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Fλ/d as a predictor of image classification algorithm performance

APPLIED OPTICS 2025年第4期64卷 845-854页

作者： Hixson, Jonathan g. Teaney, Brian Finch, Michael f. Nehmetallah, George Driggers, Ronald US Army Combat Capabil Dev Command DEVCOM C5ISR Ctr RTI Aberdeen Proving Ground NY 21005 USA Catholic Univ Amer EECS Dept 620 Michigan Ave NE Washington DC 20064 USA Univ Arizona Wyant Coll Opt Sci Infrared Syst Grp Tucson AZ 85721 USA

This research studied the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of machine learning algorithms such as the YOLO (You Only Look Once) algorithm for object classification. The YOLO_v3 and YOLO_v10 algorithms were trained using static imagery provided in the commonly available training dataset provided by Teledyne FLIR systems. image processing techniques were used to degrade image quality of the test dataset also provided by Teledyne FLIR systems, simulating detector-limited to optics-limited performance, which results in a variation of the FLD metric between 0.339 and 7.98. The degraded test set was used to evaluate the performance of YOLO_v3 and YOLO_v10 for object classification and relate the FLD metric to the probability of detection. Results of YOLO_v3 and YOLO_v10 are presented for the varying levels of image degradation. A summary of the results is discussed along with recommendations for evaluating an algorithm's performance using a sensor's FLD metric value. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： image quality image sensors Imaging systems Imaging techniques Machine learning Sensor performance

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Quantum Variational Singular Value Decomposition: A new hybrid approach towards quantum image compression 17

Quantum Variational Singular Value Decomposition: A new hybr...

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17th International conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Britant Tiwari, Abhishek CDAC Uttar Pradesh Noida India

ISBN: (纸本)9798331531195

This research paper explores the application of singular value decomposition (SVD) in quantum image processing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD is a powerful mathematical tool in image processing, used for tasks such as image compression, noise reduction, and feature extraction. In this study, we propose quantum variational singular value decomposition (QVSVD) based on the variational quantum deflation (VQD) algorithm to determine the eigenvalues that contributes in calculating the singular values of the image matrix and eventually doing the SVD. This facilitates the extraction of eigenvalues with exponential speedup on real hardware compared to classical methods. We detail the implementation of this quantum algorithm within the framework of QIP, highlighting its advantages in terms of computational efficiency and accuracy on a Fault-Tolerant Quantum Computing (FTQC). Furthermore, we present a comparative analysis of the quantum and the classical method of SVD, demonstrating the accuracy of the image data. The simulation results validate the theoretical advantages of using quantum algorithms for SVD in image processing. This work not only underscores the potential of quantum computing in enhancing image processing techniques but also sets the stage for future research in the field, exploring more complex image processing tasks and other quantum algorithms. Our findings suggest that quantum image processing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing. © 2025 IEEE.

关键词： Noise abatement

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： image compression

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Solving Quadratic systems With Full-Rank Matrices Using Sparse or Generative Priors

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IEEE TRANSACTIONS ON SIGNAL processing 2025年 73卷 477-492页

作者： Chen, Junren Ng, Michael K. Liu, Zhaoqiang Univ Hong Kong Dept Math Hong Kong 852 Peoples R China Hong Kong Baptist Univ Dept Math Hong Kong 852 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

The problem of recovering a signal x is an element of R-n from a quadratic system{yi=x(T)A(ix), i=1,...,m} with full-rank matrices A(i) frequently arises in applications such a sun assigned distance geometry and sub-wavelength imaging. With i.i.d. standard Gaussian matrices A(i) , this paper addresses the high-dimensional case where m << n by incorporating prior knowledge of x. First, we consider a k-sparse x and introduce the thresholded Wirtinger flow (TWF) algorithm that does not require the sparsity level k. TWF comprises two steps: the spectral initialization that identifies a point sufficiently closetox (up to a sign flip) when m=O(k(2)logn),and the thresholded gradient descent which, when provided a good initialization, produces a sequence linearly converging to x with m=O(k log n) measurements. Second, we explore the generative prior, assuming that x lies in the range of an L-Lipschitz continuous generative model with k-dimensional inputs in an l(2)-ball of radius r. With an estimate correlated with the signal, we develop the projected gradient descent (PGD)algorithm that also comprises two steps: the projected power method that provides an initial vector with O(root k log(L)/m) l(2)-error given m=O( k log(Lnr))measurements, and the projected gradient descent that refines the l(2)-error to O(delta) at a geometric rate when m=O(k log Lrn/delta(2)). Experimental results corroborate our theoretical findings and show that: (i)our approach for the sparse case notably outperforms the existing provable algorithm sparse power factorization;(ii) leveraging the generative prior allows for precise image recovery in the MNIST dataset from a small number of quadratic measurements.

