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

共 16439 条记录，以下是1691-1700 订阅

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Helmet Pose Measurement Method Based on Feature Learning 6

Helmet Pose Measurement Method Based on Feature Learning

引用

6th International conference on Information Communication and Signal processing, ICICSP 2023

作者： Fu, Gaobo Zhao, xinbo Zou, xiaochun Yan, Linjie Zhang, Baoshang School of Software Northwestern Polytechnical University Xi'an China Science and Technology on Electro-optic Control Laboratory Luoyang China School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350339994

In modern aviation systems, the helmet sight (HMS) assists the pilot to cooperate with the warplane to quickly complete the target tracking and aiming, and the helmet pose measurement is the core technology of the helmet sight. Since the helmet pose measurement technology involved in aviation safety, this technology is kept confidential at home and abroad, and there is a lack of open source helmet data set and high-precision helmet posture measurement algorithm. For this purpose, this paper produced the first helmet dataset, Helment 8K, and proposed the CSPDarkNet-Ht helmet pose measurement algorithm based on feature learning. The target is to form the helmet dataset image on the helmet model according to the unique feature points of the helmet model design. Helment 8K data set is made by building a helmet model with 3dmax and then rendering 8282 helmet images at different angles with targets. The CSPDarkNet-Ht algorithm divides the yaw angle and pitch angle of the helmet pose angle into two sub-tasks for classification and regression. Firstly, The algorithm uses CSPDarkNet to extract the low, medium and high semantic information of the target features for the backbone network. Then, different semantic feature layers are classified from different angles and regression of the highest semantic feature layer is performed to obtain the predicted pose angles. Finally, the loss function was optimized to include the cross-entropy loss of the five coarse and fine categories as the total loss. Moreover, the average absolute error of the angular prediction on the Helment 8K dataset is 0.5, and the prediction accuracy is 91.09% within 1 degree error, which is consistent with the accurate measurement of the helmet pose. © 2023 IEEE.

关键词： helmet pose measurement hybrid coarse-fine classification target

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Contrast Enhancement and ROI detection in satellite images Application to the environment and sustainable development

Contrast Enhancement and ROI detection in satellite images A...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Aicha Mehidi Malika Mimi Abdallah Mehidi Soraya Zehani Electrical Engineering Department Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Electrical Engineering Department Laboratory of Vision and Communication Systems Faculty of Sciences and Technology University of Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of image processing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.

关键词： Earth image segmentation Software algorithms Signal processing algorithms Satellite images Sustainable development Artificial intelligence

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A Comparative Review on Design and Development of Automation System for Grading of Onion

A Comparative Review on Design and Development of Automation...

引用

AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Rahul Suryawanshi Jayesh Jagtap Dnyaneshwari Shegokar Siddhesh Jaju Chandan M. N Avinash M. Badadhe Department of Automation and Robotics JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researchers because of the rising need for quality produce. Thus, the findings highlighted in this work show the progress and current approaches and methodologies designed for the design and implementation of automation systems for grading onions. This work outlines various methods such as image processing, gas sensor technology, Near Infra-Red (NIR) spectroscopy, X-ray imaging, Laser Doppler Vibrometry (LDV), and some artificial intelligence approaches that have been used in improving onion sorting systems. In addition, it reveals the drawbacks of these technologies, such as the realization of high image processing, the availability of small data sets, and the stability of the sortation systems. Based on the findings of this review, some recommendations for future research are suggested, such as the integration of multi-sensor systems and the enhancement of the efficiency of the automated onion grading systems using different types of algorithms.

关键词： Automation Reviews image processing X-ray lasers Laser stability Doppler effect Gas lasers Artificial intelligence X-ray imaging Sorting

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

引用

International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making Signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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Block-based Multi-scale Haze image Enhancement Method for Surveillance Application 6

Block-based Multi-scale Haze Image Enhancement Method for Su...

