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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61-70 订阅

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On Comparing Multiple Approaches for Classifying Vessels in imagery 6

On Comparing Multiple Approaches for Classifying Vessels in ...

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conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications VI

作者： Warner, John G. Patel, Vishal M. US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781510674219;9781510674202

Deep learning-based approaches, such as Convolutional Neural Nets (CNNs), have shown high performance in classifying contents of images. CNNs, however, have the notable drawbacks of potentially high computing costs, poor explainability, and wide performance variance if the underlying imagery data deviates from the training baseline. As advanced image processing capabilities are matured, the on-board detection of objects in space-based imagery is increasingly proposed. On-board satellite processing applications, which may be resource-limited, can drive the need for simpler models that reduce the necessary computing burden for edge computing applications. This raises the question of how well classic computer vision techniques can compete with more modern approaches. This paper characterizes and compares the performance of multiple computer vision models for the application of distinguishing maritime vessels from typical clutter in commercial electrooptical (EO) satellite imagery. A Support Vector machine (SVM) model using manually curated features is compared to multiple DL-based models spanning a range of model sizes, with the goal of determining whether classical approaches can compete favorably with DL when computational resources are taken into consideration. Differences in performance and processing resources are characterized between the approaches. Findings include that the SVM-based model may approach the accuracy of some CNN-based models for classifying images of clouds in satellite EO imagery for smaller DL-based models. However, even the smallest DL-based models, which take about the same computational resources as the SVM-based model, generally out-perform the SVMbased model. This finding may have implications for the operational use of on-board processing techniques for satellite payloads.

关键词： CNN spacecraft onboard processing Automatic Target Recognition maritime

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LeCA: In-Sensor Learned Compressive Acquisition for Efficient machine vision on the Edge 23

LeCA: In-Sensor Learned Compressive Acquisition for Efficien...

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50th Annual International Symposium on Computer Architecture (ISCA)

作者： Ma, Tianrui Boloor, Adith Jagadish Yang, Xiangxing Cao, Weidong Williams, Patrick Sun, Nan Chakrabarti, Ayan Zhang, Xuan Washington Univ St Louis MO 63110 USA Univ Texas Austin Austin TX USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400700958

With the rapid advances of deep learning-based computer vision (CV) technology, digital images are increasingly consumed, not by humans, but by downstream CV algorithms. However, capturing high-fidelity and high-resolution images is energy-intensive. It not only dominates the energy consumption of the sensor itself (i.e. in low-power edge devices), but also contributes to significant memory burdens and performance bottlenecks in the later storage, processing, and communication stages. In this paper, we systematically explore a new paradigm of in-sensor processing, termed "learned compressive acquisition" (LeCA). Targeting machine vision applications on the edge, the LeCA framework exploits the joint learning of a sensor autoencoder structure with the downstream CV algorithms to effectively compress the original image into low-dimensional features with adaptive bit depth. We employ column-parallel analog-domain processing directly inside the image sensor to perform the compressive encoding of the raw image, resulting in meaningful hardware savings, and energy efficiency improvements. Evaluated within a modern machine vision processing pipeline, LeCA achieves 4x, 6x, and 8x compression ratios prior to any digital compression, with minimal accuracy loss of 0.97%, 0.98%, and 2.01% on imageNet, outperforming existing methods. Compared with the conventional full-resolution image sensor and the state-of-the-art compressive sensing sensor, our LeCA sensor is 6.3x and 2.2x more energy-efficient while reaching a 2x higher compression ratio.

关键词： CMOS image sensor image compression autoencoder

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Research on Unmarked Motion Action Recognition Technology Based on Computer vision 5

Research on Unmarked Motion Action Recognition Technology Ba...

引用

5th Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2024

作者： Zhao, Lun Shandong Vocational College of Science and Technology Weifang China

ISBN: (纸本)9798350374407

The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for specific sports or target actions based on computer vision image data input, the aim is to provide references for the application of unmarked motion capture technology in the field of sports motion recognition. The research employed a literature review methodology, conducting searches in six databases, namely Web of Science, PubMed, Scopus, Google Scholar, IEEE Xplore, and China National Knowledge Infrastructure (CNKI), covering publications from January 2000 to June 2020. Through boolean logic operations on the retrieved literature, key information such as first author/publication year, types/targets of motion, participant information, camera parameters, image feature extraction techniques, action recognition algorithms, evaluation methods for action recognition quality, training and validation methods for image data, and performance metrics for action recognition were extracted. After screening, a total of 23 articles were included in the study. The findings revealed that $39 \%$ of the studies utilized machine learning algorithms based on support vector machines, while $35 \%$ employed deep learning algorithms based on convolutional neural networks. Commonly used evaluation metrics for action recognition quality included classification accuracy, confusion matrix, and displacement error. The development of computer vision motion capture, models, and algorithms demonstrated promising applications in areas such as action technique recognition and sports performance analysis. Traditional machine learning algorithms like support vector machines and principal component analysis remain dominant in action recognition technology;however, in certain scenarios, the performance of deep learning algorithms surpassed that of traditional machine learning methods.

关键词： Motion capture

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Engineering applications based on Industrial Robots and machine vision

Engineering Applications based on Industrial Robots and Mach...

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International conference on image processing, Computer vision and machine Learning (ICICML)

作者： Huang, Yiren Hao, Weidong Wang, Zhihao Wu, Hao Guilin Univ Elect Technol Sch Mech & Elect Engn Guilin Guangxi Peoples R China

ISBN: (纸本)9781665464680

This paper introduces the structure and operation mode of automatic production line based on the actual situation of laser quenching automatic production line of tool in enterprises. Robot vision integrates workpiece positioning coordinates with robot coordinates to realize the positioning and grasping function of robot through machine vision. Focus on OpenCV image processing methods. This paper describes its principle and possible problems from the aspects of system structure, robot coordinate calibration, visual identification and positioning and software design.

关键词： Robot machine vision workpiece positioning automation

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Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI based Computer vision applications 3

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shekhar, Sudhanshu Sathwik, T.S. Pritwani, Mayank Mohana Ramakanth Kumar, P. Sreelakshmi, K. RV College of Engineering® Bengaluru India

ISBN: (纸本)9798331527549

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications. © 2025 IEEE.

关键词： Adversarial machine learning

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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...

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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 ...

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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...

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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...

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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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