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Analyzing long-term performance of the Keck-ii adaptive optics system

JOURNAL OF ASTRONOMICAL TELESCOPES INSTRUMENTS AND SYSTEMS 2022年第2期8卷

作者： Ramey, Emily Lu, Jessica R. Yin, Ruoyi Robinson, Steve Wizinowich, Peter Ragland, Sam Lyke, Jim Jia, Siyao Sakai, Shoko Gautam, Abhimat Do, Tuan Hosek, Matthew, Jr. Ghez, Andrea Morris, Mark R. Becklin, Eric Matthews, Keith Univ Calif Berkeley Dept Astron Berkeley CA 94720 USA WM Keck Observ Kamuela HI USA Univ Calif Los Angeles Dept Phys & Astron Los Angeles CA USA CALTECH Div Phys Math & Astron Pasadena CA 91125 USA

We present an analysis of the long-term performance of the W. M. Keck observatory laser guide star adaptive optics (LGS-AO) system and explore factors that influence the overall AO performance most strongly. Astronomical surveys can take years or decades to finish, so it is worthwhile to characterize the AO performance on such timescales in order to better understand future results. The Keck telescopes have two of the longest-running LGS-AO systems in use today, and as such they represent an excellent test-bed for processing large amounts of AO data. We use a Keck-ii near infrared camera 2 (NIRC2) LGSAO surve of the Galactic Center (GC) from 2005 to 2019 for our analysis, combining image metrics with AO telemetry files, multi-aperture scintillation sense/differential imaging motion monitor turbulence profiles, seeing information, weather data, and temperature readings in a compiled dataset to highlight areas of potential performance improvement. We find that image quality trends downward over time, despite multiple improvements made to Keck-ii and its AO system, resulting in a 9 mas increase in the average full width at half maximum (FWHM) and a 3% decrease in the average Strehl ratio over the course of the survey. image quality also trends upward with ambient temperature, possibly indicating the presence of uncorrected turbulence in the beam path. Using nine basic features from our dataset, we train a simple machine learning (ML) algorithm to predict the delivered image quality of NIRC2 given current atmospheric conditions, which could eventually be used for real-time observation planning and exposure time adjustments. A random forest algorithm trained on this data can predict the Strehl ratio of an image to within 18% and the FWHM to within 7%, which is a solid baseline for future applications involving more advanced ML techniques. The assembled dataset and coding tools are released to the public as a resource for testing new predictive control and point spread fu

关键词： adaptive optics machine learning predictive modeling

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CMOS image sensor for wide dynamic range feature extraction in machine vision

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ELECTRONICS LETTERS 2021年第5期57卷 206-208页

作者： Kim, Hyeon-June Kangwon Natl Univ Dept Elect Informat Commun Engn Gangwon South Korea

This letter presents a wide dynamic range (WDR) feature extraction (FE) readout scheme for machine vision applications using CMOS image sensors (CISs). The proposed scheme with the proposed pixel structure has two operating modes, the normal and WDR modes. In the normal operating mode, the proposed CIS captures a normal image with high sensitivity. In addition, as a unique function, a bi-level image is obtained for real-time FE even if a pixel is saturated in strong illumination conditions. Thus, compared to typical CISs for machine vison, the proposed CIS can reveal object features that are blocked by light in real time. In the WDR operating mode, the proposed CIS produces a WDR image with its corresponding bi-level image. A prototype CIS was fabricated using a standard 0.35-mu m 2P4M CMOS process with a 320 x 240 format (QVGA) with 10-mu m pitch pixels. At 60 fps, the measured power consumption was 5.98 mW at 3.3 V for pixel readout and 2.8 V for readout circuitry. The dynamic range of 73.1 dB was achieved in the WDR operating mode.

关键词： image recognition image sensors Computer vision and image processing techniques

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VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

VISTA: A Visual and Textual Attention Dataset for Interpreti...

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IEEE Winter applications and Computer vision Workshops (WACVW)

作者： Harshit Tolga Tasdizen School of Computing University of Utah Salt Lake City UT USA Scientific Computing and Imaging Institute University of Utah Salt Lake City UT USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The recent developments in deep learning (DL) led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated vision and Language Models. Despite their remarkable capabilities, these models are frequently regarded as black boxes within the machine learning research community. This raises a critical question: which parts of an image correspond to specific segments of text, and how can we decipher these associations? Understanding these connections is essential for enhancing model transparency, interpretability, and trustworthiness. To answer this question, we present an image-text aligned human visual attention dataset (VISTA) 1 1 The data is available at https://***/h-pal/Data-for-VISTA that maps specific associations between image regions and corresponding text segments. We then compare the internal heatmaps generated by VL models with this dataset, allowing us to analyze and better understand the model's decision-making process. This approach aims to enhance model transparency, interpretability, and trustworthiness by providing insights into how these models align visual and linguistic information. We conducted a comprehensive study on text-guided visual saliency detection in these VL models. This study aims to understand how different models prioritize and focus on specific visual elements in response to corresponding text segments, providing deeper insights into their internal mechanisms and improving our ability to interpret their outputs.

