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

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Hand Gesture-Based AI System for Accessing Windows applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

引用

2024 IEEE International conference on Signal processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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A machine vision Measurement Method for Large Plates Based on Reference Point Assistance 5

A Machine Vision Measurement Method for Large Plates Based o...

引用

5th International conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ji, Tianyi Zhao, Zhiwei Zhao, Ning Southeast Univ Coll Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350320428

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.

关键词： reference points size measurement machine vision image processing

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Picture processing Optimization Technology Based on Mask R-CNN Algorithm 3

Picture Processing Optimization Technology Based on Mask R-C...

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3rd International conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Tan, Guihua Liu, Yiran College of Information Engineering Loudi Xiaoxiang Vocational College Hunan Loudi417000 China

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. © 2023 The Authors. Published by Elsevier B.V.

关键词： image retrieval

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Multilevel Crop image Segmentation Using Firefly Algorithm and Recursive Minimum Cross Entropy 4th

Multilevel Crop Image Segmentation Using Firefly Algorithm a...

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4th International conference on machine Intelligence and Signal processing, MISP 2022

作者： Kumar, Arun Kumar, A. vishwakarma, Amit PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9789819900466

image segmentation plays an important role in computer vision technology and agriculture is one of their applications. The crop images present near the vicinity are complex and dense. Hence, multilevel thresholding of such crop images is a tedious task. In this paper, we propose multilevel thresholding of crop images using recursive minimum cross entropy and firefly algorithm. The firefly is based on the social behavior of the swarm of fireflies and bioluminescent information-sharing phenomena. It is a swarm-based algorithm, which offers a better search mechanism to find the optimum threshold value. The performance of the proposed method is observed over ten complex background crop images and compared with the wind-driven optimization algorithm. The better fidelity parameters evidence the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Enhancing Human-Computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV 3

Enhancing Human-Computer Interaction: Hand Detection for Air...

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3rd International conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shukla, Pranjal Das, Prasenjit Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

In recent years, there has been a remarkable increase in interest and challenges in image processing and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf. © 2023 IEEE.

关键词： Air Writing Computer vision Digital sketching Gesture Recognition Hand Detection Hand Tracking Interactive whiteboard

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

引用

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

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning image pattern recognition MobileNetV3-small Wheat imperfect grains

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Development of a fusion technique and an algorithm for merging images recorded in the IR and visible spectrum in dust and fog 19

Development of a fusion technique and an algorithm for mergi...

引用

conference on Electro-Optical and Infrared Systems - Technology and applications XIX

作者： Semenishchev, Evgeny Zelensky, Aleksandr Alepko, Andrey Zhdanova, Marina Voronin, viacheslav Ilyukhin, Yury Tula State Univ TulSU Lab Cognit Technol & Simulat Syst 92 Sq Lenina Tula 300012 Tula Russia Moscow State Tech Univ STANKIN Ctr Cognit Technol & Machine Vis 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510655461

The article proposes a fusion technique and an algorithm for combining images recorded in the IR and visible spectrum in relation to the problem of processing products by robotic complexes in dust and fog. Primary data processing is based on the use of a multi-criteria processing with complex data analysis and cross-change of the filtration coefficient for different types of data. The search for base points is based on the application of the technique of reducing the range of clusters (image simplification) and searching for transition boundaries using the approach of determining the slope of the function in local areas. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors with a resolution of 1024x768 (8 bit, color image, visible range) and 640x480 (8 bit, color, IR image) are used. images of simple shapes are used as analyzed objects.

关键词： image fusion machine vision preprocessing IR noise robotic complexes

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Advancements in Road Surface Classification: A Comprehensive Review of Computer vision and IoT Approaches

Advancements in Road Surface Classification: A Comprehensive...

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International conference on Computer vision and Internet of Things 2023, ICCvioT 2023

作者： Kovilpillai, Judeson Antony J. Dhar, Soumi Singh, Uday Kumar Prakash, S. Saidala, Ravi Kumar Jayanthy, S. Department of CSE Alliance University Anekal Bangalore India Department of Research and Innovation Alliance University Anekal Bangalore India Department of ECE Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9781839539848

The rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by investigating diverse methodologies within the realms of computer vision and the Internet of Things (IoT). Through an extensive investigation, various techniques encompassing image processing, machine Learning(ML), Deep Learning(DL), and IoT are examined for their effectiveness in classifying road surfaces at different terrains. Moreover, this article also reviews datasets, signals, sensors, communication protocols, IoT implementation strategies, pre-processing methodologies, and feature extraction techniques. This investigation delves into novel approaches aimed at resolving road surface classification challenges, meticulously examining their respective strengths and limitations. Furthermore, this article includes a comparative analysis of these advanced methodologies, facilitating the identification of the most suitable model for the task. The assessment takes into account intricate methodological aspects, types of sensors/cameras, dataset variations, and performance metrics, thereby providing valuable insights into the landscape of road surface classification for autonomous driving applications. © The Institution of Engineering & Technology 2023.

关键词： Internet of things

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A Review of machine vision applications in Aerospace Manufacturing Quality Inspection

A Review of Machine Vision Applications in Aerospace Manufac...

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Computer, Control and Robotics (ICCCR), International conference on

作者： Li Deng Gang Liu Yilian Zhang School of Mechanical and Automotive Engineering Shanghai University Of Engineering Science Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. This paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Drilling Technological innovation Reviews machine vision Inspection Market research Sensor systems

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