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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1521-1530 订阅

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Remote measurement of the clinical prescription of spectacle lenses 25

Remote measurement of the clinical prescription of spectacle...

引用

conference on Novel Optical systems, Methods, and Applications XXV

作者： Guan, Yuanxin Miller, Joseph M. Harvey, Erin M. Ashley, Sean Schwiegerling, Jim Univ Arizona James C Wyant Coll Opt Sci 1630 E Univ Blvd Tucson AZ 85721 USA Univ Arizona Dept Ophthalmol & Vis Sci 655 N Alvernon Way 204 Tucson AZ 85711 USA

ISBN: (纸本)9781510654174;9781510654167

The SpectRx system has been developed to measure sphero-cylindrical spectacle lens power as an alternative to clinical lensmeters. This work was inspired by the ongoing global pandemic, which limited physical access to eye care facilities for regular eye exams The SpectRx system aims to bypass this limitation by providing at-home prescription measurements. The power and orientation of the spectacle lenses are obtained by the use of readily available objects such as a cell phone camera, a displayed or printed target, and a fixed-dimension magnetic stripe card. The magnification of the lenses can be calculated by examining the image captured through the lens of the target at a fixed distance. The magnification may be spatially varying due to the cylinder component of the lens. processing the pictures captured with a cell phone camera is done automatically with standard image processing algorithms. The processed images, in turn, are used to calculate a clinical prescription, i.e., Sph/CylxAxis. The SpectRx may expand access to quality eye care in not only the current pandemic situation but also in locations where eye care may not be easily accessible, such as some rural or remote areas. The image processing and clinical prescription calculation are discussed here.

关键词： Spectacle lenses power measurement telemedicine image processing

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Hierarchical DNN-Based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions 16

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

引用

16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Yu, Cheng-Hsuan Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781665464956

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both largescaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for oversegmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： image segmentation superpixel merging deep learning

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Wavefront coded image restoration algorithm based on compressed sensing for off-axis mirror system 7

Wavefront coded image restoration algorithm based on compres...

引用

7th Asia Pacific conference on Optics Manufacture (APCOM)

作者： Yi, Huang Jun, Chang Li Dongmei Cao Jiajing Shan, Du Beijing Inst Technol Beijing 100081 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China

ISBN: (数字)9781510652095

ISBN: (纸本)9781510652095;9781510652088

By combining optical systems and image processing, wavefront coding can greatly expand the depth of focus and depth of field of optical systems. It has been widely used in iris detection, high-power microscopic objective lens, infrared optical system athermalized, and so on. At present, the image restoration algorithms commonly used in wavefront coding are based on deconvolution, Wiener filtering, and so on. Although these algorithms can achieve an excellent image restoration effect, they will also bring boundary ringing effects and artifacts to the image. When the image is disturbed by strong noise, the restoration effect will also be seriously affected. To solve these problems, a wavefront coded image restoration algorithm based on compressed sensing is proposed in this paper. The strong data reconstruction ability of the compressed sensing restoration algorithm is used to restore the encoded image obtained by the wavefront coding system. This method can effectively suppress noise and reconstruct the image without artifact and boundary ringing effect. Through the comparison of simulation results, the effectiveness of the proposed method is verified.

关键词： wavefront coding compressed sensing artifact

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Review of Reconstruction Methods for Spectral Snapshot Compressive Imaging 11th

Review of Reconstruction Methods for Spectral Snapshot Compr...

引用

11th International conference on Communications, Signal processing, and systems, CSPS 2022

作者： Yuan, Hao Ding, xiaoming Yan, Qiangqiang Wang, xiaocheng Li, Yupeng Han, Tingting Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China CAS Key Laboratory of Spectral Imaging Technology Xi’an Institute of Optics and Precision Mechanics Xi’an710119 China

ISBN: (纸本)9789819926527

Snapshot compressive imaging (SCI) uses a 2D sensor to obtain higher dimensional data and then reconstructs the underlying high-dimension data by elaborate algorithms. Applying SCI to capture hyperspectral images is known as spectral SCI. Although this technique has been proposed for more than a decade, it has not been widely used, mainly because its reconstruction accuracy and reconstruction speed are not yet satisfactory, which is the research focus on spectral SCI. This paper investigates the literatures on reconstruction methods of spectral SCI, mainly involving coded aperture optimization, model-based reconstruction algorithms and deep learning-based reconstruction algorithms. In this paper, we also provide a summary of studies on noise modeling and denoising for reconstructed spectral SCI data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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High Dynamic Range image processing Techniques for Automation and Machine Vision

High Dynamic Range Image Processing Techniques for Automatio...

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Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Chunhao Li Zhejiang University of Science and Technology Hangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores various HDR processing techniques and their potential applications in automation and machine vision. By using methods such as multiple image fusion, image registration, and tone mapping, the paper demonstrates how HDR processing can enhance visual data in automated systems, improving accuracy in environments requiring complex lighting conditions. This work applies HDR algorithms to real-world scenarios, showcasing their potential in industrial automation and robotics, where accurate visual data plays a crucial role.

关键词： Visualization image registration Automation Accuracy Service robots Machine vision Lighting Real-time systems High dynamic range image fusion

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A Vision-aided Open Radio Access Network for Obstacle-aware Wireless Connectivity 20

A Vision-aided Open Radio Access Network for Obstacle-aware ...

