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Automatic Rice Variety Identification System: state-of-the-art review, issues, challenges and future directions

MULTIMEDIA TOOLS AND applications 2023年第18期82卷 27305-27336页

作者： Komal, Ganesh Kumar Sethi, Ganesh Kumar Bawa, Rajesh Kumar Punjabi Univ Patiala India MM Modi Coll Dept Comp Sci Patiala India Punjabi Univ Dept Comp Sci Patiala India

Automatic rice variety identification or quality analysis is a challenging task in image processing and reflects advanced insights into agricultural research with the help of emerging computational technologies. It is the process of identifying the variety of the rice grains by matching them with the training dataset. It is an arduous task because the quality of rice grains is distinct from each other due to the availability of their numerous varieties in the market and unique inherent characteristics. Therefore, customers must identify the superior quality of rice from different available types in the market. This paper demonstrates an exhaustive and transparent perspective on the recent research studies for developing various identification systems using other techniques and a broad view towards this peculiar research area. The paper's main aim is to present in an organized way the related works on identification systems of rice and finally throws exposure on the synthesis analysis based on the research findings. This research study provides valuable and valuable assistance to novice researchers in the agricultural field by amalgamating the studies of various methods and techniques of feature extractions and classification required for automatic variety identification of rice. It is evident from the study that research work carried out on the automated variety identification systems with higher accuracy rates in deep learning using a conjunction of various features of rice is minimal as compared to other techniques and indeed presents a future direction.

关键词： machine learning Neural networks Computer vision Support vector machine Discriminant analysis

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A New Video Summarization Approach Using Object Density Based image Similarity for Smart City applications 29

A New Video Summarization Approach Using Object Density Base...

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29th International Conference on Information Technology, IT 2025

作者： Altundogan, Turan Goktug Karakose, Mehmet Mert, Fatih Manisa Celal Bayar University Computer Engineering Department Manisa Turkey Firat University Computer Engineering Department Elazig Turkey Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Turkey

ISBN: (纸本)9798331517649

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches. © 2025 IEEE.

关键词： image Similarity Object Detection Smart City Video Summarization

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A machine learning model for Alzheimer's disease prediction

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IET CYBER-PHYSICAL SYSTEMS: THEORY & applications 2024年第2期9卷 125-134页

作者： Rani, Pooja Lamba, Rohit Sachdeva, Ravi Kumar Kumar, Karan Iwendi, Celestine Maharishi Markandeshwar Deemed Univ MMICTBM Ambala Haryana India Maharishi Markandeshwar Deemed Univ Maharishi Markandeshwar Engn Coll Elect & Commun Engn Dept Ambala Haryana India Chitkara Univ Chitkara Univ Inst Engn & Technol Dept Comp Sci & Engn Rajpura Punjab India Univ Bolton Sch Creat Technol Bolton England

Alzheimer's disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE-RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE-RF. machine learning algorithms namely Decision tree, XGB, and random forest are used for model building to predict Alzheimer's disease. Experiments are performed in two ways, first on the original dataset and then on class balanced datasets. As the dataset is highly imbalanced, the class imbalance problem is overcome by SMOTE technique. image

关键词： biomedical electronics computer vision feature extraction medical signal processing

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Flotation Froth image Recognition Using vision Transformers 22

Flotation Froth Image Recognition Using Vision Transformers

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Liu, Xiu Aldrich, Chris Curtin Univ Western Australian Sch Mines Minerals Energy & Ch GPOB U1987 Perth WA 6845 Australia Univ Stellenbosch Dept Proc Engn Private Bag 11 ZA-7602 Stellenbosch South Africa

ISBN: (纸本)9781713872344

The application of computer vision systems on industrial flotation plants has benefited considerably from advances in deep learning over the last decade, mostly based on the use of convolutional neural networks and transfer learning. More recently, vision transformers (ViTs) have attracted strong interest since their first appearance in 2017, compared to the popular convolutional neural networks (CNNs). Although becoming well-established in many areas, they have not yet been considered meaningfully in machine vision or signal processing applications in mineral processing, despite the obvious benefits that their application could realize. In this paper, it is demonstrated that ViTs are neural network architectures highly capable of discriminating between different froth flotation images. A customized ViT model and a pretrained ViT model using transfer learning were studied and compared. The former achieved satisfactory performance and the latter achieved near perfect performance, both at a significantly lower computational cost than CNNs. These results suggest that ViTs can be a competitive alternative to CNNs in the advancement of computer vision systems on industrial flotation plants. Copyright (c) 2023 The Authors.

