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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

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A UAV-Based Remote Sensing image Automatic Monitoring System Empowered by Artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Tao Liu Bohan Zhang College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing, feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing speed, anti-interference ability and accuracy. Especially in the processing of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing capability, and extension to practical applications such as disaster monitoring and environmental protection.

关键词： Support vector machines Accuracy Disasters Computer architecture Real-time systems Data models Convolutional neural networks Protection Remote sensing Monitoring

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Robust Point Tracking Based On image Matching And Machine Learning On Video images Taken from Fast Moving Camera

Robust Point Tracking Based On Image Matching And Machine Le...

引用

IEEE Signal processing and Communications Applications (SIU)

作者： Ali Güven İmam Şamil Yetik Elektrik ve Elektronik Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi Ankara

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.

关键词： Target tracking Machine learning algorithms image resolution Navigation image matching Signal processing algorithms Feature extraction

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Exploring Google Earth Engine Platform for Satellite image Classification Using Machine Learning algorithms 8th

Exploring Google Earth Engine Platform for Satellite Image C...

引用

8th International conference on Smart City Applications, SCA 2023

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Laboratory of Information Technology and Modeling LTIM Faculty of Sciences Ben M’sik Hassan II University Casablanca Morocco Polydisciplinary Faculty of Sidi Bennour Chouaib Doukkali University El Jadida Morocco

ISBN: (纸本)9783031543753

Google Earth Engine is a geospatial data processing platform that runs in the cloud. It offers free access to massive amounts of satellite data as well as unlimited computing power to monitor, visualize, and analyze environmental features on petabyte scale. The capability of this platform to support diverse approaches for land use and land cover (LULC) classification using both pixel based and object-oriented methods has been made possible through the provision of a variety of machine learning algorithms. Earth observation data have proven to be a valuable resource of quantitative information more consistent in time and space than traditional ground surveys. They offer numerous opportunities for mapping and monitoring urban areas, as well as a variety of physical, climatic, and socioeconomic data to support urban planning and decision-making. We used Landsat 8 satellite data to perform supervised classification in this paper, and we used three advanced machine learning methods Support Vector Machine (SVM), Random Forest (RF), and Minimum Distance (MD) to classify water areas, built-up areas, cultivated areas, sandy areas, barren areas, and forest areas on Moroccan territory. The classification results are displayed using a set of accuracy indicators that includes overall accuracy (OA) and the Kappa coefficient. We obtained 0.93 as a better accuracy for the MD algorithm, however, the worst accuracy result is 0.74 for the SVM algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Engines

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Infrared image Target Detection System Based on RV1126 7

Infrared Image Target Detection System Based on RV1126

引用

7th Asia Pacific conference on Optics Manufacture (APCOM)

作者： Zhu, Jiayi Zhou, Yi Jiang, Daozhong Wang, Hongtao Han, Rui Yin, Tong Li, Shu Jiangsu North Huguang Optoelect Co Ltd 25 ShengAnLu Wuxi 214035 Jiangsu Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

The computing power of the image processor of the handheld viewing system is usually low, which brings some difficulties to the image processing. In this article, an infrared image target detection system is built with the RV1126 development board as the core. Compared with visible light, infrared image has the characteristics of low resolution and blurred details of small targets. According to the above characteristics, conventional image processing algorithms are difficult to deploy to embedded infrared image target detection systems. Therefore, this article uses SSD neural network to train the infrared target detection model, and converts the model into an infrared target detection model that can be deployed on RV1126 development board through Rknn. The actual test shows the SSD target detection network can achieve intelligent target detection and recognition on the RV1126-based embedded platform in the infrared image target detection.

关键词： RV1126 Infrared image Target detection SSD

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GLCM Based Feature Extraction and Medical X-ray image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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A Survey on Privacy Preserving Content Based image Retrieval and Information Sharing in Cloud Environment 4

A Survey on Privacy Preserving Content Based Image Retrieval...

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4th International conference on Recent Trends in Computer Science and Technology, ICRTCST 2021

作者： Chouragade, Pushpanjali M. Ambhore, Premchand B. Government College Of Engineering Amravati Computer Science & Engineering MS Amravati India

ISBN: (纸本)9781665466332

As the quantity of images has expanded significantly, commonly utilized Content Based image Retrieval algorithms are commonly utilized in our ordinary routine. When it comes to processing and storing information, image retrieval systems are generally highly expensive. As a result, image owners should consider outsourcing these functions to a cloud server. However, since the cloud server can only be trusted to a certain amount, image owners' confidentiality might be jeopardized. This situation gives rise to the need of an algorithm/method which makes the images retrieval secure. For addressing this issue, the effort will be done to propose the Secure Content Based image Retrieval method with less complexity, high speed of operation, high accuracy and high retrieval efficiency. For this, various works have been studied to find best approach for achieving the efficient retrieval scheme. © 2022 IEEE.

关键词： Search engines

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Template for Preparation of Papers for IEEE Sponsored conferences & Symposia 25

Template for Preparation of Papers for IEEE Sponsored Confer...