引用

conference on Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies VI

作者： Voronin, V. Zhdanova, M. Khamidullin, I Tokareva, O. Zelensky, A. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (纸本)9781510655546

images captured in surveillance systems suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The basic idea is to apply the frequency domain image enhancement approach for different image block scales. The parameter of transform coefficient enhancement for every block is driven through optimization of measure of enhancement. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. To test the performance of the proposed algorithm, the public database O-HAZE is used.

关键词： dehazing surveillance image enhancement block-rooting frequency domain

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Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholding for Color image Segmentation

Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholdi...

引用

Natural Language processing (ICNLP), International conference on

作者： Lan Rong Feng Danlin Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to make full use of the color information of the image and improve the accuracy of color image segmentation, this paper proposes an adaptive SLIC-based fuzzy intensity dissimilarity thresholding for color image segmentation, which does not need gray conversion. Firstly, the proposed algorithm adaptively selects the number of super-pixels through the sum of image information and image complexity, and uses SLIC technology to extract image super-pixels; Then, the median value of each channel pixel in each super-pixel block is used as the super-pixel value to calculate the super-pixel intensity information, and the super-pixel intensity histogram is counted; Finally, an intensity dissimilarity function based on IT2FS is constructed to search the optimal threshold. On Berkeley images and Weizmann images, the proposed algorithm is compared with the five related algorithms. The experiments show that the proposed algorithm has achieved good results in terms of visual effects and evaluation indicators, which proves the effectiveness of the algorithm.

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Integration of Deep Learning and Time Series Decomposition for Enhanced Short-Term Load Forecasting

Integration of Deep Learning and Time Series Decomposition f...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Zhiqiang xi xiaotong Ye Jibo Yu Department of Computer Science and Engineering Sichuan University of Science and Engineering Guang 'an China Department of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This study aims to improve the accuracy of short-term power load forecasting through deep learning to ensure the dynamic balance and stable operation of the power grid system. In response to the poor prediction accuracy of traditional serial combination models and the issue of data leakage in modal decomposition prediction models, this paper proposes a parallel combination model based on deep learning. The model integrates a self-developed time series decomposition (TD) module, Bidirectional Gated Recurrent Unit (BiGRU), Temporal Convolutional Network (TCN), and Multi-Head Self-Attention (MHSA). First, the time series decomposition module decomposes power load data into trend and periodic components to extract long-term and short-term features. These features are then fed into parallel BiGRU and TCN deep networks for processing. The multi-head self-attention mechanism is incorporated to further enhance the model’s expressive capabilities. Additionally, the model leverages the Beluga Whale Optimization (BWO) algorithm to optimize hyperparameters, fully exploiting the complex correlations within the data. Experimental results show that the proposed BWO-TD-BiGRU-TCN-MHSA deep learning model significantly outperforms baseline models in terms of MAPE, RMSE, MAE, and R 2 . This verifies the model's high accuracy and practicality in power load forecasting, providing reliable support for the planning and stable operation of power systems.

关键词： Deep learning Accuracy Load forecasting Time series analysis Predictive models Prediction algorithms Feature extraction Data models Whale optimization algorithms Load modeling

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Semantic Segmentation and image Quality Assessment of Anterior Segment images for Smartphones 14

Semantic Segmentation and Image Quality Assessment of Anteri...

引用

14th International conference on Graphics and image processing (ICGIP)

作者： Chen, Yuanhao Huang, Kun Fan, Wen Chen, Qiang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Med Univ Dept Ophthalmol Affiliated Hosp 1 Nanjing 210029 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Eye diseases have always been a threat to public health worldwide. Many people suffer from various eye diseases, but there are not enough skilled ophthalmologists to meet the demand for medical care. Thus, finding a method to perform ophthalmic examinations automatically and conveniently is necessary. Although many well-designed ophthalmic diagnosis systems have been proposed to diagnose ophthalmic disorders using artificial intelligence algorithms, they tend to depend on high-quality anterior segment images to perform appropriately. In order to capture high-quality anterior segment images simply with a smartphone, we proposed a system including a semantic segmentation model and an image quality assessment method for anterior segment images. Our proposed segmentation model, namely the multi-task anterior segment image semantic segmentation (MT-ASISS) model, has a designed multi-task learning network structure and achieves an accuracy of 92.63% in Dice and a processing speed of 138ms per frame on smartphones. Our anterior segment image quality assessment method, namely Mixed-Parameters Quality Assessment (MPQA) method, has an accuracy of 92.6% in mean average precision (mAP). The system can help reduce the demand for professional image collecting equipment, share the burden of choosing satisfactory images manually and improve the efficiency of acquiring anterior segment images.

