检索结果-内蒙古大学图书馆

Conference on Synthetic Data for Artificial Intelligence and machine Learning - Tools, Techniques, and applications II

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Levanen, Audrey Rynes, Peter L. ThermoAnalytics Inc 23440 Airpk Blvd Calumet MI 49913 USA

ISBN: (纸本)9781510673892;9781510673885

machine learning algorithms are capable of processing image-based scenes, detecting and recognizing embedded targets. This has been demonstrated by data scientists and computer vision engineers, but performant algorithms must be robustly trained to successfully complete such a complex task. This typically requires a large set of training data on which the algorithm can base statistical predictions. Electro-optical infrared (EO/IR) remote sensing applications necessitate a substantial image database with suitable variation for adept learning to occur. For human detection/recognition applications diversity in clothing ensembles, pose, season, times of day, sensor platform perspectives, scene backgrounds and weather conditions can be included in training image sets to ensure sufficient input variety. However, acquiring such a diverse image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR). Alternatively, generating synthetic imagery with appropriate features is possible and has been shown to perform well, but a careful methodology must be followed if robust training is to be accomplished. In this work, MuSES and CoTherm are used to generate synthetic EO/IR remote sensing imagery of various human dismounts with a range of clothing, poses and environmental factors. The performance of a YOLO ("you only look once") deep learning algorithm is studied, and sensitivity conclusions are discussed.

关键词： EO/IR scene simulation machine learning artificial intelligence target signature automated image generation signature prediction deep learning EO/IR signature prediction

来源：评论

学校读者我要写书评

暂无评论

Hardware Implementation of machine vision System for Component Detection 7th

Hardware Implementation of Machine Vision System for Compone...

引用

7th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2022

作者： Smruthi, P. Prajna, K.B. John, Jibin G. Pasha, Aslam Taj VLSI and Embedded Systems Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru India Research and Technology Development Group ACE Designers Ltd Bengaluru India

ISBN: (纸本)9789811954818

Inspection of components using machine vision technologies provides solution for quality and process control. This technique is used in various applications such as automotive, pharmaceutical, food and beverage, electronics, packages, process control and special application. The basic idea is to use machine vision technology in the industries to improve the production quality, reduce the scrap product due to non-conformity by controlling the manufacturing process through machine vision and also to prevent the value addition for scrap product in the subsequent stage of manufacturing process. Thus, designing and developing a machine vision system for component identification used the image processing algorithm. Inside the component, small circles which are of 0.5 diameter are detected using Hough circle detection. Graphical user interface was developed for visual representation of the image processing. The training model of the system is developed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Unveiling the vision: A Comprehensive Review of Computer vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

引用

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, Computer vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Generative Artificial Intelligence for Hyperspectral Sensor Data: A Review

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution machine learning Neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

来源：评论

学校读者我要写书评

暂无评论

Scenario-Based Synthetic Data Generation for an AI-based System Using a Flight Simulator

Scenario-Based Synthetic Data Generation for an AI-based Sys...

引用

AIAA SciTech Forum

作者： Sprockhoff, Jasper Gupta, Siddhartha Durak, Umut Krueger, Thomas German Aerosp Ctr DLR Inst Flight Syst Braunschweig Germany German Aerosp Ctr DLR Inst AI Safety & Security St Augustin Germany

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

In recent years, algorithms based on machine learning have significantly advanced many technical areas, including computer vision. Since the performance of machine learning applications is data-dependent, a sufficient amount of high-quality data must be available to achieve robust and stable performance. However, the collection of large amounts of real-world data that covers the operational parameters of the AI-based system is often a difficult task because of availability, cost, or even potential danger. Therefore, synthetic data generation is often used to supplement data sets with additional required data samples. In this paper, we propose a baseline for an automated toolchain to generate synthetic image data of aircraft for machine-learning computer vision applications using a flight simulator. Scenario-based approaches have shown applicability to systematically generate valid test cases for system safety evaluation. We leverage a similar approach to generate data for training of AI-based systems. Our approach requires the user to create scenario models using our modelling tool. These models define the operational ranges for a set of parameters that characterize executable scenarios. The scenarios defined by the models are used to automatically produce images from simulations carried out with the FlightGear open-source flight simulator. We distinguish between a static and a dynamic simulation approach. The static approach generates a sequence of independent scenes, while the dynamic approach creates situations that mimic a collision avoidance scenario. With our approach, we can automatically generate large amounts of raw image data covering the relevant parameter ranges based on the models created by the user.

