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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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The Estimation of Inertial Measurement Units Accuracy Using Digital image processing algorithms

The Estimation of Inertial Measurement Units Accuracy Using ...

引用

2022 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2022

作者： Shmatko, E.V. Poroykov, A. Yu. National Research University Moscow Power Engineering Institute Department of Physics Named after V.A. Fabrikant Moscow Russia

ISBN: (纸本)9781665462402

The paper describes an approach for estimation of inertial measurement unit using image processing algorithm to determine the position of an object in space. The results of measurements of the angular velocities of a freely rotating platform using the inertial and photogrammetry methods are presented. The inertial method data contains the measurement results of a three-axis solid gyroscope. The photogrammetry method determines the position of the platform using digital video cameras based on images of fiducial markers installed on it. A comparison of the measurement results is presented, conclusions about the accuracy of the tested gyroscope are drawn. © 2022 IEEE.

关键词： Angular velocity

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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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ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama 17th

ResRnnNet: Learning to Reconstruct the 3D Room Layout from a...

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Zhao, Shaonan Li, Wei Univ New South Wales Sch Civil Engn & Environm Sydney NSW 2052 Australia Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9789811910579;9789811910562

3D room layout reconstruction from a single RGB panoramic image has been an emerging research topic in recent years. To achieve better prediction accuracy, in this paper, we propose a new approach to predict 3D room layout from a single panoramic image. Our reconstruction flow follows a common framework which is same as LayoutNet [9] and HorizonNet [4];however, we redesign a new deep learning architecture with recurrent neural networks (RNNs) encoder-decoder as an extension for keypoints refinement and use a gradient ascent optimization algorithm to minimize the similar loss. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.

关键词： 3D room layout Deep learning Manhattan world Panorama image

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6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Completion

6G XL-MIMO Channel Estimation with Sub-Nyquist Tensor Comple...

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Semyon Dorokhin Vladimir Lyashev Mikhail Makurin Moscow Institute of Physics and Technology Moscow Russia Radio Transmission Technology Lab Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Channel estimation overhead reduction is one of the main problems for 6G XL-MIMO systems. As the number of antennas and subcarriers grows, so does the overhead of traditional channel estimation methods. High overhead limits user mobility and affects the latency. Most of the popular tensor-based channel estimation algorithms combine reference signals transmission and channel tensor estimation into one problem. Such approach typically imposes limitations on reference signals, making backward-compatibility with existing 5G standard challenging. We propose to separate tensor completion and channel tensor elements estimation into different tasks. We show that tensor completion algorithms from image processing and fMRI scanning can be reused for sub-Nyquist completion of OFDM MIMO tensors to reduce the overhead. Furthermore, we extend one of these algorithms from CPD to Tensor Train (TT) and demonstrate that TT-based algorithm reduces channel estimation error up to 2 times. With the proposed approach other algorithms can be further developed based on tensor completion theory.

关键词： 6G mobile communication Tensors OFDM image processing Channel estimation Signal processing algorithms Estimation Massive MIMO Functional magnetic resonance imaging Standards

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A Survey of Transformer-Based Few-Shot image Classification Techniques

A Survey of Transformer-Based Few-Shot Image Classification ...

引用

Natural Language processing (ICNLP), International conference on

作者： Chaoqi Song Ying Liu Jinglu He Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Few-shot image classification is a critical issue in the field of computer vision, facing challenges related to data scarcity and model generalization. Transformer models, representing self-attention mechanisms, have made significant strides in recent years in the domain of few-shot classification. This paper commences with an introduction to the background and challenges of few-shot classification, along with a description of the principles and structure of the Transformer model. Subsequently, the paper categorizes Transformer-based few-shot image classification methods into meta-learning-based, metric-learning-based, fine-tuning-based, and feature-enhancement-based approaches, whose theoretical foundations of each method are expounded and the comparative analysis of representative algorithms are also provided. Furthermore, the paper delves into prospective research directions in this field.

关键词： Surveys Representation learning Learning systems Computational modeling Neural networks Computer architecture Transformers Natural language processing Classification algorithms image classification

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Abnormal image Classification Using Vision Transformer for Smart Agriculture 8

Abnormal Image Classification Using Vision Transformer for S...

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8th IEEE International conference on Engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hafeez, Talha Shahid, Muhammad Farrukh Khanzada, Tariq J.S. Fast School of Computing FAST-NUCES Karachi Pakistan King Abdulaziz University Faculty of Computing and Information Technology Department of Information System Jeddah21589 Saudi Arabia Mehran Uet Computer Systems Engineering Departments Jamshoro Pakistan

ISBN: (纸本)9798350327090

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset. © 2023 IEEE.

