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Harnessing machine vision and image processing in multicolor lateral flow assays for cutting-edge liquid biopsy applications

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SENSORS AND ACTUATORS B-CHEMICAL 2025年 437卷

作者： Lamprou, Eleni Kokkinis, Athanasios Kalligosfyri, Panagiota M. Koustoumpardis, Panagiotis N. Kalogianni, Despina P. Univ Patras Dept Chem GR-26504 Rion Patras Greece Univ Patras Dept Mech Engn & Aeronaut Robot Grp GR-26504 Rion Patras Greece

Lateral flow assays (LFAs) are important diagnostic tools with numerous applications in various scientific fields, including diagnostics, medicine, analytical chemistry, biochemistry, environmental and food science. Artificial Intelligence (AI) and image processing tools are the state-of-the-art technology in analytical tools, especially in Point-of-Care (POC) devices that improve the detection efficiency without the need for highly qualified personnel. In this context, we have developed novel multicolor LFAs exploiting machine vision and image analysis tools for the automated "reading" of the visual result of LFAs using beads of different colors as reporters to distinguish between multiple targets. The system consists of a multicolor test integrated with a mobile/ smartphone and a web application for the automatic interpretation of the results. The use of multicolor beads, relating each color to a specific target, enhanced image analysis-based discrimination of the tests between different targets. The developed diagnostic tool has been applied to cutting-edge liquid biopsy applications which include the detection of three different microRNA molecules spiked in urine samples. The developed integrated system has been successfully applied to a series of real samples, advancing the field of LFAs diagnostics. The system showed 99.3 % accuracy, 99.1 % sensitivity and 100 % specificity.

关键词： Rapid test MicroRNA Colored beads Urine Computer vision Smartphone Mobile device

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Phydiisp: a physics-guided differentiable pipeline for low-light machine vision

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SIGNAL image AND vIDEO processing 2025年第5期19卷

作者： Hou, Jingchao Gendy, Garas He, Guanghui Shanghai Jiao Tong Univ Shanghai Peoples R China

In low-light environments, machine vision tasks often suffer from performance degradation because traditional image Signal processing (ISP) pipelines are primarily optimized for image quality metrics such as Peak Signal-to-Noise Ratio and Structural Similarity Index, which do not adequately address the specific needs of these applications. Existing methods fall short in enhancing the critical image features required for computer vision tasks under challenging lighting conditions. To address this, we introduce PhyDiISP, a physics-guided, differentiable ISP pipeline designed to improve machine vision performance in low-light scenarios. PhyDiISP integrates traditional ISP design principles with physical insights, including demosaicing for RAW-to-RGB conversion, global tone mapping to adjust overall brightness, and Multiscale Retinex-based enhancement to tackle low-light challenges. Experimental results show that PhyDiISP outperforms existing ISP methods in object detection accuracy across standard benchmarks by effectively enhancing key image features. Furthermore, when trained with L1 loss and aligned with ground truth on datasets of dark-light environments and real RAW-to-RGB conversions, it demonstrates competitive image quality. These results confirm that PhyDiISP is a viable and effective solution for real-world low-light machine vision applications.

关键词： Physics-guided Differential ISP Computer vision Low light

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Privacy-Preserving Autoencoder for Collaborative Object Detection

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IEEE TRANSACTIONS ON image processing 2024年 33卷 4937-4951页

作者： Azizian, Bardia Bajic, Ivan v. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural Network (DNN) model runs on the edge, and the rest is executed on the cloud. In such applications, the machine vision model does not need the exact visual content to perform its task. Taking advantage of this potential, private information could be removed from the data insofar as it does not significantly impair the accuracy of the machine vision system. In this paper, we present an autoencoder-style network integrated within an object detection pipeline, which generates a latent representation of the input image that preserves task-relevant information while removing private information. Our approach employs an adversarial training strategy that not only removes private information from the bottleneck of the autoencoder but also promotes improved compression efficiency for feature channels coded by conventional codecs like vvC-Intra. We assess the proposed system using a realistic evaluation framework for privacy, directly measuring face and license plate recognition accuracy. Experimental results show that our proposed method is able to reduce the bitrate significantly at the same object detection accuracy compared to coding the input images directly, while keeping the face and license plate recognition accuracy on the images recovered from the bottleneck features low, implying strong privacy protection. Our code is available at https://***/bardia-az/ppa-code.