引用

20th Wireless On-Demand Network systems and Services conference, WONS 2025

作者： Simões, Carolina Coelho, André Ricardo, Manuel Universidade do Porto Inesc Tec Faculdade de Engenharia Portugal

ISBN: (纸本)9783903176713

High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance by processing and interpreting visual data, enabling obstacle avoidance and ensuring LoS signal propagation. We propose a vision-aided Radio Access Network (RAN) based on the O-RAN architecture and capable of perceiving the surrounding environment. The vision-aided RAN consists of a gNodeB (gNB) equipped with a video camera that employs CV techniques to extract critical environmental information. An xApp is used to collect and process metrics from the RAN and receive data from a Vision Module (VM). This enhances the RAN's ability to perceive its surroundings, leading to better connectivity in challenging environments. © 2025 IFIP.

关键词： image coding

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Intelligent Detection of Insulator Defects on Transmission Lines in Complex Backgrounds based on Improved DETR Visual Perception Model

Intelligent Detection of Insulator Defects on Transmission L...

引用

Artificial Intelligence and Power systems (AIPS), International conference on

作者： Songping Tang Ruifeng Zhu Chao Ji Chong Li Yuxi Dong Yun Zhang Junpeng Liu Huizhou PowerSupply Bureau of Guangdong Power Grid Co. Ltd. Huizhou China Xi'An Polytechnic University Xi'an China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

The problem of insulator defects in transmission lines caused by adverse environmental conditions poses a significant risk to the safe and stable operation of the power system. In order to improve upon traditional methods of inspecting transmission lines in real-time, this article proposes an optimized detection method based on the Detection Transformer (DETR) algorithm. With the aim of achieving a more intelligent detection of transmission lines, unmanned aerial vehicles (UAVs) are employed for line inspections. Firstly, the insulator images captured by UAV inspections are processed to remove fog and noise, enhancing their clarity. Subsequently, a hybrid attention mechanism module is introduced into the backbone network ResNet50 of the DETR algorithm to reduce data processing errors. Insulator defect data is annotated using LabelImg software and saved according to the Pascal VOC standard dataset. Finally, the annotated data is input into the improved DETR algorithm for feature data training, generating an optimal image neural network detection model capable of intelligently detecting insulators in transmission lines amidst complex backgrounds.

关键词： Training Power transmission lines Software algorithms Inspection Insulators Feature extraction Autonomous aerial vehicles Real-time systems Power systems Visual perception

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Automatic Detection Method of Target Axis and Mark Ring Based on Edge Optimization and Line Screening

Automatic Detection Method of Target Axis and Mark Ring Base...

引用

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

作者： xinrui Yang Junbiao Chen Mingwei Gao Jing Cao Kai Wang School of Optics and Photonics Beijing Institute of Technology Beijing China Testing Technology Center Norinco Group Testing and Research Institute Xi’an China Development Planning Service Norinco Group Testing and Research Institute Xi’an China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Aiming at the problem that traditional target contour and central axis detection algorithms are easily affected by factors such as non-uniform backgrounds, incomplete target imaging, and tail flame interference, which can lead to deviations in the central axis and mass center, an automatic detection method for the object central axis and mark rings based on edge optimization and line screening is proposed. Firstly, the frame difference algorithm is combined with canny algorithm to initially locate the ROI region of the moving target. Then, an improved edge detection algorithm based on the OTSU method is used to automatically extract the effective edges. Firstly, the frame difference algorithm is combined with the canny algorithm to initially locate the ROI region of the moving target. Then, an improved edge detection algorithm based on the OTSU algorithm is used to automatically extract the effective edges. After that, the target attitude interval and length threshold are calculated using EDLines algorithm, and the central axis is selected based on hough transform and line screening criteria. Finally, the mark rings are detected through RGB image segmentation and morphological operations, and their intersections with the central axis are used as the feature points. The experimental results show that the proposed method has obvious advantages over the traditional algorithms in engineering application, detection efficiency and accuracy.

关键词： image segmentation image edge detection Noise Fires Tail Interference Transforms Feature extraction Solids Optimization

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Objects Segmentation in Augmented Reality Environment 5

Objects Segmentation in Augmented Reality Environment

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5th International conference on Computational Linguistics and Intelligent systems (COLINS)

作者： Sidenko, Ievgen Misiuk, Tetiana Kondratenko, Galyna Kondratenko, Yuriy Petro Mohyla Black Sea Natl Univ 68th Desantnykiv Str10 UA-54003 Mykolaiv Ukraine

The paper discusses objects (images) segmentation algorithms that are applicable in mobile applications with augmented reality. An example of image processing with virtual objects by different algorithms (the MeanShift algorithm, the GrabCut algorithm, the k- means algorithm) is considered. Different libraries, tools, and environments to implement segmentation algorithms were analyzed, such as Scikit-image, Pixellib, OpenCV, Point Cloud Library. The application was created for mobile devices running iOS 10 and higher. The GrabCut algorithm turned out to be the best algorithm for image processing. The processing result was the closest to the expected one. Although the algorithm has some errors. Despite the fact that the area that was contoured turned out to be the clearest and most complete in comparison with other algorithms, this area also includes areas of the image that do not belong to the objects under study.

关键词： Augmented reality mobile applications image segmentation virtual scene

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Face Recognition algorithms for Online and OnSite Classes 19

Face Recognition Algorithms for Online and OnSite Classes

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19th International Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Kliangsuwan, Thitinan Heednacram, Apichat Silanon, Kittasil Prince Songkla Univ Coll Comp Phuket Thailand

ISBN: (数字)9781665485104

ISBN: (纸本)9781665485104

Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system.

关键词： attendance system face recognition algorithm image processing

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