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Generative Artificial Intelligence for Hyperspectral Sensor Data: A Review

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

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Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 229卷

作者： Paul, Nijhum Sunil, G. C. Horvath, David Sun, Xin North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58108 USA North Dakota State Univ Genom Phen & Bioinformat Program Fargo ND 58108 USA USDA ARS ETSARC Weed & insect Biol unit Fargo ND 58102 USA

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a thorough overview of the state-of-the-art DL technologies for plant stress detection. For this purpose, a systematic literature review was conducted to identify relevant articles for highlighting the technologies and approaches currently employed in the development of a DL-based plant stress detection system, specifically the advancement of image-based data collection systems, image preprocessing techniques, and deep learning algorithms and their applications in plant stress classification, disease detection, and segmentation tasks. Additionally, this review emphasizes the challenges and future directions in collecting and preprocessing image data, model development, and deployment in real-world agricultural settings. Some of the key findings from this review paper are: Training data: (i) Most plant stress detection models have been trained on Red Green Blue (RGB) images;(ii) Data augmentation can increase both the quantity and variation of training data;(iii) Handling multimodal inputs (e. g., image, temperature, humidity) allows the model to leverage information from diverse sources, which can improve prediction accuracy;Model Design and Efficiency: (i) Self-supervised learning (SSL) and Few-shot learning (FSL)-based methods may be better than transfer learning (TL)-based models for classifying plant stress when the number of labeled training images are scarce;(ii) Custom designed DL architectures for a specific stress and plant type can have better performance than the state-of-the-art DL architectures in terms of efficiency, overfitting, and accuracy;(iii) The multi-task learning DL structure reuses most of the network architecture while performing multiple tasks (e.g., estimate stress type and severity) simultaneously, which makes the learning much

关键词： Plant stress Computer vision Deep learning image processing Imaging sensor Precision agriculture

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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

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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)

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Training a shadow removal network using only 3D primitive occluders

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VISUAL COMPUTER 2025年第4期41卷 2339-2376页

作者： Gallego, Neil Patrick Del Ilao, Joel Cordel, Macario, ii Ruiz Jr, Conrado De La Salle Univ 2401 Taft Ave Malate Manila 1004 Metro Manila Philippines De Le Salle Univ Graph Animat Multimedia & Entertainment GAME Lab Manila Philippines De La Salle Univ Ctr Automat Res Manila Philippines De La Salle Univ Dr Andrew L Tan Data Sci Inst Manila Philippines La Salle Univ Ramon Llull Human Environm Res HER Grp Barcelona 08022 Spain

Removing shadows in images is often a necessary pre-processing task for improving the performance of computer vision applications. Deep learning shadow removal approaches require a large-scale dataset that is challenging to gather. To address the issue of limited shadow data, we present a new and cost-effective method of synthetically generating shadows using 3D virtual primitives as occluders. We simulate the shadow generation process in a virtual environment where foreground objects are composed of mapped textures from the Places-365 dataset. We argue that complex shadow regions can be approximated by mixing primitives, analogous to how 3D models in computer graphics can be represented as triangle meshes. We use the proposed synthetic shadow removal dataset, DLSUSynthPlaces-100K, to train a feature-attention-based shadow removal network without explicit domain adaptation or style transfer strategy. The results of this study show that the trained network achieves competitive results with state-of-the-art shadow removal networks that were trained purely on typical SR datasets such as ISTD or SRD. Using a synthetic shadow dataset of only triangular prisms and spheres as occluders produces the best results. Therefore, the synthetic shadow removal dataset can be a viable alternative for future deep-learning shadow removal methods. The source code and dataset can be accessed at this link: https://***/SynthShadowRemoval/.

关键词： Shadow removal Deep neural network Synthetic images Computer graphics image processing

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Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics applications

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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

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

Authentication and Verification in Human-Robot Cooperative R...

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

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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

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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

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

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

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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

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machine learning-based augmented vision for detecting driver drowsiness

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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

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An Experimental Evaluation of LLM on image Classification 35th

An Experimental Evaluation of LLM on Image Classification

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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

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