关键词： Sparse matrices Complexity theory Signal processing algorithms Vectors Phase measurement Compressed sensing Standards Particle measurements image segmentation image reconstruction Generative prior projected gradient descent projected power method quadratic system sparse prior wirtinger flow

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A Comprehensive Survey of Methods for Identifying Counterfeit Banknotes Using image processing and Machine Learning 6th

A Comprehensive Survey of Methods for Identifying Counterfei...

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6th International conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Sharmila, B. Sowmiya, A. Mary, Teena Sandeep, J. Sreeja, C.S. CHRIST University Dharmaram College Post Hosur Road Karnataka Bengaluru560029 India

ISBN: (纸本)9789819783281

The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on image processing techniques and machine learning algorithms are helpful in detecting counterfeit notes. This survey paper reviews the current strategies on fake banknote detection using image processing techniques and machine learning algorithms. We discuss various stages of the detection process, including image acquisition, preprocessing, feature extraction and classification. Furthermore, we analyze the limitations and comparative performance of different algorithms and approaches mentioned in the literature. The survey aims to provide insights into the various methodologies, challenges and future directions in the field of fake banknote detection, facilitating the development of more robust and effective counterfeit detection systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Realization of Digital Protection Algorithm of National Traditional Culture Based on Virtual Reality Technology

Realization of Digital Protection Algorithm of National Trad...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Hongxia Hou, Lanbao School of Literature and Media Jingchu University of Technology Jingmen448000 China School of Mathematics and Physics Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

National traditional culture and traditional technology are the products of historical precipitation and indispensable precious resources. The establishment of national cultural database is of great significance for the protection and inheritance of cultural heritage. Aiming at the specific functional requirements of traditional cultural digital protection system for image segmentation and retrieval, this paper focuses on the common algorithms of image segmentation and feature matching. This paper analyzes and compares the applicability and processing effect of each algorithm in ethnic patterns. EGBIS image segmentation algorithm and shape context feature matching algorithm are used to achieve high precision image processing. This paper designs and implements a national culture retrieval system that meets the application needs. Combined with the image characteristics of ethnic patterns and the application requirements of the system, this paper defines the concepts of source images, primitives and hyperprimitives, and establishes a relational database based on them. This study has laid a certain theoretical foundation for the study of digital inheritance of national culture. © 2025 SPIE.

关键词： image segmentation image retrieval Databases Feature extraction image processing algorithms and systems image processing Visualization Distance measurement Virtual reality Visual analytics

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Medical image Segmentation Techniques: Advances and Challenges 6

Medical Image Segmentation Techniques: Advances and Challeng...

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6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Boulehmi, Hela Filali, Rania Linda DGET ISET de Bizerte Bizerte Tunisia

ISBN: (纸本)9798331506520

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging. © 2025 IEEE.

关键词： Convolutional neural networks

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

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

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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

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Robust Multilayer Matrix Factorization for Blind Hyperspectral image Unmixing 16

Robust Multilayer Matrix Factorization for Blind Hyperspectr...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Yang, Gang Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9781510689251

Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing applications. In this paper, we propose a novel robust multilayer matrix factorization (RMLMF) approach for hyperspectral image unmixing. Firstly, instead of sampling all observed pixels, the endmembers are modeled as a linear combination of candidate endmembers to reduce the redundant spectra and alleviate heavy computational cost. Secondly, to explore the hidden information, the shallow decomposition is extended into multilayer structure. Furthermore, considering that hyperspectral images (HSIs) still suffer from a variety of noises, L2,1-norm is employed to measure the reconstruction error of the multilayer structure. Experimental results on both synthetic and real data sets demonstrate the superiority of the proposed RMLMF over many state-of-the-art algorithms. © 2025 SPIE.

关键词： Matrix factorization

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