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

共 3060 条记录，以下是71-80 订阅

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Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

引用

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

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 IEEE.

关键词： Constrained optimization

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image Classifier Using Resource-Constrained Device and Tiny machine Learning 1st

Image Classifier Using Resource-Constrained Device and Tiny ...

引用

1st International conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： vinod, K.S. Kanmani Ruby, E.D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

image classification is one of the main parts of computer vision, which is important in applications like self-driving automotives/vehicle systems. While working with image/video data it needs huge amount of resources including computing power, graphic processing units (GPU), memory, high end CPUs, etc. We can use the small microcontrollers to do the same task, by using high-end machines for training and building the model and converting the model so that they fit into microcontroller unit, by using the method called Transfer Learning. In our work, we use Arduino nano 33 BLE sense and OV7675 camera module and online machine learning framework called Edge impulse for building the model. It is found that our tiny machine learning model works well and provides a real-time solution for image classification in the resource-constrained scenario. The experimental results show that the image classifier is performing with around 100% accuracy and so has got a wide scope in real-time classification applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphics processing unit

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

Authentication and Verification in Human-Robot Cooperative R...

引用

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

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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2024 IEEE 8th International conference on Signal and image processing applications, ICSIPA 2024

2024 IEEE 8th International Conference on Signal and Image P...

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8th IEEE International conference on Signal and image processing applications, ICSIPA 2024

ISBN: (纸本)9798350352368

The proceedings contain 39 papers. The topics discussed include: performance analysis of several CNN based models for brain MRI in tumor classification;MRI-based lumbar sagittal alignment classification system;3D mapping and landing zone identification in complex terrains using DSM and photogrammetry;vision language models for oil palm fresh fruit bunch ripeness classification;towards no shadow: region-based shadow compensation on low-altitude urban aerial images;comparative analysis of deep learning architectures for blood cancer classification;exploration of group and shuffle module for semantic segmentation of sea ice concentration;on handcrafted machine learning features for art authentication;and acoustic signature modelling of marine vessels in various environmental and operational conditions.

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges 9

Maritime Image Stabilization: A Comprehensive Review of Tech...

引用

9th International conference on Electronic Technology and Information Science (ICETIS)

作者： Wei, Enping Tan, Yong Chai Tai, vin Cent Hao, Yanan Zhang, Xiaodong Zhang, Tian SEGI Univ Fac Engn Built Environm & Informat Technol Ctr Modelling & Simulat Kuala Lumpur Malaysia

ISBN: (纸本)9798350388350;9798350388343

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： image Stabilization Assessment Application Maritime Environment Ship-borne Camera

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Intelligent patrol wheeled robot based on image recognition 3

Intelligent patrol wheeled robot based on image recognition

引用

3rd International conference on machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Wu, Wei Lv, Xinyue Pei, Zeyu An, Changying Ning, Jixiang Gao, Zhenzhen College of Engineering China University of Petroleum-Beijing at Karamay Karamay834000 China

ISBN: (纸本)9781510681880

In recent years, with the development of artificial intelligence technology, intelligent robots are more and more widely used in many fields. In this paper, an intelligent patrol wheeled robot based on image recognition is designed and implemented. The robot utilizes Raspberry PI as its core control unit and integrates sensing equipment such as cameras and ultrasonic sensors. The functions of autonomous navigation, intelligent obstacle avoidance and path tracking are improved through computer vision and machine learning technology. The main technologies include OpenCV for image processing, HOG feature extraction and SVM algorithm for traffic sign recognition, and traffic light detection system based on color recognition. In addition, this paper also discusses the use of positional PID control algorithm to achieve visual lane keeping scheme. Through detailed system design and experimental verification, this paper shows the performance benefits of the intelligent robot in practical applications, and provides a new idea for the future research of intelligent robots. It is expected that the robot has a wide range of application potential in intelligent transportation, public safety and other fields. © 2024 SPIE.

关键词： Intelligent robots

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From Templates to Transformers: A survey of Multimodal image Captioning Decoders

From Templates to Transformers: A survey of Multimodal Image...