关键词： Flight Simulators machine Learning Computer vision Collision Avoidance System System Entity Structure Aviation Flight Planning Python Generative Adversarial Network Natural Language processing

来源：评论

学校读者我要写书评

暂无评论

SC-LSTM based Human Action Recognition

SC-LSTM based Human Action Recognition

引用

9th International Conference on Signal and image processing (ICSIP)

作者： Kang, Naixin Yi, Pengfei Geng, Qingshu Dong, Jing Liu, Rui Wang, Ling Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China

ISBN: (纸本)9798350350920

Human Action Recognition is one of the most applied research directions in the field of Computer vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human Action Recognition machine Learning Computer vision Human-Computer Interaction Security

来源：评论

学校读者我要写书评

暂无评论

LeCA: In-Sensor Learned Compressive Acquisition for Efficient machine vision on the Edge 23

LeCA: In-Sensor Learned Compressive Acquisition for Efficien...

引用

50th Annual International Symposium on Computer Architecture (ISCA)

作者： Ma, Tianrui Boloor, Adith Jagadish Yang, Xiangxing Cao, Weidong Williams, Patrick Sun, Nan Chakrabarti, Ayan Zhang, Xuan Washington Univ St Louis MO 63110 USA Univ Texas Austin Austin TX USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400700958

With the rapid advances of deep learning-based computer vision (CV) technology, digital images are increasingly consumed, not by humans, but by downstream CV algorithms. However, capturing high-fidelity and high-resolution images is energy-intensive. It not only dominates the energy consumption of the sensor itself (i.e. in low-power edge devices), but also contributes to significant memory burdens and performance bottlenecks in the later storage, processing, and communication stages. In this paper, we systematically explore a new paradigm of in-sensor processing, termed "learned compressive acquisition" (LeCA). Targeting machine vision applications on the edge, the LeCA framework exploits the joint learning of a sensor autoencoder structure with the downstream CV algorithms to effectively compress the original image into low-dimensional features with adaptive bit depth. We employ column-parallel analog-domain processing directly inside the image sensor to perform the compressive encoding of the raw image, resulting in meaningful hardware savings, and energy efficiency improvements. Evaluated within a modern machine vision processing pipeline, LeCA achieves 4x, 6x, and 8x compression ratios prior to any digital compression, with minimal accuracy loss of 0.97%, 0.98%, and 2.01% on imageNet, outperforming existing methods. Compared with the conventional full-resolution image sensor and the state-of-the-art compressive sensing sensor, our LeCA sensor is 6.3x and 2.2x more energy-efficient while reaching a 2x higher compression ratio.

关键词： CMOS image sensor image compression autoencoder

来源：评论

学校读者我要写书评

暂无评论

Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

引用

machine vision AND applications 2024年第4期35卷 75-75页

作者： Kirupakaran, Anish Monsley Laskar, Rabul Hussain Natl Inst Technol Silchar Dept ECE Cachar 788010 Assam India

Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted with case-sensitive characters in the English alphabet. The literature underscores a notable gap, the absence of an open-source multi-scale un-instructional gesture database featuring a comprehensive dictionary. In response, we have created the NITS (gesture scale) database, which encompasses isolated mid-air gesticulations of ninety-five alphanumeric characters. In this research, we present a scale-centric framework that addresses three critical aspects: (1) detection of smaller gesture objects: our framework excels at detecting smaller gesture objects, such as a red color marker. (2) Removal of redundant self co-articulated strokes: we propose an effective approach to eliminate redundant self co-articulated strokes often present in gesture trajectories. (3) Scale-variant approach for recognition: to tackle the scale vs. size ambiguity in recognition, we introduce a novel scale-variant methodology. Our experimental results reveal a substantial improvement of approximately 16% compared to existing state-of-the-art recognition models for mid-air gesture recognition. These outcomes demonstrate that our proposed approach successfully emulates the perceptibility found in the human visual system, even when utilizing data from monophthalmic vision. Furthermore, our findings underscore the imperative need for comprehensive studies encompassing scale variations in gesture recognition.