关键词： Measurement Visualization image segmentation Computer vision Analytical models Computational modeling machine vision Natural language processing Reliability Saliency detection

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Medical Adaptation of Large Language and vision-Language Models: Are We Making Progress?

Medical Adaptation of Large Language and Vision-Language Mod...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Jeong, Daniel P. Garg, Saurabh Lipton, Zachary C. Oberst, Michael Machine Learning Department Carnegie Mellon University United States Mistral AI France Department of Computer Science Johns Hopkins University United States Abridge AI United States

ISBN: (纸本)9798891761643

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream medical tasks, such as answering medical licensing exam questions. In this paper, we compare seven public "medical" LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. For instance, across the tasks and model pairs we consider in the 3-shot setting, medical LLMs only outperform their base models in 12.1% of cases, reach a (statistical) tie in 49.8% of cases, and are significantly worse than their base models in the remaining 38.2% of cases. Our conclusions are based on (i) comparing each medical model head-to-head, directly against the corresponding base model;(ii) optimizing the prompts for each model separately;and (iii) accounting for statistical uncertainty in comparisons. While these basic practices are not consistently adopted in the literature, our ablations show that they substantially impact conclusions. Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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Remote Sensing image Captioning (RSIC): A Technical Review 1st

Remote Sensing Image Captioning (RSIC): A Technical Review

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1st International Conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： Dhinesh, A. Sumathy, P. Department of Computer Science Bharathidasan University Tamilnadu Tiruchirappalli620023 India

ISBN: (纸本)9789819776153

Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, military intelligence, and autonomous vehicles. The effective procedure to generate the captions from remote sensing images complements the above-mentioned application domains. Various baseline data sets have been created by the researchers to enhance the quality of captioning by processing the diverse features of the geospatial information. In this paper, we have technically reviewed important literature that follow different algorithms for generating the captions. For example, we have presented the technical review on vision-Language Aligning Paradigm (VLCA) under the bi-lingual caption generation model, Joint-Training Two-Stage (JTTS) technique under multimodel fusion category, Multilevel and Contextual Attention Network (MLCA-Net) under context-aware captioning, LEVIR-CC belongs to transfer learning model, BERT and GPT-3 models belong to transfer-based model, Multiscale Attention (MSA) and Multifeat Attention (MFA) of Multiscale captioning model and Summarization Driven (SD)-RSIC of fine-grained captioning model. We have also presented the performance of each of these methods on various benchmark datasets. For evaluation, different well-known performance metrics are considered. The result is critically evaluated and commented on. In the future, a more rigorous review of these methods along with other relevant methods will be presented along with implementation data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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Advancements in Structural Health Monitoring Using Combined Computer-vision and Unmanned Aerial Vehicles Approaches 10th

Advancements in Structural Health Monitoring Using Combined ...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Sabato, Alessandro Niezrecki, Christopher Dabetwar, Shweta Kulkarni, Nitin Nagesh Bottalico, Fabio Nieduzak, Tymon Univ Massachusetts Lowell Dept Mech Engn Lowell MA 01852 USA

ISBN: (纸本)9783031072581;9783031072574

Aerospace, civil, energy, and mechanical engineering structures continue to be used despite reaching their design lifetime. Developing sensing and data analytics to assess the structural condition of the targeted systems is crucial. Traditional contact-based techniques may produce inconsistent results and are labor-intensive to be considered a valid alternative for monitoring large-scale structures such as bridges, large buildings, and wind turbines. Advancements in image-processing algorithms made techniques such as three-dimensional digital image correlation (3D-DIC), infrared thermography (IRT), motion magnification (MM), and structure from motion (SfM) appealing tools for structural health monitoring and non-destructive testing. Besides, as those techniques are implemented within unmanned aerial vehicles (UAVs), the measurement process is expedited while reducing interference with the targeted structure. This paper summarizes the research experience performed at the University of Massachusetts Lowell. The results of these activities show that the combination of autonomous flight with 3D-DIC, IRT, and SfM can provide precious insights into the structural conditions of the inspected systems while reducing downtime and costs. The study includes future research directions to make those approaches suitable for real-world applications.

关键词： Computer vision Digital image correlation Infrared imaging Motion magnification Optical techniques Structure from motion Unmanned aerial vehicles

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Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance applications 10th

Real-Time Object Detection and Tracking Design Using Deep Le...