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2023 International conference on Computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Sharma, Himanshu Padha, Devanand Central University of Jammu Department of Computer Science and It Rahya-Suchani Jammu India

ISBN: (纸本)9798350338003

Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer vision (CV) in conjunction with Natural Language processing (NLP). The image-to-text synthesis, also known as image Captioning (IC), has numerous applications in visual assistance, machine vision, remote security, healthcare, and remote sensing, among others. Since their origin, the IC frameworks have been constructed utilizing a two-subcomponent pipeline consisting of the visual feature extraction and natural language modeling subcomponents. The recent surge of interest in the applications of sequential deep learning models in machine translation has resulted in the development of efficient language modeling architectures. Deep sequential modeling architectures, such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU), tackle the intricacies of the multi-modular space far better than existing machine learning-based translation approaches. The tremendous growth of deep language modeling architectures in IC necessitates a comprehensive review of its literature. In this survey, we conduct an exhaustive and analytical analysis of the language modeling architectures used in IC frameworks for caption generation, along with their training corpus datasets. We also identify a list of open research issues and potential research areas for future work. © 2023 IEEE.

关键词： Natural language processing systems

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Performance Improvement in Welding Operations Through image processing 2

Performance Improvement in Welding Operations Through Image ...

引用

2nd International conference on Artificial Intelligence and machine Learning applications, AIMLA 2024

作者： Sharma, Mohit Kumar Menon, Soumya V Tripathy, Padmaja Vivekananda Global University Department of Electrical Engineering Jaipur India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India ARKA JAIN University Department of Mechnical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

This examination intends to enhance the overall performance of welding operations through picture processing. It's going to use an aggregate of PC vision and gadgets, getting to know to perceive better and tune welds, improve the accuracy of the system, and reduce the capability for mistakes. Particularly, the study will make use of a deep learning method to classify welds in specific classes, allowing the welding operations to be more effectively monitored and operated. Additionally, a convolutional neural network technique will be utilized to pick out the welds and estimate the vital parameters from the image statistics. Sooner or later, a robot arm geared up with a digital camera and a torch can be used to validate the welding process in an actual-world scenario. The effects of this take a look at will be used to enhance the performance and nice of welding operations through higher visibility into the system. © 2024 IEEE.

关键词： Welds

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A Comparative Study on Pruning Deep Convolutional Neural Networks Using Clustering Methods: K-Means, CLIQUE, DENCLUE, and OptiGrid 24

A Comparative Study on Pruning Deep Convolutional Neural Net...

引用

9th International conference on Multimedia and image processing (ICMIP)

作者： Alqemlas, Danah Saud Jeragh, Mohammad Esmaeel Kuwait Univ Kuwait Kuwait Kuwait Oil Co Kuwait Kuwait

ISBN: (纸本)9798400716164

In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks require large models, which is a challenge to deploy in devices with limited resources. These resource-constrained devices have limited computation power and memory. Hence, the neural networks must be optimized through network acceleration and compression techniques. This paper proposes a novel method to compress and accelerate neural networks from a small set of spatial convolution kernels. Firstly, a novel pruning algorithm is proposed based on the density-based clustering method that identifies and removes redundancy in CNNs while maintaining the accuracy and throughput tradeoff. Secondly, a novel pruning algorithm based on the grid-based clustering method is proposed to identify and remove redundancy in CNNs. The performance of the three pruning algorithms (density-based, grid-based, and partitional-based clustering algorithms) is evaluated against each other. The experiments were conducted using the deep CNN compression technique on the VGG-16 and ResNet models to achieve higher accuracy on image classification than the original model at a higher compression ratio and speedup.

关键词： Neural Network Pruning Clustering Methods image processing

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Research and Implementation of Material image Subject Segmentation Method Based on machine vision 5

Research and Implementation of Material Image Subject Segmen...

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5th International conference on Advances in Electrical Engineering and Computer applications, AEECA 2024

作者： Wang, Lu Qingdao University of Science and Technology Chinesisch-Deutsche Technische Fakultat Qingdao China

ISBN: (纸本)9798350355253

This article proposes a simple and effective method for image subject segmentation. Our research mainly focuses on the characteristics of material images in the experimental platform. Through in-depth research, we have found that using edge detection, morphological operations, and fill truncation can form an efficient subject segmentation technique. To verify the effectiveness of our proposed method, we conducted a series of experiments. The results indicate that our method only takes 0.238 seconds to process a single image. This speed is very impressive and demonstrates the potential application of this method on embedded devices. Our method is very suitable for embedded devices such as experimental benches. Embedded devices often have limited computing power and resources, thus requiring an efficient and lightweight subject segmentation method. Our proposed method precisely meets these requirements because it is simple and effective, and can quickly perform image subject segmentation in resource limited environments. In summary, our method has significant advantages in image subject segmentation problems. It is not only simple and easy to understand, but also efficient and fast, making it very suitable for use on embedded devices such as experimental benches. We believe that this method will have a positive impact on the field of image processing and bring more possibilities for related application areas. © 2024 IEEE.

关键词： machine vision

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