关键词： Anterior Segment image Segmentation Multi-Task Learning image Quality Assessment Lightweight Network

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Spatial Grid Algorithm in Aviation Control Area Division

Spatial Grid Algorithm in Aviation Control Area Division

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Fangjun Wu Air Traffic Control and Navigation School Air Force Engineering University Xi'an Shaanxi China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The partition stage in the traditional algorithms has lacked spatial utilization (low) and high computational complexity. Therefore, it can bring a series of problems. Therefore, some researchers have tried to explore more spatially efficient algorithms, such as a spatial grid algorithm (SGA), for the partition stage in air traffic control area. The SGA (or algorithm) is used to partition the aviation control area in this paper. As mentioned previously the SGA can partition areas by their grids or subregions, and some related operation constraints are considered and integrated. The algorithm is evaluated based on complexity, flexibility, and spatial utilization, and compared with traditional algorithms. The experiment results show SGA have a significant advantage for the partition of air control area. The SGA can be efficiently executed, has a fast partitioning rate compared to traditional algorithms, and has a significant computational efficiency advantage. It can make spatial resources in a regular pattern in the aviation control area and improve spatial utilization.

关键词： Accuracy Scalability Education Information processing Regulation Real-time systems Partitioning algorithms Computational efficiency Air traffic control Optimization

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Precision alignment of off-Axis multi-reflection optical system based on PSM 8

Precision alignment of off-Axis multi-reflection optical sys...

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8th Asia Pacific conference on Optics Manufacture, APCOM 2023 and 3rd International Forum of Young Scientists on Advanced Optical Manufacturing, YSAOM 2023

作者： Fu, xihong Shen, Zhong Ruan, Ping Yang, Hao Kang, Shifa Li, Hua Li, Zhaohui Xi an Institute of Optics and Precision Mechanics of CAS China University of Chinese Academy of Sciences China

ISBN: (纸本)9781510672673

The field of space imaging optics is committed to developing large field of view, large aperture, and unobstructed space optical imaging systems. The structure of optical systems has evolved from coaxial optical systems to off axis optical systems. In order to reduce the difficulty of installation and improve the efficiency of off-Axis multi-reflection optical systems, the paper studies the precision installation and adjustment process method of off-Axis multi-reflection optical systems on account of the principles of spherical autocollimation and spatial coordinate measurement. Establish global coordinate system based on the optical design and processing benchmark of the off-Axis multi-reflection optical system, use a laser tracker and auto-collimation theodolite to accurately locate the elevation and azimuth angles of the reflector, and adjust the optical target ball to the theoretical center point position and align with PSM. Remove the target ball. If the actual center of the spherical mirror is not in the theoretical coordinate position, the position deviation and sharpness between the reflection image point and the point light source can be monitored in real-Time, and the posture of the mirror can be precisely adjusted to make its reflection image point coincide with PSM. This process method can achieve high-precision and high-efficiency adjustment of the off-Axis multi-reflection optical system. The assembly results show that the spatial coordinate position error between the mirrors in the off-Axis multi-reflection optical system is better than 15μm through high-precision spatial coordinate positioning, reference transfer and precision measurement. The angle error is better than 30″, and the center field of view wave aberration RMS=0.034λ@632.8nm, meeting the design specifications. © COPYRIGHT SPIE.

关键词： Optical systems

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