关键词： Classification (of information)

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image processing for Future Machine Learning Algorithm Applications on Infrared Thermography of Building Envelope systems 5th

Image Processing for Future Machine Learning Algorithm Appli...

引用

5th International conference on Building Energy and Environment, COBEE 2022

作者： Gertsvolf, David Horvat, Miljana Khademi, April Aslam, Danesh Berardi, Umberto Department of Architectural Science Toronto Metropolitan University TorontoON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9789811998218

This paper presents and discusses the development of a procedure required for the future application of machine learning (ML) in the review and investigation of infrared (IR) thermography images for building envelopes. A background on ML and existing applications within the building science industry is presented to capture an understanding of the possibilities available within this innovative method of evaluating deficiencies of building envelopes. Then, a breakdown of the groundwork and image processing required for the application of ML algorithms is reported. A procedure developed using a python coding environment is presented, demonstrating the process of taking an RGB IR image, grey scaling it and normalizing for the application of unsupervised ML algorithm. Similarly, for the purpose of supervised ML, the same approach is presented, followed by a procedure for the creation of segmentation masks using two types of approaches: temperature threshold and manually identified segments. A discussion of possible development of these new techniques concludes this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Implementation of a Deep Learning Technique for Detection of Objects on Roads for Autonomous Vehicles

Implementation of a Deep Learning Technique for Detection of...

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International conference on Applied Electromagnetics, Signal processing and Communication (AESPC)

作者： Kakarla. Deepti S. Aruna Deepti Ch. Neetu K.R. Deepthi Dept of ECE Vasavi college of Engineering Hyderabad India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Autonomous driving and vehicle safety things profoundly depend on object detection to guarantee precise an robust right-time performance under different environmental conditions. The effectiveness of this systems depends on precise and efficient processing algorithms. The manuscript aim is to contrast the correctness of three notable image processing algorithms in the context of auto driving: Convolutional Neural Networks (CNN), Retina Net, and You Only Look Once v8 (YOLOv8). The assessment is performed using the Roboflow and kaggle dataset. The first algorithm under assessment is the Convolutional Neural Networks(CNN), Retina Net, and You Only Look Once v8 (YOLOv8). Each algorithm's unique approach to object detection is reviewed, observing their strengths and weaknesses in accurately identifying things in complex road situations. A comparative analysis is performed using the Robo flow and Kaggle dataset, covering various real-world contexts. The algorithms are evaluated based on correctness, considering occurrences with and without occlusion and truncation. Computation time,a vital aspect for real-time applications, is also considered. The precision-recall curve is applied as a metric to determine the algorithms' overall performance. Retina Net, with its focal loss mechanism, is reviewed for its capability to manage occluded and truncated things and its influence on overall correctness. YOLOv8 emerges as a standout performer, displaying superior correctness in discovering challenging road target things under complex situations and adverse weather conditions. Its right-time deployment capabilities, combined with high correctness, position it as a compelling choice for autonomous driving systems. In conclusion, this comparative analysis shines a light on the strengths and deficiencies of notable image processing algorithms for object discovery in autonomous driving systems. YOLOv8 stands outas the most accurate and efficient algorithm, excelling in challenging real-

关键词： YOLO Accuracy Roads image processing Simulation Vehicle safety Retina Real-time systems Convolutional neural networks Autonomous vehicles

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Resource-Constrained image processing for Scientific CubeSats 6

Resource-Constrained Image Processing for Scientific CubeSat...

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6th International conference on algorithms, Computing and systems, ICACS 2022

作者： Kozaris, Ioannis Gioulekas, Fotios Katsaros, Panagiotis School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Directorate of Informatics 5th Regional Health Authority Larissa Greece

ISBN: (纸本)9781450397407

CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained nature of such systems poses significant challenges in software design, for the system payload. We focus on image compression and processing for scientific space missions, a vital functionality, due to the limited onboard storage capacity and the infrequent time periods, in which a satellite in orbit can send images to the ground, through a limited bandwidth connection. For CubeSat systems, the performance of image processing depends - even more than other space systems - on trade-offs, which are influenced by size, power and complexity constraints. In this context, we present the design challenges and the image compression/processing solution developed, for a university CubeSat built to carry on a biological experiment. All changes of the performance capability in the different parts of the payload image data chain (image resolution, onboard storage capacity, communication channel bandwidth and error characteristics) are taken into account. Moreover, any decision that increases the risk of incorrect reception of images from the experiment is weighted against the benefits of improved performance. We provide experimental results that show how our image processing solution resolves the associated trade-offs, while fulfilling the mission requirements. © 2022 ACM.

关键词： image compression

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