引用

IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Jarofka, Maximilian Sieberg, Philipp M. Hurten, Christian Benedens, Tim Peters, Ricarda Kracht, Frederic E. Schramm, Dieter NIST Boulder CO 80305 USA Rice Univ Houston TX 77005 USA Colorado State Univ Dept Phys Ft Collins CO 80523 USA Univ Colorado Elect Engn Dept Boulder CO 80309 USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This paper describes the integration of publicly available geoinformation into a simulation environment. Within the research project `AutoBin', the autonomous control of an inland vessel is to be tested in a simulated environment and the developed algorithms are to be validated. This requires a photorealistic simulation within a modern game engine. In this paper, the different sources of the used geodata are presented. Furthermore, the necessary adaptations of the data for the desired integration into the simulation are shown. At the end of the integration of the different data sources, an environment model of the entire test field in the project `AutoBin` - a part of the Dortmund-Ems-Canal from the lock Waltrop to the port of Dortmund - is created. The last section of this paper describes the necessary adaptations to integrate the environment model into the simulation infrastructure of the research and control center for autonomous inland vessels `VeLABi'.

关键词： Adaptation models Solid modeling Three-dimensional displays Soft sensors Stereo image processing Games Seaports

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Implementation of Intelligent Robotic systems for Pick and Place based on image processing

Implementation of Intelligent Robotic Systems for Pick and P...

引用

Asian Control conference

作者： Sang-Won Kang Tae-Wook Um Nam-Jin Park Hyo-Sung Ahn School of Mechanical Engineering Gwangju Institute of Science and Technology Gwangju Korea

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper introduces an intelligent delta robot system enhanced with image processing to optimize Pick & Place operations in Agricultural Produce Centers (APCs). Facing a critical demand for mechanization in agriculture due to a shrinking rural workforce, our system utilizes camera-based segmentation and depth estimation to efficiently automate the packaging of fruits and vegetables. It concentrates on essential tasks such as precise gripping and ungripping, supported by advanced camera-based visual sensors. Integrating these vision technologies with delta robot kinematics and specialized image processing algorithms allows the robot to execute highly accurate movements. Our implementation showcases significant enhancements in the efficiency and reliability of APC operations, advancing the field of agricultural robotic automation and establishing a new standard for future developments in automated food production.

关键词： image segmentation Visualization Automation Estimation Packaging Streaming media Robot sensing systems

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A novel approximation scheme for floating-point square root and inverse square root for FPGAs 11

A novel approximation scheme for floating-point square root ...

引用

11th International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Pennestri, Pietro Huang, Yanqiu Alachiotis, Nikolaos Univ Twente Fac EEMCS NL-7522 NB Enschede Netherlands

ISBN: (纸本)9781665467179

Jointly computing the square root (SQRT) and the inverse square root (ISQRT) of floating-point numbers is common in many algorithms, e.g., in image or time series data processing when computing norms or vector normalization. Existing designs suffer from high latency and inefficient resource utilization due to the separate architectures that carry out these two operations. In this paper, we first propose a non-iterative approximation method for computing SQRT and ISQRT based on the Chebyshev min-max criterion to reduce the latency while meeting the accuracy requirements of various applications;thereafter a shared architecture of these two operations is designed and implemented in FPGA with less logic units. In contrast with other approximation solutions, our method does not need to perform any iterations and the accuracy can be mathematically estimated. A comparison with vendor-provided IP cores for FPGAs revealed that our proposed SQRT/ISQRT floating-point IP core utilizes less resources while reducing the clock-cycle latency by nearly four times.

关键词： Time-frequency analysis Time series analysis Computer architecture Chebyshev approximation Data processing Cyclones IP networks

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Detection of Leaf Diseases Based on Ensemble Convolutional Neural Networks 4

Detection of Leaf Diseases Based on Ensemble Convolutional N...

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4th IEEE International conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Chithra, P.L. Pushparani, S. Janes University of Madras Department of Computer Science Tamil Nadu Chennai India Ethiraj College for Women Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350306118

The appearance of infections in plantations is the most common cause of crop losses in a variety of crops. Due to lower product quality and a narrower offering of items, these usually result in reduced income for farmers and higher pricing for consumers. Infection symptoms are mostly evident in plant foliage, hence they are widely used to detect and identify diseases. Combined with computational intelligence or soft computing approaches, image-processing tools can help farmers diagnose diseases more effectively. The disease in plants can be identified using traits gleaned from its symptoms. A strategy using an ensemble approach for providing more precise predictions by merging results through different machine learning algorithms. A group of models cooperating to address a shared issue. Ensemble learning uses the strength of a variety of techniques to overcome the particular weaknesses of each model, as opposed to depending just on one model to provide the optimal answer. The set ought to be less error-prone than a single model. Combining many iterations of a projected model is the process of bagging. Before being used, each model is independently trained using an average model. Bagging's objective is to reduce variance below that of any model. In this work, images of leaves comprising healthy class and diseases obtained from the Plant Village dataset are fed into three Convolutional Neural Network architectures namely LeNet, Inception, and VGG16 net achieve an accuracy of 95%, 92%, and 95% respectively and the ensemble model obtains an improvement of 3.5% matched to the standard method. © 2023 IEEE.

关键词： image enhancement

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