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

共 16440 条记录，以下是1811-1820 订阅

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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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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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FUSION OF VISIBLE image AND SAR image BASED ON CONVOLUTIONAL NETWORK 24

FUSION OF VISIBLE IMAGE AND SAR IMAGE BASED ON CONVOLUTIONAL...

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Shilan Liu Chinese Flight Test Establishment Xi'an Shaanxi China

ISBN: (纸本)9798400713880

With the need for image development, a single image can no longer comprehensively represent a video scene, and the development of fused images is imminent. image fusion refers to the process of extracting the most favorable information from each channel through the integrated processing of image processing and computer technology, and finally combining them into a high-quality image. At present, most of the image fusion techniques are mainly focused on visible and infrared images, and the research on synthetic aperture radar and visible light images is limited. In this paper, we focus on image fusion based on visible light and synthetic aperture radar images. First, this paper introduces the definition of image fusion and analyzes the image fusion potential of visible light image and synthetic aperture radar image. Second, the advantages and disadvantages of the two existing image fusion methods are summarized, and a convolutional neural network-based image fusion method is proposed to solve the existing problems. This method mainly processes the high-frequency part and low-frequency part of the image separately. The low-frequency part is mainly processed by linear combination, and the high-frequency part is fused by extracting the corresponding salient features based on VGG19 network. In order to compare with other image fusion algorithms, we conduct experimental tests using the OS dataset to obtain specific definitions of various evaluation metrics of fused images, and find that the proposed algorithm improves the evaluation metrics of fused images. Finally, we conclude this paper and present the remaining challenges in image fusion research and predict possible future research directions.

关键词： image fusion

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Coarse-fine Surrogate Model Driven Preference-based Multi-objective Evolutionary Fuzzy Clustering Algorithm for Color image Segmentation 3

Coarse-fine Surrogate Model Driven Preference-based Multi-ob...

引用

3rd International conference on Natural Language processing, ICNLP 2021

作者： Zhao, Feng Liu, Feifan Xi'An University of Posts and Telecommunications School of Communications and Information Engineering School of Artificial Intelligence Xi'an China

ISBN: (纸本)9781665414111

When applied to image segmentation, most existing multi-objective evolutionary clustering algorithms usually consider the information at the pixel level. Sometimes ignoring the region information of the image may lead to unideal segmentation performance. Moreover, these multi-objective evolutionary clustering algorithms require expensive computation for evaluating fitness functions. In order to improve the segmentation performance and time efficiency for image segmentation, a coarse-fine surrogate model driven preference-based multi-objective evolutionary fuzzy clustering (CFS-PMEFC) algorithm is proposed in this paper. First of all, the coarse-fine surrogate model and the preference information derived from the user are introduced to design multi-objective evolutionary clustering framework, which can improve the evolutionary efficiency and quality of clustering centers. Then, Mahalanobis distance-based fitness functions fusing the region information of the image are constructed to further promote the segmentation performance. In addition, a clustering index with the region information of the image is designed to select the optimal solution from the final solution set of CFS-PMEFC. Experiments on Berkeley images show that CFS-PMEFC greatly improves the accuracy of image segmentation and meanwhile reduces the computational cost. © 2021 IEEE.

关键词： image segmentation

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Identifying Recaptured images Using Deep Hybrid Correlation Network 14

Identifying Recaptured Images Using Deep Hybrid Correlation ...

引用

14th International conference on Digital image processing, ICDIP 2022

作者： Zhu, Nan Liu, Zhiqin Guo, xiaolu Department of Electronic Information Engineering Xi'an Technological University Xi'an710021 China

ISBN: (纸本)9781510657564

With the explosive advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes more and more easy. Such recaptured images can not only be used for deceiving intelligent recognition systems but also for hiding tampering traces. In order to prevent such a security loophole, we propose a recaptured image detection approach based on deep hybrid correlation network. Specifically, we first design a deep hybrid correlation module to extract the correlations in different color channels and neighboring pixels. This module has three different branches, in which a 1×1 convolution layer is used to learn the correlations between color channels while two consecutive convolution sub-modules are used to extract the correlations between neighboring pixels. Then we feed the output of this module into consecutive convolution modules to further learn the hierarchical representation for make decision. Ablation experiments verify the effectiveness of our proposed deep hybrid correlation module, while single database experiments demonstrate that our proposed method can achieve average accuracy with about 99% on three public databases. Specifically, our method not only performs very close to the state-of-the-art methods on the most difficult-to-detect ICL-COMMSP database and the relative low-quality NTU-ROSE database, but also improves the performance on the most diverse Dartmouth database obviously, which verifies the effectiveness of the proposed deep architecture. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method. © 2022 SPIE.

关键词： Convolution

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Face Detection on Thermal Infrared images Combined with Visible images 16th

Face Detection on Thermal Infrared Images Combined with Visi...

引用

16th International conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Chen, Yujia Wang, Liqing Xu, Guangda School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Infrared Division Wuhan Huazhong Numerical Control Co. Ltd. Wuhan China National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789811912528

Due to COVID-19, intelligent thermal imagers are widely used all over the world. Since intelligent thermal imagers usually require real-time temperature measurement, it is significant to find a method to quickly and accurately detect human faces in thermal infrared images. This paper mainly proposes two different methods. One is to use image processing methods and face detected from visible images to determine the position of the face in the infrared image, while the other is to use target detection algorithms on infrared images, including YOLOv3 and Faster R-CNN. This paper uses the two methods on a self-collected dataset containing 944 pairs of visible and infrared images and observes the robustness of methods by adding random noise to images. Experiments show that the first one has much lower latency and the latter one has higher accuracy in both cases. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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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...

引用

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...

引用

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

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

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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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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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