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

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Automatic Sown Field Detection Using machine vision and Contour Analysis 21st

Automatic Sown Field Detection Using Machine Vision and Cont...

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21st International conference on Computational Science and Its applications (ICCSA)

作者： Shirobokov, Mikhail Grishkin, Valeriy Kayumova, Diana St Petersburg State Univ Univ Embankment 7-9 St Petersburg 199034 Russia

ISBN: (纸本)9783030869601;9783030869595

The paper proposes a prototype of an algorithm based on the use of machine vision methods, which allows automatic identification and selection of fields sown with agricultural crops on images. The algorithm works with satellite images and consists of two stages. At the first stage, the image undergoes initial processing, after which edge detection and contour finding algorithms are applied to it. At the second stage, the obtained image areas enclosed within the contours are represented as a set of numerical and logical parameters which are used for filtering and classification of the areas.

关键词： image processing machine vision Contour analysis

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vision Transformers-based Hand Gesture Classification 3

Vision Transformers-based Hand Gesture Classification

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3rd International Informatics and Software Engineering conference, IISEC 2022

作者： Al-Zebari, Adel Omar, Naaman Sengur, Abdulkadir Duhok Polytechnic University Department of Information Technology Duhok Iraq Firat University Department of Electrical-Electronics Engineering Elazig Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Hand gesture recognition (HGR) is a hot topic in machine learning and image processing communities. HGR is also vital for some Human-Computer Interaction (HCI) applications. Up to now, traditional machine learning approaches and deep convolutional neural networks (CNN) have been applied to HGR. Although these methods perform well enough on HGR, in this paper, we used a recent model namely vision transformer (viT) on HGR. viT is developed for improving the performance of CNN. viT has a similar architecture to CNN but it has also different layers for the classification task. We used the viT in a transfer learning fashion and applied it to the NTU hand gesture dataset. A holdout cross-validation test approach is considered in experiments and classification accuracy is used as a performance measure. The experimental works produce a 96.4% accuracy score and a comparative study with CNN models is carried out. Results show that the proposed model has potential in HGR. © 2022 IEEE.

关键词： Convolutional neural networks

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Product Digital Quality Inspection using machine vision Systems - A Categorical Review 17

Product Digital Quality Inspection using Machine Vision Syst...

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17th Annual System of Systems Engineering conference, SOSE 2022

作者： Reyna, Mark Delgado, Guadalupe Akundi, Aditya Luna, Sergio Chumacero, Erik University of Texas Rio Grande Valley Complex Engineering Systems Laboratory BrownsvilleTX United States University of Texas Rio Grande Valley Dept. of Manufacturing and Industrial Engineering EdinburgTX United States University of Texas Rio Grande Valley Dept. of Informatics and Engineering Systems BrownsvilleTX United States University of Texas at El Paso El Industrial Manufacturing and Systems Engineering Dept El PasoTX United States

ISBN: (纸本)9781665496230

A key element of quality control in manufacturing is Product Inspection, which is a process that allows for verifying a product's quality enabled by activities such as measuring, examining, and testing one or more characteristics of a product. Automated machine vision systems undertake this task for in-line, in-process inspections. machine vision (MV) technology is designed to imitate the human visual system. The ability for computers to see the world through cameras and analyze their findings autonomously, often through deep learning, is making MV a market-disrupting technology. Many manufacturers seek to adopt this technology to improve production rates, product quality, and lower costs. The focus of this paper is to review the MV systems being developed to handle quality control, specifically product inspection tasks for various manufacturing applications. We present recently developed MV systems that perform product inspection based on free-form analysis, dimensional analysis, and surface analysis. Further, a discussion is provided to address the commonalities found in MV systems and the challenges the technologies continue to face. Additionally, each product quality inspection technique is examined for image processing techniques used. © 2022 IEEE.

关键词： Computer vision

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MFA-DAF: Unsupervised Multimodal Medical image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

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

作者： Xinyu Xie Xiaozhi Zhang Dongping Xiong Lijun Ouyang School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality.

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One Shot Face image Style Transfer Method Based on GAN

One Shot Face Image Style Transfer Method Based on GAN

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

作者： Wenyin He Department of Computer Science Wuhan University Wuhan Hubei China

Style transformation on face images has traditionally been a popular research area in the field of computer vision, and its applications are quite extensive. Currently, the more mainstream schemes include Generative Adversarial Network (GAN)-based image generation as well as style transformation and Stable diffusion method. In 2019, the NviDIA team proposed StyleGAN, which is a relatively mature scheme for generating real faces as well as face feature blending. The whole StyleGAN model is trained based on the Flickr-Faces-HQ Dataset (FFHQ) dataset, the This is a large dataset, so the model takes a long time to train. My aim is to form a One-shot stylized face image generator, which means that only one reference face and one stylized face need to be input, and a brand-new face with a mixture of features can be generated in a short training time. This is inspired by the existing research result JoJoGAN, which learns a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial paired dataset from a single example of the style. This paper will make improvements to JoJoGAN, including improving the encoder that utilizes the GAN Inversion method to generate latent codes for image features, and the random mixing of latent codes to produce a more refined paired dataset.

