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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81-90 订阅

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Representation Extraction Using Hyperbolic Knowledge Distilled Framework - An Industrial Application on High Risk Environment 16

Representation Extraction Using Hyperbolic Knowledge Distill...

引用

16th International conference on Computer and Automation Engineering (ICCAE)

作者： Kumar, Vijeth Murugesan, Malathi Veneri, Giacomo Baker Hughes Bangalore India Baker Hughes Florence Italy

ISBN: (纸本)9798350370058;9798350370164

We propose a computer vision architecture based on Hyperbolic networks, contrastive learning and knowledge distillation to detect unsafe behavior in energy production and oil & gas plants. Data scarcity poses a significant challenge to develop machine learning applications in industry. Indeed, the data may be incomplete, inconsistent, or biased, making it difficult to develop accurate and reliable models. Insufficient data during training phase has direct impact on the models' representation learning capabilities;with the aid of vision Transformers (ViTs), we are able to solve data crunch situations by learning efficient representations of the existing data. We harnessed the power of ViTs, as it incorporates more global information, leading to quantitatively stronger intermediate feature representations. Further, we approached the task with contrastive learning and obtained pairs of samples which are similar, to tackle the limited data availability in our industrial use case. The proposed approach by applying hyperbolic embeddings helps in extracting complex relationships in the data. Furthermore, the size of the model makes it suitable for devices with low computational capabilities such as unmanned robots.

关键词： Hyperbolic networks Contrastive Learning and Knowledge Distillation image processing image Understanding vision Transformers (ViTs) Data Scarcity

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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Generating Caption From images Using Flickr image Dataset 15

Generating Caption From Images Using Flickr Image Dataset

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Piyush Kaur, Kiranpreet Gandotra, Aaryan Patial, Pranay Radhika Gaba, Sehaj Chandigarh University Department of Cse Mohali India

ISBN: (纸本)9798350370249

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results. © 2024 IEEE.

关键词： Caption from an image Caption of an image Encoder- Decoder outputs text from an image

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RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGINEERING STRUCTURES USING machine vision TECHNOLOGY 43

RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGIN...

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ASME 43rd International conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yu, Qifeng Han, Yudong Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

ISBN: (纸本)9780791887806

The harsh marine atmospheric conditions, including high temperatures, humidity, and salt spray, prevalent in the coastal areas of Hainan, pose a significant challenge to the durability of ground facilities and equipment, often resulting in corrosion and functional degradation. Hence, accurate monitoring of corrosion is paramount for maintaining coastal engineering structures. This study leveraged data from the Chinese National Center for Materials Corrosion and Protection Science to extract corrosion morphology features using image analysis techniques. Subsequently, a corrosion identification model was developed using machine learning algorithms. The model's accuracy was validated through various evaluation metrics. The research outcomes have practical applications in the maintenance and management of coastal engineering structures by providing automatic corrosion morphology recognition. This enables maintenance personnel to promptly undertake repair measures, thereby reducing maintenance costs and enhancing structural sustainability.

关键词： Corrosion morphology machine learning techniques image processing YOLO v5 automatic recognition

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Research on Intelligent Tracking System for Hearing-Impaired Athletes Based on machine vision 2

Research on Intelligent Tracking System for Hearing-Impaired...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Jiashi, Jiangxiong Leshan Normal University Sichuan Leshan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

In this paper, deformation correction, feature extraction, image filtering, particle manipulation and other steps are used to achieve the relative positioning between the objects. The visually-assisted image processing process is implanted into the virtual instrument to achieve real-time location and tracking of moving objects. It can help the hearing-impaired communicate from sign language to emotional speech. The system includes gesture recognition, expression emotion calculation, speech emotion synthesis and other modules. And it can carry out sign language synchronous transmission, two-way interaction, emotional expression. The test results show that in the space of 1.2 m∗1.2 m, the positioning accuracy achieved by this algorithm is about 1 cm, and the root-mean-square error is 0.3 cm, The system can easily transform gesture language into emotional language. This system can make the hearing-impaired people and healthy people have more direct communication. Therefore, the translation of sign language by deaf athletes is no longer just to maintain a neutral tone, but to add more emotions. © 2024 IEEE.

