检索结果-内蒙古大学图书馆

Momentum-Space Tunable Metasurfaces for Switchable image processing

ADVANCED OPTICAL MATERIALS 2025年

作者： Zhang, Kai Wang, Shuo Qiu, Jumin Yang, Muyi Liu, Tingting Xiao, Shuyuan Staude, Isabelle Pertsch, Thomas Wang, Yu Zou, Chengjun Chinese Acad Sci Inst Microelect Beitucheng West Rd 3 Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China Nanchang Univ Sch Phys & Mat Sci Nanchang 330031 Peoples R China Friedrich Schiller Univ Jena Inst Solid State Phys D-07743 Jena Germany Friedrich Schiller Univ Jena Abbe Ctr Photon D-07745 Jena Germany Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Nanchang Univ Inst Adv Study Nanchang 330031 Peoples R China

The exceptional ability of optical metasurfaces to manipulate light has enabled integrated analog computing and image processing in ultracompact, energy-efficient platforms that support high speeds. To date, metasurfaces have demonstrated various analog processing functions, including differentiation, convolution, and classification. However, a fundamental limitation of existing designs is their static functionality, which restricts adaptability to diverse application scenarios. To address this challenge, momentum-space reconfigurable metasurfaces operating in the near-infrared range are experimentally demonstrated, capable of switchable image processing functions including image differentiation and bright-field imaging. These meta-devices are achieved by integrating nematic liquid crystals with silicon metasurfaces that support resonances of quasi-bound states in the continuum (quasi-BICs). The quasi-BIC modes enable further design freedom over the angular dispersion of metasurfaces. The results showcase an electrically tunable, CMOS-compatible approach to reconfigurable optical computing, offering significant potential for applications such as online training of diffractive neural networks, machine vision, and augmented reality.

关键词： dielectric metasurfaces liquid crystals momentum-space tunable metasurfaces optical image processing

来源：评论

学校读者我要写书评

暂无评论

Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

6th IEEE International Conference on image processing, applications and Systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Comparative Study on Evaluating the Performance of Automated Bacterial Colony Counting with Available APP and Software on Generated image Dataset

引用

SN Computer Science 2025年第4期6卷 1-16页

作者： Arora, Prachi Tewary, Suman Krishnamurthi, Srinivasan Kumari, Neelam School of Computing Indian Institute of Information Technology Una (IIIT-Una) Una 177209 India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad 201002 India Thin Film Coating Facility CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Materials Science and Sensor Applications CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Advanced Materials and Processes Division CSIR-National Metallurgical Laboratory (CSIR-NML) Jamshedpur 831007 India MTCC-Gene bank CSIR-Institute of Microbial Technology (CSIR-IMTECH) Sector 39-A Chandigarh 160039 India

Recent developments in image analysis and interpretation using computer vision techniques have shown potential for novel applications in microbiology laboratories to support the task of automation, aiming for faster and more reliable detection. image processing techniques and machine learning models can be valuable tools in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories for further analysis. In this context, creating a dataset of different bacterial strain images is a fundamental objective for developing and improving the accuracy of image processing models. Therefore, this manuscript acquired a dataset of water samples with different bacterial strain images on a petri dish following a standardized process with controlled conditions of positioning and lighting. The image acquisition device was also developed with a light-emitting diode (LED) and diffuser as a lighting source and a smartphone camera with 16 MP resolution. In addition, the present manuscript also focuses on comparing the accuracy of the proposed algorithm with the available apps and software using the custom-built imaging device. Hence, the resulting dataset consists of 100 images, which is helpful for researchers working in image processing to develop an algorithm for automated counting of bacterial colonies on petri dishes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bacterial colonies image datasets image processing Imaging device Segmentation Water samples

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-based Rice Variety Classification: A State-of-the-art Review and Future Directions

引用

SMART AGRICULTURAL TECHNOLOGY 2025年 10卷

作者： Islam, Md. Masudul Himel, Galib Muhammad Shahriar Moazzam, Md. Golam Uddin, Mohammad Shorif Jahangirnagar Univ Dept Comp Sci & Engn Dhaka Bangladesh Jahangirnagar Univ Dept Phys Dhaka Bangladesh Univ Sains Malaysia Sch Comp Sci USM Penang 11800 Malaysia

Rice is a staple food for a significant portion of the global population, making accurate classification of rice varieties essential for farming and consumer protection. This review provides a focused analysis of the current advancements and challenges in applying computer vision (CV) techniques to rice variety classification. The study examines key steps in the automation process, including image acquisition, pre-processing, feature extraction, and classification algorithms, with particular emphasis on machine learning and deep learning methods such as Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in recent research. However, practical implementation faces challenges, including the availability of high-quality datasets, the impact of environmental variations on image quality, and the computational demands of complex models. Our study discusses these obstacles and highlights the importance of developing resilient and scalable systems for real-world applications. By synthesizing findings from various studies, this review proposes future directions for advancing rice variety classification, focusing on improved feature extraction techniques, enhanced dataset management, and integrating innovative machine learning paradigms. This work is a valuable resource for researchers and practitioners aiming to advance rice classification technologies and contribute to food security and agricultural sustainability.

