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

共 3293 条记录，以下是31-40 订阅

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vision-Based Assistive Systems for visually Impaired People: A Review 5th

Vision-Based Assistive Systems for Visually Impaired People:...

引用

5th International conference on Optical and Wireless Technologies (OWT)

作者： Mandia, Sandeep Kumar, Ashok Verma, Karan Deegwal, Jitendra Kumar Govt Mahila Engn Coll Dept Elect & Commun Engn Ajmer 305002 India Natl Inst Technol Dept Comp Sci & Engn Delhi 110040 India Govt Engn Coll Dept Elect Instrumentat & Control Engn Ajmer 305025 India

ISBN: (纸本)9789811916458;9789811916441

This research reviews the current state of vision-based assistive solutions for the visually impaired (vi). The paper focuses primarily on camera-based assistive system solutions. We focused the review on vision-based assistive solutions proposed for vi people. The sensors, image processing algorithms, and wireless communication protocols employed in the survey have been summarised. Acoustic output devices were used in addition to cameras, Radio Frequency Identification (RFID), and Global Positioning System (GPS). vision-based assistive solutions have evolved from traditional image processing techniques to machine learning to deep learning for assistance for vi users. Wi-Fi and Bluetooth devices are the most common wireless technologies used by vision-based assistive systems. The literature does not adequately leverage the optimization of deep learning models for edge devices.

关键词： visually impaired people vision-based assistive systems Wireless technologies

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Style-Driven image Enhancement for Entry-Level Mobile Devices 7

Style-Driven Image Enhancement for Entry-Level Mobile Device...

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7th International conference on machine vision and applications (ICMVA)

作者： Christian Matias, Angelo Patrick Del Gallego, Neil De La Salle Univ Manila Philippines De La Salle Univ Graph Animat Multimedia & Entertainment GAME Lab Manila Philippines

ISBN: (纸本)9798400716553

Modern smartphones usually have automatic camera adjustment features that predetermine how images will be processed. Without an intervention from the user (e.g., manual adjustment of exposure settings, addition/removal of certain image filters), the predetermined camera settings dictate the look and feel of images taken. Since higher-end mobile devices tend to gravitate towards a more visually appealing style and clearer images, image enhancement on entry-level devices could be performed by transferring the style from a higher-end device to a lower-end one. This paper proposes a learning-based, style-driven image enhancement for entry-level devices. Using a deep residual style transfer network, we train a model that learns the relationship between images taken from a high-end device and those taken from an entry-level device to create a filter that could be used to enhance the images captured from an entry-level device. Our quantitative and qualitative analyses show that our proposed method can enhance images to match the qualities produced by higher-end mobile device cameras.

关键词： image processing image enhancement super-resolution computational photography mobile devices

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Advanced machine vision techniques for real-time quality detection and grading of navel oranges using high-resolution imaging and deep learning algorithms

Advanced machine vision techniques for real-time quality det...

引用

International conference on Computer vision, Robotics, and Automation Engineering, CRAE 2024

作者： Zhang, Xiaoyan Beijing Institute of Technology Zhuhai Zhuhai519000 China

ISBN: (纸本)9781510682283

In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This review explores the latest advancements in machine vision technologies for navel orange quality assessment, focusing on image processing, deep learning algorithms, and multispectral imaging. image processing techniques, fundamental to machine vision systems, enable the extraction of visual features such as color, shape, and texture, enhancing detection accuracy. Recent studies have demonstrated the efficacy of deep learning algorithms, particularly convolutional neural networks (CNNs), in achieving high-precision grading and real-time performance. Furthermore, multispectral and hyperspectral imaging technologies offer rich spectral information, facilitating more accurate quality detection and maturity assessment. Despite significant progress, challenges such as complex natural environments, lighting conditions, and the high cost of imaging equipment persist. Future research directions include integrating multi-source data fusion, developing efficient deep learning algorithms, and promoting cost-effective imaging technologies. Our proposed advanced algorithm employs the DAHENG machine vision experimental platform and MER-132-30UC camera for high-resolution image acquisition, with HALCON software for sophisticated image processing and real-time classification. This system enhances accuracy and efficiency in navel orange quality detection and grading, addressing key challenges in current agricultural practices. © 2024 SPIE.

关键词： Citrus fruits

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Research on Badminton Movement machine Learning Model Based on Computer vision Technology 2

Research on Badminton Movement Machine Learning Model Based ...

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

作者： Zong, Cheng Hohhot Vocational College Hohhot China

ISBN: (纸本)9798350360240

This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization. © 2024 IEEE.

关键词： Convolutional neural networks

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Attention Modules Improve image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

Attention Modules Improve Image-Level Anomaly Detection for ...

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IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： vieira E Silva, Andre Luiz Simties, Francisco Kowerko, Danny Schlosser, Tobias Battisti, Felipe Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Tech Univ Chemnitz Jr Professorship Media Comp Chemnitz Germany Univ Fed Rural Pernambuco Visual Comp Lab DC Recife PE Brazil

ISBN: (纸本)9798350318920;9798350318937

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

关键词： Algorithms Algorithms and algorithms applications formulations image recognition and understanding machine learning architectures Remote Sensing

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A Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture 4th

A Review of Pre-processing Techniques for Weed-Plant Detecti...