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Unveiling the vision: A Comprehensive Review of Computer vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International conference on

作者： Meena Laad Ratan Maurya Najeeb Saiyed Dept. of Applied Science Symbiosis Institute of Technology Pune Maharashtra Dept. of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Maharashtra

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In the era of rapid technological advancement, Computer vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring.

关键词： Deep learning Computer vision Reviews Smart cities Surveillance Precision medicine Urban planning

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Indoor Smart Walker Based on Artificial vision for the Autonomous Movement of Seniors with Mobility Problems and Reduced vision 2nd

Indoor Smart Walker Based on Artificial Vision for the Auton...

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2nd International conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Guamushig-Laica, Jose Jerez-Gavilanez, Miguel Mendoza-Chipantasi, Dario Marino-Salguero, Jessica Univ Fuerzas Armadas ESPE Quijano & Ordonez Latacunga 050102 Ecuador Tech Univ Darmstadt Dolivostr 15 D-64293 Darmstadt Germany

ISBN: (纸本)9783030991708;9783030991692

The usual solution to mobility problems for the elderly lies in the use of conventional canes and walkers. However, when the older adult also has vision problems, these devices are not sufficient to provide safe walking, and the senior suffers from constant falls. This article proposes a smart walker based on artificial vision for indoor use in nursing homes that safely enables autonomous mobility for visually impaired seniors. The walker has been equipped with activation switches on the handlebars, ultrasonic sensors, a Kinect visual sensor, traction motors, and a controller. The control algorithm is based on reactive navigation, odometry and artificial vision and is designed to avoid obstacles and detect dangerous situations such as stairs. The control unit is a Raspberry Pi running Raspbian and relies on message passing between processes and visualization. The prototype of the smart walker is implemented in the nursing home "Luis Maldonado Tamayo" (Pujili-Ecuador), where tests are being carried out with elderly persons who still have motor skills and some partial vision impairment. The walker can effectively detect obstacles in a distance range from 50 - 86 cm. Almost 90% of small, 96% medium, and 98% dynamics obstacles are avoided. The demonstration video is available at https://***/MJzvzIily4k.

关键词： Intelligent walker Senior - mobility machine vision image processing Mobile robotics

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C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using vision Transformer Networks

C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Visi...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Sairam VC Rebbapragada Pranoy Panda vineeth N Balasubramanian Machine Learning and Vision Group IIT Hyderabad India Fujitsu AI Research India

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challenges, including small object sizes, distortion, occlusion, and real-time processing requirements. Current methods integrating multi-scale feature fusion and temporal information have limitations in handling extreme blur and minuscule objects. To address this, we propose a novel coarse-to-fine detection strategy based on vision transformers. We evaluate our approach on three challenging drone- to-drone detection datasets, achieving F1 score enhancements of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively. Additionally, we demonstrate real-time processing capabilities by deploying our model on an edge-computing device. Our code will be made publicly available.

关键词： Computer vision Codes image edge detection image representation Transformers Distortion Real-time systems

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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

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Algorithm, image processing and machine vision (AIPMV), International conference on

作者： Xiaolei Li Xingjie Xiao Siyuan Yang Moquan Sha Zongyao Sha Jianguang Tu School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications.

关键词： Measurement visualization Target tracking Trajectory tracking machine vision video surveillance Robustness Hardware image sequences Trajectory

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Lung Pathology Using Artificial Intelligence Analysis of Ultrasound images: A Survey

Lung Pathology Using Artificial Intelligence Analysis of Ult...

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Sustainable Technology and Engineering (i-COSTE), International conference on

作者： Ali Alwadi Girija Chetty Mohammad Alwadi Jawad Alnaimi Amjad Gawanmeh Khaled Zuhair Said Mahmoud Faculty of Science and Technology University of Canberra Canberra Australia Department of Computer Studies Arab Open University Amman Jordan College of Engineering & IT University of Dubai Dubai United Arab Emirates State University of New York at Binghamton Hillsboro Oregon United States of America

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

This paper first studies and analyses the Artificial Intelligence algorithms that can potentially be used to identify various diseases from medical images and scans. Then researches the related work in this area to find a suitable machine Learning model that can be used to identify Interstitial Syndrome in Lung Ultra Sound images. By researching similar applications of the latest machine Learning algorithms, this team of researchers are adopting a vision Transformer system developed on a Convolutional Neural Network-based algorithm. The later has proven to be efficient in the area of UltraSound image processing in general and more flexible to configure and parameterize in the training process. The vision Transformer system is a segmentation-based model that divides the input image to smaller subset of images of the same size. By merging the adjacent small patches into bigger layers, bigger windows to perform self-attention on, the system extracts the points of interest out of these frames.

关键词： Training Surveys image segmentation Computer vision Ultrasonic imaging machine learning algorithms Lungs Transformers Convolutional neural networks Diseases

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