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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 14

Advancing Fingerprint Recognition Quality Assessment: Introd...

引用

14th Indian conference on Computer Vision, Graphics and image processing, ICVGIP 2023

作者： Jaiswal, Prateek Koner, Arka Namboodiri, Anoop M. CVIT IIIT Hyderabad India India

ISBN: (纸本)9798400716256

In the field of biometric security, the quality assessment of fingerprint images is paramount for boosting the accuracy of fingerprint recognition systems. These systems are fundamental for the secure and efficient authentication and identification of individuals. Our research presents FRBQ (Fingerprint Recognition-Based Quality), an innovative quality metric designed to navigate the limitations of the NFIQ2 model. FRBQ exploits deep learning algorithms in a weakly supervised setting and utilizes matching scores from DeepPrint, a Fixed-Length Fingerprint Representation Model. Each score is paired with labels indicating the robustness of fingerprint image matches. However, in a fully referenced setting, these labels can be subjective, lacking a clear definition of what "image quality" inherently means. This weakly labeled approach strives to capture diverse perspectives on image quality, potentially making it a more encompassing metric. In comparison to NFIQ2, our research showcases the superior performance of the FRBQ model. It not only correlates better with recognition scores but also effectively evaluates challenging images that NFIQ2 struggles with. Validated by the esteemed FVC 2004 dataset, FRBQ proves its efficacy in fingerprint image quality assessment. This study underscores the transformative potential of AI in biometrics, emphasizing its capability to capture details that traditional methods might overlook. Our work stresses the critical role of precise quality assessment in the evolution of fingerprint recognition systems. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image quality

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Stopping Method for Iterative Scene Identification in an image Stream 18

Stopping Method for Iterative Scene Identification in an Ima...

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18th International Scientific and Technical conference on Dynamics of systems, Mechanisms and Machines, Dynamics 2024

作者： Muranov, A.N. Titova, A.P. Nogmov, M.H. Laboratory No 3 Institute of Design-Technology Informatics of RAS Moscow Russia

ISBN: (纸本)9798331509019

As part of solving a set of computer vision problems when analyzing video streams, a fairly popular task is to effectively stop the process of identifying scenes or objects. Arresters are one of the main problems in systems that implement online computer vision. This is explained by the fact that period of time spent obtaining results in video analytics systems is no less important than the reliability of the analysis results. In this regard, the conducted research focused on the problem of stopping when identifying scenes in a video stream. This requires detailed stopping algorithms for identifying scenes in video streams with a decision to arrest iterations, taking into account that it is not possible to generally assess the accuracy of the identification result. In this work, a list of properties of monotonic stopping functions was compiled, and an original algorithmic technique for solving stopping problems was proposed. The testing results demonstrated the high efficiency of the proposed method for identifying the text part of documents. © 2024 IEEE.

关键词： Video streaming

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Review Paper on Glaucoma Detection Using Machine Learning 5th

Review Paper on Glaucoma Detection Using Machine Learning

引用

5th International conference on Energy systems, Drives and Automations, ESDA 2022

作者： Jha, Kushal Gokharu, Naman Mathur, Rohan Bhandari, Sachin Department of Computer Engineering Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS Shirpur Campus India

ISBN: (纸本)9789819701537

Glaucoma is one of the leading causes of vision loss worldwide. Glaucoma cannot be cured in its advance stages. So, early detection of disease has become an important factor in the medical field. Numerous studies quickly became clear that using different image processing methods, the retinal fundus picture could be uncovered. In this study, many automated glaucoma detection techniques were thoroughly reviewed various papers were compared on the basis of the methodologies they adopted for detecting glaucoma from 2D fundus images created using CDR. 85% of glaucoma cases can be accurately detected by the majority of machine learning algorithms. First, image segmentation techniques like Elliptical Hough transform and edge detection gave the region of interest, i.e. optic disc and cup. These extracted images were then given to the machine learning and deep learning models to detect presence of glaucoma in the fundus image of the eye. The most significant deep learning, machine learning, and transfer learning methods for analysing retinal images were reviewed, along with their benefits and drawbacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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image Deblurring Based on Generative Adversarial Networks 7