关键词： image coding Data privacy Training Privacy Codecs visualization machine vision Deep neural network coding for machines privacy model inversion attack collaborative intelligence adversarial training feature compression

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Highly Efficient Back‑End‑of‑Line Compatible Flexible Si‑Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal processing and Bionic machine vision

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Nano-Micro Letters 2024年第11期16卷 323-339页

作者： Dayanand Kumar Hanrui Li Dhananjay D.Kumbhar Manoj Kumar Rajbhar Uttam Kumar Das Abdul Momin Syed Georgian Melinte Nazek El‑Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer EngineeringComputer Electrical Mathematical Science and EngineeringKing Abdullah University of Science and Technology(KAUST)23955‑6900 ThuwalSaudi Arabia

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices,opening numerous opportunities across countless domains,including personalized healthcare and advanced *** 3D integration,edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy ***,we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications,including electroencephalogram(EEG)-based seizure prediction,electromyography(EMG)-based gesture recognition,and electrocardiogram(ECG)-based arrhythmia *** experiments on three biomedical datasets,we observe the classification accuracy improvement for the pretrained model with 2.93%on EEG,4.90%on ECG,and 7.92%on EMG,*** optical programming property of the device enables an ultralow power(2.8×10^(-13) J)fine-tuning process and offers solutions for patient-specific issues in edge computing ***,the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions,making it promising for neuromorphic vision *** display the benefits of these intricate synaptic properties,a 5×5 optoelectronic synapse array is developed,effectively simulating human visual perception and memory *** proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

关键词： Neuromorphic computing Electrophysiological signal Artificial vision system image recognition Memristor

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Application of Improved Support vector machine for Pulmonary Syndrome Exposure with Computer vision Measures

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CURRENT BIOINFORMATICS 2024年第3期19卷 281-293页

作者： Khadidos, Adil O. Alshareef, Abdulrhman M. Manoharan, Hariprasath Khadidos, Alaa O. Selvarajan, Shitharth King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Panimalar Engn Coll Dept Elect & Commun Engn Chennai India Kebri Dehar Univ Dept Comp Sci & Engn Kebri Dehar Ethiopia

Background In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for *** The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making *** As a result, the suggested method incorporates an Improved Support vector machine (ISvM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much *** Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing *** The proposed technique with ISvM produces 83 percent of successful results.

关键词： Computer vision image processing pulmonary disease support vector machine (SvM) pulmonary syndrome loop generation

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Advances in Computer vision and Deep Learning and Its applications

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ELECTRONICS 2025年第8期14卷 1551-1551页

作者： Wang, Aili Wu, Haibin Iwahori, Yuji Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Laser Spect Technol & Ap Harbin 150080 Peoples R China Chubu Univ Dept Comp Sci 1200 Matsumoto cho Kasugai 4878501 Japan

(1) Computer vision: The field of computer vision is making significant strides in dynamic reasoning capability through test-time scaling (TTS) [1] technology. TTS optimizes the robustness and interpretability of models in complex tasks by flexibly allocating computational resources. Multimodal base models, such as CLIP (contrastive language-image pre-training) [2] and Florence, facilitate the deep fusion of vision and language through cross-modal alignment techniques. These advancements have significantly improved the accuracy of visual question answering (vQA) and cross-modal retrieval. Generative AI technologies, such as Stable Diffusion, have also broken through the limitations of 2D image generation, enabling the transition to semantics-driven 3D scene models, like neural radiance fields (NeRF) [3]. This shift supports the generation of spatial models with physically interactive attributes from a single sheet of input, providing a new paradigm for virtual reality and industrial design. In addition, the introduction of the spatial intelligence [4] concept allows computer vision systems to simulate physical interactions in 3D space, driving the development of embodied intelligence and robot *** articles included in this Special Issue cover advancements in ten research directions: computer vision, feature extraction and image selection, pattern recognition for image processing techniques, image processing in intelligent transportation, neural networks, machine learning and deep learning, biomedical image processing and recognition, image processing for intelligent surveillance, deep learning for image processing, robotics and unmanned systems, and AI-based image processing, understanding, recognition, compression, and reconstruction. I have categorized the 33 articles included in this Special Issue based on these research directions, with the classification system not only demonstrating the vertical extension of the technological depth but also embodyin

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An ultra-fast and energy-efficient CNTFET-based image corner detection hardware for real-time image processing applications

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2024年 175卷

作者： Behbahani, Fereshteh Behrad, Alireza Moaiyeri, Mohammad Hossein Shahed Univ Elect Engn Dept Tehran Iran Shahid Beheshti Univ Fac Elect Engn Tehran Iran

In image processing and machine vision, corner detection is pivotal in diverse applications, including computer vision, 3D reconstruction, face detection, object tracking, and video technologies. Despite the wide usage, the real-time and energy -efficient hardware implementation of corner detection algorithms remains a critical challenge because of the computational resource limitations. On the other hand, owing to the complicated nature of corner detection algorithms, their hardware implementation has been limited to the graphics processing unit (GPU) and field programmable gate arrays (FPGA) platforms. In this regard, this work aims to propose a novel and ultra -efficient carbon nanotube field-effect transistor (CNTFET)-based hardware for image corner detection. Thanks to the proposed corner detection algorithm, the designed hardware has been realized using 2742 transistors with competitive accuracy. The proposed corner detection hardware indicated a remarkable salt -and -pepper noise immunity without using any noise reduction circuit. Our comprehensive simulations demonstrate 78%, 87%, and 94.5% total average improvements in delay, power, and energy compared to the other related corner detection hardware. Moreover, the proposed CNTFET-based corner detection hardware shows a 43 ps propagation delay, demonstrating its real-time operation. The proposed corner detection algorithm at the system level shows suitable accuracy metrics such as Recall, Precision, and error of detection (EoD) compared to the other well-known corner detectors. Our method has established a new pathway for real-time circuit -level hardware design for image processing and machine vision applications.