引用

4th International conference on Data, Engineering, and applications, IDEA 2022

作者： Sonawane, Sandip Patil, Nitin N. R. C. Patel Institute of Technology Shirpur India

ISBN: (纸本)9789819700363

Precision agriculture has recently gained significant importance in computer vision technologies. Various processes as a part of agricultural production cycle from planting to harvesting can be carried out automatically and effectively by using computer vision. The lack of publicly available image datasets is a major obstacle to the rapid design and assessment of computer vision based applications and also to machine learning algorithms which support these applications. To reduce this bottleneck, numerous image dataset collections have been discovered and made publicly available since 2015. In spite of this development, there is still a need to focus on survey of these datasets. Two most important concerns—choosing the right dataset and knowing how to pre-process and prepare the images in datasets, are considerably challenging task in every application. This review paper gives a thorough analysis of the public image datasets and numerous pre-processing techniques carried out in the field of precision agriculture. This thorough study can lead to development of suitable methods for improved quality and productivity of the crop along with proper weed management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Application of Computer 3D image vision Algorithm in Intelligent image Recognition System 5

Application of Computer 3D Image Vision Algorithm in Intelli...

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2023 5th International conference on Artificial Intelligence and Computer applications, ICAICA 2023

作者： Li, Yuan Yu, Xin Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350323313

In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision image processing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional image processing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded image processing. © 2023 IEEE.

关键词： image recognition

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SC-LSTM based Human Action Recognition

SC-LSTM based Human Action Recognition

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9th International conference on Signal and image processing (ICSIP)

作者： Kang, Naixin Yi, Pengfei Geng, Qingshu Dong, Jing Liu, Rui Wang, Ling Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China

ISBN: (纸本)9798350350920

Human Action Recognition is one of the most applied research directions in the field of Computer vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human Action Recognition machine Learning Computer vision Human-Computer Interaction Security

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Identification of Late Blight in Potato Leaves Using image processing and machine Learning 3rd

Identification of Late Blight in Potato Leaves Using Image P...

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3rd International conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Leepkaln, Renan Lemes de Re, Angelita Maria Wiggers, Kelly Lais Midwestern Parana State Univ Guarapuava Parana Brazil Fed Technol Univ Parana Pato Branco Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

Potato is a widely consumed food worldwide, and its productivity has increased due to new varieties and the use of technologies related to irrigation, nutrition, and soil preparation, among others. However, diseases such as late blight disease can often affect the crop, impacting many farmers around the world. As a way to help production, technology in agriculture is increasing. Among the various computational techniques that can be applied, those based on digital image processing associated with machine learning algorithms stand out, producing excellent results. This work aimed to develop a methodology for recognizing late blight disease in potato leaves using digital image processing techniques and machine learning algorithms. It was possible to obtain promising results. The experiments were carried out in a set of images from a public database containing images of healthy and unhealthy leaves (with late blight). We compare the performance of machine learning algorithms using feature vectors obtained with SIFT algorithm and RGB descriptors. The best performance was using the Decision Tree algorithm and SIFT vectors, with 99.24% of accuracy.

关键词： Automatic disease recognition Digital image machine Learning Computer vision

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Revolutionizing machine vision: Advanced Convolutional Strategies for Rapid image processing 13

Revolutionizing Machine Vision: Advanced Convolutional Strat...

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13th International conference of Information and Communication Technology, ICTech 2024

作者： Wu, Hanlei Pittsburgh Institute Sichuan University Chengdu China

ISBN: (纸本)9798350376258

This paper presents a comprehensive examination of innovative strategies aimed at enhancing machine vision technology, particularly in the context of energy efficiency and processing speed, critical factors for applications like facial recognition. The study focuses on three distinct approaches: an optimized two-dimensional convolution algorithm, a novel Field-Programmable Gate Array (FPGA) implementation, and advancements in multichannel meta-imagers. Firstly, the paper discusses an optimized algorithm for two-dimensional convolutions, a fundamental operation in machine vision. This advanced algorithm significantly reduces computational complexity. For instance, in executing a two-dimensional 3×3 cyclic convolution, the proposed method reduces the number of necessary multiplications from 81 to merely 13, offering a substantial improvement in efficiency. Secondly, the paper explores an innovative FPGA implementation of the two-dimensional convolution algorithm. This implementation is designed to minimize the use of shift registers, multipliers, and adders. As a result, it utilizes fewer Look-Up Tables (LUTs), leading to energy and time savings in executing the convolution process. The paper details the architecture of this FPGA-based approach and its implications for energy consumption and processing speed in machine vision applications. Finally, the paper introduces a novel technique called the Avg-Topk method, addressing a critical challenge in the pooling layer of convolutional neural networks. This method combines the benefits of average pooling with the advantages of max pooling, aiming to enhance the accuracy of the pooling layer without compromising on efficiency. The Avg-Topk method represents a significant step forward in optimizing the pooling process within machine vision systems. In summary, this paper delves into groundbreaking methods to improve the speed and energy efficiency of machine vision systems, offering valuable insights and potential solution

关键词： Shift registers

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