The article presents a concept of the analysis of mechanical wear of prisms in the in-pavement airport lamps. The solution is based on imageprocessing technique that requires an appropriate selection of parameters du...
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ISBN:
(数字)9788362065424
ISBN:
(纸本)9788362065424
The article presents a concept of the analysis of mechanical wear of prisms in the in-pavement airport lamps. The solution is based on imageprocessing technique that requires an appropriate selection of parameters due to the specificity of the objects. During the experimental tests, a database consisting of 316 photos of IDM airport lamps mounted in the airport areas was used. The proposed solution using an artificial neural network allows for the classification of lamps with an efficiency of 81.4%.
In this paper, the 3D space imaging model of machinevision is constructed. Starting from the traditional machinevisionimageprocessing algorithm flow, the image denoising process and target tracking process are opt...
In this paper, the 3D space imaging model of machinevision is constructed. Starting from the traditional machinevisionimageprocessing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional imageprocessing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded imageprocessing.
Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and e...
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Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.
With the widespread use of drones, detecting small targets in aerial images captured by drones poses significant challenges. Issues such as small size and low resolution make these targets difficult to detect. In resp...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
With the widespread use of drones, detecting small targets in aerial images captured by drones poses significant challenges. Issues such as small size and low resolution make these targets difficult to detect. In response to this, this paper proposes an improved object detection algorithm based on YOLOv8. Firstly, a lightweight convolution and Manhattan self-attention mechanism were introduced into the backbone network, along with a feature fusion module in the neck, and the loss function was optimized to enhance the model's performance and robustness in small target detection. Experiments show that the model achieved a 6.2% improvement in mAP@0.5 and a 5% increase in recall on the VisDrone2019 dataset, demonstrating its effectiveness in detecting small targets in drone applications.
This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization.
image segmentation in computer visionapplications plays a critical role in the video processing workflow. In real applications, where interesting elements are moving in the presence of moving objects in the backgroun...
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ISBN:
(纸本)9783031065279;9783031065262
image segmentation in computer visionapplications plays a critical role in the video processing workflow. In real applications, where interesting elements are moving in the presence of moving objects in the background, complex models are required in the segmentation process to obtain better results. In this paper, a methodology based on super-resolution and test time augmentation is proposed to improve the precision and effectiveness of the segmentation process. Our proposal avoids both modification and retraining of the model. Experiments show that our approach can increase the mean average precision of images segmentation in sequences from well-known benchmark datasets with a significant improvement.
Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and re...
Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly d...
Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly detection technique called Dual-Domain Autoencoders (DDA) is proposed to address these challenges. First, Nonnegative Matrix Factorization (NMF) is applied to decompose the hyperspectral data into anomaly and background components. Refinement of the designation is then done using intersection masking. Next, a spectral autoencoder is trained on identified background signature pixels and used to reconstruct the image. The reconstruction error highlights spectral anomalies. Furthermore, a spatial autoencoder is trained on principal component patches from likely background areas. Fused reconstruction error from the spectral and spatial autoencoders is finally used to give enhanced anomaly detection. Experiments demonstrate higher AUC for DDA over individual autoencoders and benchmark methods. The integration of matrix factorization and dual-domain, fused autoencoders thus provides superior anomaly identification. Spatial modeling further constrains the background, enabling accurate flagging of unusual local hyperspectral patterns. This study provides the effectiveness of employing autoencoders trained on intelligently sampled hyperspectral pixel signatures and spatial features for improved spectral-spatial anomaly detection.
The article proposes an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by analyzing data obtained by a machinevision system. At the first stage, the operation...
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ISBN:
(纸本)9781510655560
The article proposes an approach to improve the accuracy of restoring the boundaries of objects obtained to create 3D structures by analyzing data obtained by a machinevision system. At the first stage, the operation of reducing the number of color gradients is performed, the technique allows you to combine similar values into common enlarged structures. This operation allows you to simplify the analyzed objects, since small details are not important. In parallel with the first operation of denoising is performed. The paper proposes the application of the multicriteria processing method with the possibility of smoothing locally stationary sections and preserving the boundaries of objects. As an algorithm for strengthening the boundaries of objects, a modification of the combined multi-criteria method is used, which makes it possible to reduce the effect of salt/pepper noise and impulse failures, as well as to strengthen the detected boundaries of objects. The resulting images with enhanced boundaries are fed to the input of the block for constructing three-dimensional objects. The data obtained by both a stereo pair and a camera based on 3D construction using structured light were used in the work. On a set of synthetic data simulating the work in real conditions, the increase in the efficiency of the system using the proposed approach is shown. Based on field data under conditions of interfering factors in the form of dust/fog, the applicability of the proposed approach for solving problems of increasing the accuracy of restoring the boundaries of objects obtained to create three-dimensional structures is shown. images of simple shapes are used as analyzed objects.
Diabetic retinopathy is a serious eye disease which can lead to vision defects in diabetic patients. Early detection is important for preventing vision loss. Automating the detection process makes it less laborious an...
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ISBN:
(数字)9798350355468
ISBN:
(纸本)9798350355475
Diabetic retinopathy is a serious eye disease which can lead to vision defects in diabetic patients. Early detection is important for preventing vision loss. Automating the detection process makes it less laborious and helps in achieving more accurate results. Therefore, this research aims to study various machine learning methods and develop an effective model for diabetic retinopathy detection. The proposed model operates through four stages: pre-processing, feature extraction, feature optimization, and classification. imageprocessing techniques are thoroughly utilized to enhance the fundus images. The textual and spatial features are then extracted and most relevant features are selected using particle swarm optimization. These features are then fed into machine learning classifiers for disease diagnosis. The model is trained and validated on the MESSIDOR I and MESSIDOR ii datasets. The model achieves an accuracy (85.9%), F1 score (0.8585), Sensitivity (0.8049) and Specificity (0.8958) on MESSIDOR ii data with Support Vector machine (SVM) classifier. The results obtained show the effectiveness of the work in early detection.
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