关键词： vision transformer froth flotation froth image analysis deep learning transfer learning transformer

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Stealing the Invisible: Unveiling Pre-Trained CNN Models Through Adversarial Examples and Timing Side-Channels

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2024年第4期14卷 634-646页

作者： Shukla, Shubhi Alam, Manaar Mitra, Pabitra Mukhopadhyay, Debdeep Indian Inst Technol Kharagpur Ctr Computat & Data Sci Kharagpur 721302 India New York Univ Abu Dhabi Ctr Cyber Secur Abu Dhabi U Arab Emirates Indian Inst Technol Kharagpur Comp Sci & Engn Dept Kharagpur 721302 India

machine learning, with its myriad applications, has become an integral component of numerous AI systems. A common practice in this domain is the use of transfer learning, where a pre-trained model's architecture, readily available to the public, is fine-tuned to suit specific tasks. As machine Learning as a Service (MLaaS) platforms increasingly use pre-trained models in their backends, it is crucial to safeguard these architectures and understand their vulnerabilities. In this work, we present ArchWhisperer, a model fingerprinting attack approach based on the novel observation that the classification patterns of adversarial images can be used as a means to steal the models. Furthermore, the adversarial image classifications in conjunction with model inference times is used to further enhance our attack in terms of attack effectiveness as well as query budget. ArchWhisperer is designed for typical user-level access in remote MLaaS environments and it exploits varying misclassifications of adversarial images across different models to fingerprint several renowned Convolutional Neural Network (CNN) and vision Transformer (ViT) architectures. We utilize the profiling of remote model inference times to reduce the necessary adversarial images, subsequently decreasing the number of queries required. We have presented our results over 27 pre-trained models of different CNN and ViT architectures using CIFAR-10 dataset and demonstrate a high accuracy of 88.8% while keeping the query budget under 20. This is a marked improvement compared to state-of-the-art works.

关键词： Graphics processing units Predictive models Computer architecture Computational modeling Analytical models Fingerprint recognition Timing Convolutional neural networks Load modeling Integrated circuit modeling Model extraction attack trustworthy AI systems model fingerprinting adversarial attacks timing side-channel

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Artificial Intelligence for Security and Defence applications ii

Artificial Intelligence for Security and Defence Application...

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Artificial Intelligence for Security and Defence applications ii 2024

ISBN: (纸本)9781510681200

The proceedings contain 42 papers. The topics discussed include: adaption of ai models for processing formal reports in the field of joint ISR;applying deep learning to enhance person detection in maritime images;secure sparse gradient aggregation with various computer-vision techniques for cross-border document authentication and other security applications;bi-modal accuracy distribution in quantization aware training of SNNs - an investigation;an FPGA-based neuromorphic vision system accelerator;visual prompt tuning and ensemble undersampling for one-shot vehicle classification;few-shot multi-label multi-class continuous learning for dark web image categorization;classifying emotions via analysis of facial physiological response without relying on expressions;and context-aware model training for attention-based multi-camera multi-object tracking.

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Polarization image processing Technology Based on machine vision Detection

Polarization Image Processing Technology Based on Machine Vi...

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2023 International Conference on Big Data Mining and Information processing, BDMIP 2023

作者： Liu, Guoyan Zhao, Jincai Zeng, Yanan Wu, Haiyun Li, Zhi Wei, Yong College of Technology Tianjin Agricultural University Tianjin China

ISBN: (纸本)9798400709166

The detection and morphology characterization of these biological samples are the basis of life research. Optical microscopic imaging has great advantages in the characterization and detection of biological samples because of its characteristics of low sample requirements, good environmental adaptability, convenient, fast and non-destructive detection. However, due to the influence of optical diffraction limit, the resolution of optical microscopic imaging method is comparative low and thus limits the applications. Due to the polarization characteristics of the scattering field from scatterer are closely related to the microstructure of the scatterer, the measurement of the polarization characteristics of the scattering field can improve the detection and characterization ability of biological samples. Meanwhile, to achieve a higher resolution and to obtain a larger range of particle near-field scattering spectral distribution, the method in the paper takes the image acquisition part of the traditional machine vision system as the main body and a polarization modulation module is added to build an image acquisition device. The non-intuitive images obtained in the experiment are compared with the traditional direct imaging, and the results prove that the non-intuitive polarization parameter imaging can obtain the particle near-field scattering spectral distribution in a larger range than the traditional direct imaging. It is proved that non-intuitive light wave vectors are more sensitive to near-field scattering and have higher resolution. © 2023 ACM.

关键词： Computer vision

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3rd International Conference on machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd International Conference on machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar Signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC 31

FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC

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2024 International Conference on image processing

作者： Wang, Dayong Yu, Junyi Lu, Xin Dufaux, Frederic Guo, Hongwei Guo, Hui Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Honghe Univ Sch Engn Mengzi Yunnan Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.

关键词： VVC SCC content type fast coding mode decision BDPCM

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GH-Feat: Learning Versatile Generative Hierarchical Features From GANs

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第6期45卷 7395-7411页

作者： Xu, Yinghao Shen, Yujun Zhu, Jiapeng Yang, Ceyuan Zhou, Bolei Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Ant Res Hangzhou 310000 Peoples R China Hong Kong Univ Sci & Technol Sch Comp Sci & Engn Hong Kong Peoples R China Univ Calif Los Angeles Comp Sci Dept Los Angeles CA 90095 USA

Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pre-trained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, etc. We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. Code and models are available at https://***/ghfeat/.

关键词： Task analysis Generators Visualization Generative adversarial networks Feature extraction Training Representation learning Feature learning generative adversarial network generative representation image editing

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