关键词： Mean square error

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An Experimental Evaluation of LLM on image Classification 35th

An Experimental Evaluation of LLM on Image Classification

引用

35th Australasian Database conference

作者： Wu, Jiaxuan Tang, Xushuo Yang, Zhengyi Hao, Kongzhang Lai, Longbin Liu, Yongfei Univ Calif Irvine Irvine CA 92697 USA Euler AI Sydney NSW Australia Univ New South Wales Sydney NSW Australia Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819612413;9789819612420

image classification is one of the fundamental tasks in computer vision (CV) and has numerous practical applications. Traditionally, machine learning and deep learning methods such as k-Nearest Neighbors (kNN), decision trees, and Convolutional Neural Networks (CNN) have been widely used to perform this task. However, with the recent emergence of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), originally designed for natural language processing, their cross-domain applications, including in CV, are now being explored. In this paper, we investigate the capabilities of GPT-4o, a variant of the GPT model, for image classification on the Fashion-MNIST dataset. By using carefully designed prompts, we evaluate GPT-4o's performance and compare it with more traditional models. Our study offers insights into the cross-domain potential of GPT models, explores how prompt engineering can enhance GPT's performance on image classification tasks, and suggests new avenues for developing more flexible and adaptable multimodal LLM systems. The code can be found at https://***/Tanghaha1424/gpt-fashionmnist.

关键词： LLM ChatGPT image Classification Fashion-MNIST

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

This research introduces "Jaddah,"an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency. © 2025 IEEE.

关键词： image enhancement

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Hardware and Software Integration of machine Learning vision System Based on NVIDIA Jetson Nano 1

引用

Future of Information and Communication conference, FICC 2024

作者： Manolescu, Denis Reid, David Secco, Emanuele Lindo Robotics Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

This study investigates the capabilities and flexibility of edge devices for real-time data processing near the source. A configurable Nvidia Jetson Nano system is used to deploy nine pre-trained computer vision models, demonstrating proficiency in local data processing, analysis and providing real-time feedback. Additionally, the system offers deployment control via a customized Graphical User Interface (GUI) and proves very low-latency inference re-stream to other local devices using the G-Streamer framework. The machine Learning models which cover a wide range of applications, including image classification, object recognition or detection, depth estimation and semantic segmentation, show potential for IoT and industrial applications. Further, the fusion of these capabilities with AI and machine learning algorithms unveils a promising perspective for substantial industrial redevelopment. This research underscores the strategic significance of edge devices in modern computational frameworks and their potential role in future technological advancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer vision

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Evolving processing Pipelines for Industrial Imaging with Cartesian Genetic Programming

Evolving Processing Pipelines for Industrial Imaging with Ca...

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4th IEEE International conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

作者： Margraf, Andreas Cui, Henning Stein, Anthony Haehner, Jorg Fraunhofer IGCV Digital Prod & AI Augsburg Germany Univ Augsburg Organ Comp Grp Augsburg Germany Univ Hohenheim AI Agr Engn Stuttgart Germany

ISBN: (纸本)9798350337440

The reconfiguration of machine vision systems heavily depends on the collection and availability of large datasets, rendering them inflexible and vulnerable to even minor changes in the data. This paper proposes a refinement of Miller's Cartesian Genetic Programming methodology, aimed at generating filter pipelines for image processing tasks. The approach is based on CGP-IP, but specifically adapted for image processing in industrial monitoring applications. The suggested method allows for retraining of filter pipelines using small datasets;this concept of self-adaptivity renders high-precision machine vision more resilient to faulty machine settings or changes in the environment and provides compact programs. A dependency graph is introduced to rule out invalid pipeline solutions. Furthermore, we suggest to not only generate pipelines from scratch, but store and reapply previous solutions and re-adjust filter parameters. Our modifications are designed to increase the likelihood of early convergence and improvement in the fitness indicators. This form of self-adaptivity allows for a more resource-efficient configuration of image filter pipelines with small datasets.

关键词： cgp image filters monitoring segmentation

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Semantic Threads Enabling image-Text Retrieval via VQA Transformers 1

Semantic Threads Enabling Image-Text Retrieval via VQA Trans...

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1st International conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Noorbhasha, Junnubabu Guddeti, Rohitha Lingutla, Satvika Etikikota, Sujitha Kumkumkari, Santhosh Madanapalle Institute of Technology and Science Computer Science and Technology Madanapalle India

ISBN: (纸本)9798350365405

The integration of vision and language has propelled the advancement of artificial intelligence systems. Visual Question Answering (VQA) stands at the nexus of computer vision and natural language processing, enabling machines to comprehend and respond to image-related queries. This paper introduces a novel VQA approach harnessing the capabilities of the BLIP (Bootstrapping Language image Pretrained) model, a transformer-based architecture esteemed for its natural language understanding prowess. The methodology involves image preprocessing, and question translation into a standardized language for efficient processing by BLIP. Mainly, the study integrates multilingual support into the VQA framework, facilitating seamless interaction with users across diverse linguistic backgrounds. Through rigorous experimentation, this paper demonstrates the effectiveness of our approach in accurately answering questions in various languages These findings underscore the robustness and adaptability of the BLIP model in handling multilingual inputs, thereby enhancing accessibility and usability in real-world applications. This research contributes to advancing the state-of-the-art in VQA systems by addressing language barriers and promoting inclusivity in human-machine interaction. © 2024 IEEE.

关键词： Semantics

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