use in various applications, raising concerns about the weakness of frameworks to confront mocking assaults. This study presents an implementation of face spoofing detection using machine learning techniques. The exploration utilizes a far-reaching system that envelops data combination, preprocessing, incorporate extraction, and model readiness. A diverse dataset comprising genuine and spoofed facial images, representing various spoofing techniques, is utilized. Feature extraction leverages Convolutional Brain Organizations (CNNs) to catch discriminative facial elements. The selected machine learning model is trained and fine-tuned, with a focus on achieving robustness against evolving spoofing methods. The evaluation of the implemented system involves rigorous testing on a separate dataset, utilizing estimations like precision, exactness, survey, and F1-score. The study investigates post-processing techniques and considerations for real-time deployment, ensuring practical applicability is done by the method convolutional neural network (CNN). Cross-approval is performed to evaluate the model's speculation capacities, and the deployment phase explores integration into real-world scenarios. Ethical considerations, user feedback, and compliance with data privacy regulations are integral components of the study.

关键词： Face Spoofing image Classifying Face Recognition Computer vision Deep Learning

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SS-SFR: Synthetic Scenes Spatial Frequency Response on virtual KITTI and Degraded Automotive Simulations for Object Detection 26

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtu...

引用

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

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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SeafloorAI: A Large-scale vision-Language Dataset for Seafloor Geological Survey 38

SeafloorAI: A Large-scale Vision-Language Dataset for Seaflo...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Nguyen, Kien X. Qiao, Fengchun Trembanis, Arthur Peng, Xi Deep-REAL Lab Department of Computer and Information Sciences University of Delaware United States School of Marine Science and Policy University of Delaware United States

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers that is curated in collaboration with marine scientists. We further extend the dataset to SeafloorGenAI by incorporating the language component in order to facilitate the development of both vision- and language-capable machine learning models for sonar imagery. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images, 827K annotated segmentation masks, 696K detailed language descriptions and approximately 7M question-answer pairs. By making our data processing source code publicly available, we aim to engage the marine science community to enrich the data pool and inspire the machine learning community to develop more robust models. This collaborative approach will enhance the capabilities and applications of our datasets within both fields. Our code repository are available under the CC-BY-4.0 license. © 2024 Neural information processing systems foundation. All rights reserved.

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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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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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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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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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