关键词： Rice varieties classification image classification Agriculture Computer vision Systematic Review

来源：评论

学校读者我要写书评

暂无评论

machine learning-based augmented vision for detecting driver drowsiness

引用

Multimedia Tools and applications 2025年 1-23页

作者： Ramarao, Gude Soni, Palangthod Vaishnavi, Agraharam Sri VenkataVarshitha Department of Electronics and Communication Engineering G.Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India

Fatigued drivers often cause traffic accidents. This study introduces a novel method for detecting fatigue that combines machine learning and image processing techniques. We propose a unique approach that utilizes the Haar Cascade method, the CatBoost algorithm, and an Inception V3 for facial detection and eye classification, allowing for the quick identification and management of drowsiness-related issues. The proposed method uses Haar Cascade for facial detection, proving more reliable than CNN-based methods. A Python-based program detects the real-time images of the driver’s face using OpenCV, while deep learning models built on Keras analyze the facial data. The CNN is trained to distinguish between open and closed eyes, aiding in detecting fatigue. When prolonged eye closure is detected, drivers receive immediate advice to stop or take a break. This effort aims to create a fatigue detection system that is both reliable and robust, capable of swiftly identifying prolonged eye closure. Ultimately, our method has the potential to improve road safety significantly and contribute to global initiatives aimed at addressing this critical issue by providing early warnings to drivers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

引用

6th IEEE International Conference on image processing, applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 IEEE.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

An Experimental Evaluation of LLM on image Classification 35th

An Experimental Evaluation of LLM on Image Classification

引用

35th Australasian Database Conference

作者： Wu, Jiaxuan Tang, Xushuo Yang, Zhengyi Hao, Kongzhang Lai, Longbin Liu, Yongfei Univ Calif Irvine Irvine CA 92697 USA Euler AI Sydney NSW Australia Univ New South Wales Sydney NSW Australia Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819612413;9789819612420

image classification is one of the fundamental tasks in computer vision (CV) and has numerous practical applications. Traditionally, machine learning and deep learning methods such as k-Nearest Neighbors (kNN), decision trees, and Convolutional Neural Networks (CNN) have been widely used to perform this task. However, with the recent emergence of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), originally designed for natural language processing, their cross-domain applications, including in CV, are now being explored. In this paper, we investigate the capabilities of GPT-4o, a variant of the GPT model, for image classification on the Fashion-MNIST dataset. By using carefully designed prompts, we evaluate GPT-4o's performance and compare it with more traditional models. Our study offers insights into the cross-domain potential of GPT models, explores how prompt engineering can enhance GPT's performance on image classification tasks, and suggests new avenues for developing more flexible and adaptable multimodal LLM systems. The code can be found at https://***/Tanghaha1424/gpt-fashionmnist.

关键词： LLM ChatGPT image Classification Fashion-MNIST

来源：评论

学校读者我要写书评

暂无评论

MANTIS: A Mixed-Signal Near-Sensor Convolutional imager SoC Using Charge-Domain 4b-Weighted 5-to-84-TOPS/W MAC Operations for Feature Extraction and Region-of-Interest Detection

引用

IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年第3期60卷 934-948页

作者： Lefebvre, Martin Bol, David Catholic Univ Louvain Inst Informat & Commun Technol Elect & Appl Math B-1348 Louvain La Neuve Belgium Delft Univ Technol Dept Microelect NL-2628 CD Delft Netherlands

