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

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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...

引用

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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A Qualitative Study on image Quality Enhancement, Object Detection Methods to Assist visually Impaired Users 5

A Qualitative Study on Image Quality Enhancement, Object Det...

引用

5th IEEE International conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Sajini, S. Pushpa, B. Annamalai University Faculty of Science Department of Computer and Information Science Tamil Nadu Chidambaram India

ISBN: (纸本)9781665493604

Object Detection and Tracking with Recognition (ODTR) is a predominant field that finds significant applications in surveillance and assisting visually Impaired (vi) people. Though ODTR is possible with the implication of Computer vision (CV) models, the achievement of accuracy levels seemed to be meagre due to lack of clarity of images being captured. Hence a hybrid of image processing methods could improve the efficiency of the prediction. The significant objective of this research work is to study and explore the various Digital image processing (DIP) methods to refine the image and Computer vision methods to perform ODTR that could possibly improve the performance of prediction at the end of the detection. The various image processing methods like Filtering, Segmentation and Feature Extraction were studied with the technical methods from MATLAB interface and Object detection and Tracking evaluation methods with Confusion Matrix were analyzed and presented with expected outcomes and discussions. The evaluation methods were presented with expected models for supporting the proposed framework models. © 2023 IEEE.

关键词： image enhancement

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Evolving processing Pipelines for Industrial Imaging with Cartesian Genetic Programming

Evolving Processing Pipelines for Industrial Imaging with Ca...

引用

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...

引用

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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Segmenting Sonar images with an Enhanced OTSU Algorithm 2

Segmenting Sonar Images with an Enhanced OTSU Algorithm

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Yang, Fan University of Jinan School of Electrical Engineering Jinan250022 China

ISBN: (纸本)9798350331417

Sonar image segmentation technique is crucial for underwater target tracking, among other things. Due to the undersea environment's influence, noise is easily absorbed, which leads to a poor tracking performance. In order to address the issues with the traditional one-dimensional and two-dimensional OTSU algorithms, which have a slow computation time and poor anti-noise performance, this paper proposes a fast OTSU segmentation algorithm based on wavelet boosting and analyses the algorithmic principles and implementation steps of one-dimensional OTSU algorithm, two-dimensional OTSU algorithm and its fast algorithm, fast algorithm with the variance as the eigenvalue, and maximum entropy algorithm. On the basis of this, the aforementioned algorithms are used to perform image segmentation on four photos. The segmentation findings demonstrate the method's quick processing speed and strong noise resistance, which have significant implications for sonar image segmentation applications. © 2023 IEEE.

关键词： image segmentation OTSU second generation wavelet transform sonar images

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From Paper to Pixels: A Multi-modal Approach to Understand and Digitize Assembly Drawings for Automated Systems 35th

From Paper to Pixels: A Multi-modal Approach to Understand a...

引用

35th International conference on Database and Expert Systems applications (DEXA)

作者： Seliger, Raphael Guel-Ficici, Sebnem Goehner, Ulrich Univ Appl Sci Kempten Inst Data Optimized Mfg IDF Bahnhofstr 61 D-87435 Kempten Germany

ISBN: (纸本)9783031683015;9783031683022

The transition to Industry 4.0 intensifies the demand for advanced manufacturing techniques and efficient data processing capabilities. A notable challenge in engineering is that many older engineering drawings are only available in paper form, creating significant barriers for modern automated systems. This study tackles these challenges by employing advanced deep-learning techniques alongside traditional image processing to convert legacy engineering drawings into structured, machine-readable formats. Following this digitization process, this multi-modal approach further processes drawings containing a lot of heterogeneous data by filtering non-essential details to isolate and extract critical features. This process enables the conversion of complex drawings into formats suitable for computer vision and deep learning applications. The structured datasets resulting from this process are then utilized to enhance the efficiency of automated processes significantly. For instance, they enable more efficient pick-and-place operations by providing the data necessary for machine learning-driven automation.

关键词： Deep Learning Computer vision Document Analysis Engineering Drawing Instance Segmentation

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Development of an Independent Adversarial Sample Detection Model, Based on image Features 5th

Development of an Independent Adversarial Sample Detection M...

引用

5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Xu, Long Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9789819751808

Independent adversarial sample detection is an important problem in the field of computer vision and machine learning, especially in the context of the widespread use of deep learning models. This can lead to misclassification and performance degradation of the model, so adversarial sample detection is crucial to ensure the reliability of the model. This research focuses on the development of an independent adversarial sample detection model based on image features. A new approach is proposed which does not rely on the original model but focuses on detecting adversarial features in the samples. The effectiveness and robustness of the proposed method is verified in extensive experiments. The model is able to detect independent adversarial samples with high accuracy, regardless of whether the adversarial samples are targeted at a specific deep learning model or not. In addition, the method demonstrates excellent performance on a variety of image datasets and applications in different domains. It is expected to enhance the robustness and reliability of deep learning models, and thus better cope with adversarial sample attacks in practical applications. This approach also has a wide range of applications for a variety of computer vision tasks and domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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PPIR-Net: An Underwater image Restoration Framework Using Physical Priors 29th

PPIR-Net: An Underwater Image Restoration Framework Using Ph...

引用

29th International conference on Neural Information processing

作者： Zhang, Changhua Yang, Xing Chen, Zhongshu Luo, Shimin Luo, Maolin Yan, Dapeng Li, Hao Wang, Bingyan Univ Elect Sci & Technol China Chengdu Peoples R China Nucl Power Inst China Chengdu Peoples R China

ISBN: (纸本)9789819916443;9789819916450

In recent years, underwater image processing has been a hot topic in machine vision, especially for underwater robots. A key part of underwater image processing is underwater image restoration. However, underwater image restoration is an essential but challenging task in the field of image processing. In this article, we propose an underwater image restoration framework based on physical priors, called PPIR-Net. The PPIR-Net combines prior knowledge with deep learning to greatly improve the structural texture and color information of underwater images. The framework estimates underwater transmission maps and underwater scattering maps through the structure restoration network (SRN). Moreover, the color correction network (CCN) is used to achieve image color correction. Extensive experimental results show that our method exceeds state-of-the-art methods on underwater image evaluation metrics.

关键词： Underwater image restoration Physical prior knowledge Deep learning

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