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10th International Conference on Innovations in Computer Science and Engineering, ICICSE 2022

作者： Kusuma, T. Ashwini, K. Global Academy of Technology Bangalore India

ISBN: (纸本)9789811974540

We propose a CNN-based framework for "real-time object detection and tracking using deep learning" in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a "spatial attention mechanism (STAM)" to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s "You Only Look Once" technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Revolutionizing machine vision: Advanced Convolutional Strategies for Rapid image processing

Revolutionizing Machine Vision: Advanced Convolutional Strat...

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Information and Communication Technology (ICTech), International Conference of

作者： Hanlei Wu Pittsburgh Institute Sichuan University Chengdu China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This paper presents a comprehensive examination of innovative strategies aimed at enhancing machine vision technology, particularly in the context of energy efficiency and processing speed, critical factors for applications like facial recognition. The study focuses on three distinct approaches: an optimized two-dimensional convolution algorithm, a novel Field-Programmable Gate Array (FPGA) implementation, and advancements in multichannel meta-imagers. Firstly, the paper discusses an optimized algorithm for two-dimensional convolutions, a fundamental operation in machine vision. This advanced algorithm significantly reduces computational complexity. For instance, in executing a two-dimensional 3×3 cyclic convolution, the proposed method reduces the number of necessary multiplications from 81 to merely 13, offering a substantial improvement in efficiency. Secondly, the paper explores an innovative FPGA implementation of the two-dimensional convolution algorithm. This implementation is designed to minimize the use of shift registers, multipliers, and adders. As a result, it utilizes fewer Look-Up Tables (LUTs), leading to energy and time savings in executing the convolution process. The paper details the architecture of this FPGA-based approach and its implications for energy consumption and processing speed in machine vision applications. Finally, the paper introduces a novel technique called the Avg-Topk method, addressing a critical challenge in the pooling layer of convolutional neural networks. This method combines the benefits of average pooling with the advantages of max pooling, aiming to enhance the accuracy of the pooling layer without compromising on efficiency. The Avg-Topk method represents a significant step forward in optimizing the pooling process within machine vision systems. In summary, this paper delves into groundbreaking methods to improve the speed and energy efficiency of machine vision systems, offering valuable insights and potential solution

关键词： image resolution Accuracy Convolution machine vision Shift registers Energy efficiency Table lookup Classification algorithms Usability Field programmable gate arrays

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Evolving Convolutional Neural Networks with Meta-Heuristics for Transfer Learning in Computer vision 3

Evolving Convolutional Neural Networks with Meta-Heuristics ...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Srilakshmi, V. Kiran, G. Uday Mounika, M. Sravanthi, A. Sravya, N.V.K. Akhil, V.N.S. Manasa, M. B V Raju Institute of Technology Telangana Narsapur India

In the rapidly evolving landscape of computer vision and artificial intelligence, transfer learning has emerged as a powerful tool for efficiently applying pre-trained models to new tasks. This article delves into the intriguing concept of evolving Convolutional Neural Networks (CNNs) with meta-heuristics for transfer learning in computer vision. The primary focus is on enhancing the adaptability and efficiency of CNNs, making them better suited for specialized tasks. The article covers the significance of transfer learning, the challenges faced in transfer learning with CNNs, the basics of CNN architecture, and the role of meta-heuristics in optimizing CNNs. Real-world applications and success stories demonstrate the transformative potential of these techniques in fields like medical image analysis and autonomous vehicles. It explores emerging trends and potential developments in the domain, emphasizing the impact on various sectors, including healthcare, natural language processing, and robotics. The promise of evolving CNNs with meta-heuristics lies in their capacity to tackle intricate problems with greater precision, ultimately reshaping the landscape of artificial intelligence and machine learning. Ongoing research ensures a promising future for this amalgamation of technologies, promising breakthroughs that will have a lasting impact on the world of computer vision and beyond. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Towards machine learning for heterogeneous inverse scattering in 3D microscopy

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OPTICS EXPRESS 2022年第6期30卷 9854-9868页

作者： Wertheimer, Zsolt-Alon Bar, Chen Levin, Anat Technion Israel Inst Technol Dept Elect Engn Haifa Israel

Light propagating through a nonuniform medium scatters as it interacts with particles with different refractive properties such as cells in the tissue. In this work we aim to utilize this scattering process to learn a volumetric reconstruction of scattering parameters, in particular particle densities. We target microscopy applications where coherent speckle effects are an integral part of the imaging process. We argue that the key for successful learning is modeling realistic speckles in the training process. To this end, we build on the development of recent physically accurate speckle simulators. We also explore how to incorporate speckle statistics, such as the memory effect, in the learning framework. Overall, this paper contributes an analysis of multiple aspects of the network design including the learning architecture, the training data and the desired input features. We hope this study will pave the road for future design of learning based imaging systems in this challenging domain. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： image processing Imaging systems Inverse scattering Speckle noise Speckle reduction Three dimensional microscopy

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