Recent advances in artificial intelligence (AI) have prompted the search for enhanced algorithms and hardware to support the deployment of machine learning (ML) at the edge. More specifically, in the context of the Internet of Things (IoT), vision chips must be able to fulfill the tasks of low to medium complexity, such as feature extraction (FE) or region-of-interest (RoI) detection, with a sub-mW power budget imposed by the use of small batteries or energy harvesting. Mixed-signal vision chips relying on in-or near-sensor processing have emerged as an interesting candidate because of their favorable tradeoff between energy efficiency (EE) and computational accuracy compared with digital systems for these specific tasks. In this article, we introduce a mixed-signal convolutional imager system-on-chip (SoC) codenamed MANTIS, featuring a unique combination of large 16 x 16 4b-weighted filters, operation at multiple scales, and double sampling, well suited to the requirements of medium-complexity tasks. The main contributions are (i) circuits called DS3 units combining delta-reset sampling (DRS), image downsampling (DS), and voltage downshifting and (ii) charge-domain multiply-and-accumulate (MAC) operations based on switched-capacitor (SC) amplifiers and charge sharing in the capacitive DAC of the successive-approximation (SAR) ADCs, MANTIS achieves peak EEs normalized to 1b operations of 4.6 and 84.1 TOPS/W at the accelerator and SoC levels, while computing feature maps (fmaps) with a root-mean-square error (RMSE) ranging from 3 to 11.3%. It also demonstrates a face RoI detection with a false negative rate (FNR) of 11.5%, while discarding 81.3% of image patches and reducing the data transmitted off chip by 13 x compared with the raw image.

关键词： Charge domain CMOS image sensor (CIS) convolutional neural network (CNN) feature extraction (FE) mixed signal multiply-and-accumulate (MAC) operations near sensor region-of-interest (RoI) detection system-on-chip (SoC) Charge domain CMOS image sensor (CIS) convolutional neural network (CNN) feature extraction (FE) mixed signal multiply-and-accumulate (MAC) operations near sensor region-of-interest (RoI) detection system-on-chip (SoC)

来源：评论

学校读者我要写书评

暂无评论

Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics applications

引用

IEEE ACCESS 2025年 13卷 67564-67577页

作者： Rustler, Lukas Volprecht, Vojtech Hoffmann, Matej Czech Tech Univ Fac Elect Engn Dept Cybernet Prague 12135 Czech Republic

Depth sensing is an essential technology in robotics and many other fields. Many depth sensing (or RGB-D) cameras are available on the market and selecting the best one for your application can be challenging. In this work, we tested four stereoscopic RGB-D cameras that sense the distance by using two images from slightly different views. We empirically compared four cameras (Intel RealSense D435, Intel RealSense D455, StereoLabs ZED 2, and Luxonis OAK-D Pro) in three scenarios: (i) planar surface perception, (ii) plastic doll perception, (iii) household object perception (YCB dataset). We recorded and evaluated more than 3,000 RGB-D frames for each camera. For table-top robotics scenarios with distance to objects up to one meter, the best performance is provided by the D435 camera that is able to perceive with an error under 1 cm in all of the tested scenarios. For longer distances, the other three models perform better, making them more suitable for some mobile robotics applications. OAK-D Pro additionally offers integrated AI modules (e.g., object and human keypoint detection). ZED 2 is overall the best camera which is able to keep the error under 3 cm even at 4 meters. However, it is not a standalone device and requires a computer with a GPU for depth data acquisition. All data (more than 12,000 RGB-D frames) are publicly available at https://***/rgbd-comparison

关键词： Cameras Robot vision systems Robot sensing systems Sensors Robots Graphics processing units Accuracy Stereo image processing Three-dimensional printing Plastics Depth sensing Intel RealSense Luxonis OAK-D Pro ZED 2 object detection RGB-D segmentation

来源：评论

学校读者我要写书评

暂无评论

Wild Animal Detection System on a Farm Using image processing

Wild Animal Detection System on a Farm Using Image Processin...

引用

International Conference on Advances and applications of Artificial Intelligence and machine Learning, ICAAAIML 2023

作者： Barhate, Madhuri Gulhane, Nandini Gunjal, Tanvi Gudi, Swaroop Gulhane, Vedant Wagade, Prasad Gude, Ayush Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819795062

Human civilization is based on agriculture, and one of the biggest threats to agricultural productivity is the existence of wild animals on farmlands. Animals are taking over some farmlands, causing significant crop losses, property damage, and livestock risks. As a result, suitable solutions must be developed. A system to support farmers is necessary because the conventional method of constant guard monitoring is impractical. Modern technologies like machine learning, computer vision, and image processing are used by this wild animal detection system to effectively manage conflicts between humans and wildlife. machine learning and computer vision rely on the integration of particular algorithms to guarantee the accuracy and effectiveness of the system. With an emphasis on sustainability, the technology protects property and crops while also aiding in the conservation of wildlife. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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：