关键词： Fuzzy logic Programmable MFG FinFET Slope calibration image enhancement

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Editorial for the Special Issue "machine Learning in Computer vision and image Sensing: Theory and applications"

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SENSORS 2024年第9期24卷 2874页

作者： Chakraborty, Subrata Pradhan, Biswajeet Univ New England Sch Sci & Technol Armidale NSW 2351 Australia Univ Technol Sydney Fac Engn & IT Ctr Adv Modelling & Geospatial Informat Syst CAMGI Sch Civil & Environm Engn Sydney NSW 2007 Australia Griffith Univ Griffith Business Sch Nathan Qld 4111 Australia Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia

machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [1]. The diverse applications of ML models span domains such as medical image [2,3,4] and signal processing [5,6], remote sensing for earth observation and monitoring [7,8,9], the detection of daily human activities [10,11], and many more. ML models play a significant role in supporting computer vision and image-sensing applications, helping to unravel complex and real-world challenges. Recent developments in ML empower us to better analyse image and sensor data, motivating extensive research initiatives aimed at addressing applied challenges in multiple domains, including healthcare, agriculture, defence, remote sensing, earth observation, and autonomous navigation.

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A Survey on Optimal Transport for machine Learning: Theory and applications

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IEEE ACCESS 2025年 13卷 26506-26526页

作者： Pereira, Luiz Manella Amini, M. Hadi Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Florida Int Univ Secur Optimizat & Learning InterDependent Networks Miami FL 33199 USA

Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling and machine learning (ML). OT provides powerful tools for comparing probability distributions and producing optimal mappings that minimize cost functions. Consequently, OT has been widely implemented in computer vision tasks such as image retrieval, image interpolation, and semantic correspondence, as well as in broader applications spanning domain adaptation, natural language processing, and variational inference. In this survey, we aim to convey the emerging prominence and widespread applications of OT methods across various ML areas and outline future research directions. We first provide a history of OT. We then introduce a mathematical formulation and the prerequisites to understand OT, including Kantorovich duality, entropic regularization, KL Divergence, and Wasserstein barycenters. Given the computational complexity of OT, we discuss entropy-regularized version of computing optimal mappings that facilitate practical applications of OT across diverse ML domains. Further, we review prior studies on OT applications in ML. To this end, we cover the following: computer vision, graph learning, neural architecture search, document representation, domain adaptation, model fusion, medicine, natural language processing, and reinforcement learning. Finally, we outline future research directions and key challenges that could drive the broader integration of OT in ML.

关键词： Measurement Costs Surveys Natural language processing Interpolation Earth Couplings Computer vision US Department of Homeland Security Reinforcement learning machine learning optimal transport Wasserstein distance computational optimal transport

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A survey and identification of generative adversarial network technology-based architectural variants and applications in computer vision

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4594-4615页

作者： Zala, Kirtirajsinh Thumar, Deep Thakkar, Hiren Kumar Maheshwari, Urva Acharya, Biswaranjan Marwadi Univ Dept Informat Technol Rajkot 360006 Gujarat India Marwadi Educ Fdn Fac Engn Rajkot 360006 Gujarat India Pandit Deendayal Energy Univ Sch Technol Dept Comp Sci Engn Gandhinagar 382007 Gujarat India Marwadi Univ Dept Comp Engn AI & BDA Rajkot 360006 Gujarat India

The surging popularity of generative adversarial networks (GANs) has ignited a wave of innovation in the realm of computer vision, a highly explored subfield of deep learning. GANs are revolutionizing the area of machine learning because they use a game-based training technique. This is in contrast to traditional approaches to machine learning, which center on feature learning and picture production. Several subfields of computer vision have seen tremendous progress thanks to the integration of numerous processing approaches, including image processing, dynamic processing, text, audio, and video processing, as well as generalized generative adversarial networks (GANs). Nevertheless, despite the fact that GANs have made great progress, they still offer promise that has not been fully realized and space for additional development. GANs have a wide range of applications within computer vision, including data augmentation, displacement recording, dynamic modeling, and image processing. This article digs into recent advances made by GAN researchers working in the realm of AI-based security and defense and discusses their accomplishments. In particular, we investigate how well image optimization, image processing, and image stabilization are incorporated into GAN-driven picture training. We want to achieve our goal of providing a complete overview of the present status of GAN research by carefully evaluating research articles that have been subjected to peer review.

关键词： Generative adversarial networks Computer vision Loss-variants machine learning Intelligent computing Bio informatics

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