关键词： Gesture computing Computer vision Deep learning Scale variance Human-computer interaction

来源：评论

学校读者我要写书评

暂无评论

Unsupervised neural network-based image restoration framework for pattern fidelity improvement and robust metrology

引用

JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 2023年第3期22卷

作者： Du, Zijian Pu, Lingling Wei, Paul Yuan, Rui Kim, Jeeeon Tan, Jiaoying ASML Silicon Valley San Jose CA 95131 USA

Background: Scanning electron microscope (SEM) images acquired by E-beam tools for inspection and metrology applications are usually degraded by blurring and additive noises. Blurring sources include the intrinsic point spread function of optics, lens aberration, and potential motion blur caused by the wafer stage movements during the image acquisition process. Noise sources include shot noise, quantization noise, and electronic read-out noise. image degradation caused by blurring and noise usually leads to noisy, inaccurate metrology results. For low-dosage metrology applications, metrology algorithms often fail to obtain successful measurements due to elevated levels of blurring and noise. image restoration and enhancement are necessary as preprocessing steps to obtain meaningful metrology results. Initial success was obtained by applying neural network-based framework to drastically improve image quality and metrology precision as is demonstrated in the previous ***: We aim to provide more details on the neural network model architecture, model regularization, and training dynamics to better understand the model's behavior. We also analyze the effect of image restoration on key metrology performances such as line edge roughness and mean critical dimension of the ***: Non-machine learning-based image quality enhancement methods fail to restore low-quality SEM images to a satisfactory degree. More recent convolutional neural networks and vision transformer-based, supervised deep learning models have achieved superior performance in various low-level image processing and computer vision tasks. Nevertheless, they require a huge amount of training data that contain high-quality ground truth images. Unfortunately, high-quality ground truth images for low-dosage SEM images do not exist. Instead, we use self-supervised U-Net combined with a fully connected network (FCN) to recover low-dosage images without the need for ground truth training images. The

关键词： deep learning U-Net convolutional neural networks fully connected network image restoration deblurring self-supervised model generative modeling metrology

来源：评论

学校读者我要写书评

暂无评论

Towards stronger illumination robustness of local feature detection and description based on auxiliary learning

引用

SIGNAL image AND VIDEO processing 2024年第SUPPL 1期18卷 575-584页

作者： Bian, Houqin Fan, Shihe Zhang, Haolin Qin, Lunming Cui, Haoyang Wang, Xi Shanghai Univ Elect Power Coll Elect & Informat Engn Shanghai 201306 Peoples R China Xi An Jiao Tong Univ Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intell Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Local feature detection and description play a crucial role in various computer vision tasks, including image matching. Variations in illumination conditions significantly affect the accuracy of these applications. However, existing methods inadequately address this issue. In this paper, a novel algorithm based on illumination auxiliary learning module (IALM) is introduced. Firstly, a new local feature extractor named illumination auxiliary Superpoint (IA-Superpoint) is established, based on the integration of IALM and Superpoint. Secondly, illumination-aware auxiliary training focuses on capturing the effects of illumination variations during feature extraction through tailored loss functions and jointly learning mechanisms. Lastly, in order to evaluate the illumination robustness of local features, a metric is proposed by simulating various illumination disturbances. Experiments on HPatches and RDNIM datasets demonstrate that the performance of local feature extraction is greatly improved by our method. Compared to the baseline method, the proposed method exhibits improvements in both mean matching accuracy and homography estimation.

关键词： Deep learning methods Local feature Auxiliary learning image matching

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：