Image Deblurring Based on Generative Adversarial Networks

引用

7th International conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Lu, Wenling Meng, Zhaohui School of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781665478571

image deblurring technology uses deep learning method to solve the blurry problem of single image , which is a challenging problem in the field of computer vision. In recent years, the rapid development of deep learning and computer vision has promoted the performance of blur processing algorithm. From the perspective of deep learning, the article studies on the image deblurring problem, and uses convolution neural network to achieve the purpose of image deblurring. Aiming at the problem that the scale of single deblurring using multi-scale network is huge, and the important feature information is not fully used, this paper proposes a deblurring algorithm based on generative adversarial networks. The model uses feature pyramid network as a framework instead of the multi-scale input, which effectively reduces the size of network and accelerates the training speed. In order to make better use of feature information, the attention mechanism and dual scale discriminator are introduced into the network. In order to make the training process more stable, the algorithm improves the discriminator loss, using the least squares and relativistic combination. The experimental results show that the image deblurring algorithm based on the generative adversarial network achieves better restoration effect than other algorithms. © 2022 IEEE.

关键词： Generative adversarial networks

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An improved visual reality experience for gaming applications using visual image processing 15

An improved visual reality experience for gaming application...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ruby, E.D. Kanmani Titus, Anita Anitha, R. Vijayakumari, P. Ravi, S. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Jeppiaar University Department of Ece Chennai India S.A. Engineering College Department of Ai & Ds Chennai India Sairam Engineering College Department of Ece Chennai India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

Digital and augmented truth has increased in recent years, with several packages in various industries. Gaming is one such industry that has benefitted significantly from those technologies, supplying customers with an immersive and interactive revel. But there's nevertheless room for improvement, especially in visible photograph processing. Cutting-edge digital and augmented fact systems depend upon pre-rendered three-D picks, which could bring a lower fine and much less realistic visual experience. This is especially obtrusive in rapid-paced gaming packages, where photographs may appear pixilated or need more elements. To deal with this difficulty, our research proposes an advanced visual fact enjoyed by gaming applications using superior visible picture processing techniques. Our method mainly includes the realtime processing of visual data to create exceptional realism in the digital environment. This may encompass strategies, texture mapping, dynamic lights, and superior rendering algorithms. © 2024 IEEE.

关键词： Applications. Exceptional Augmented Immersive Improvement Supplying

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Proceedings - 37th International conference on VLSI Design, VLSID 2024 - held concurrently with 23rd International conference on Embedded systems, ES 2024

Proceedings - 37th International Conference on VLSI Design, ...

引用

37th International conference on VLSI Design, VLSID 2024

ISBN: (纸本)9798350384406

The proceedings contain 124 papers. The topics discussed include: a novel approach to control a DC-DC converter using its empirical physical model;optimizing medical image analysis: leveraging efficient hardware and AI algorithms;DFT static verification using early RTL exploration and debug for mobile SoC and edge AI applications;reconfigurable processing-in-memory architecture for data intensive applications;parallel-series diode-based ring amplifier for switched capacitor circuits;enhancing output corruption through GSHE switch based logic encryption;on managing test-time, power, and layer assignment in 3D socs with built-in-self-repair modules;a compact low-power 29 Gb/s pseudo random quaternary sequence generator in 65 nm CMOS;and CAD tools pathway in hardware security.

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

引用

30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) Visual SLAM (vSLAM) Monocular image Dataset

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Proceedings - International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

Proceedings - International Conference on Augmented Intellig...

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2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

ISBN: (纸本)9781665489621

The proceedings contain 245 papers. The topics discussed include: artificial intelligence system based embedded real-time system power optimization and adaptability;real-time power transmission and transformation online monitoring based on convolutional neural network algorithm;multi-stage lung cancer detection and prediction using image processing techniques;a comprehensive analysis of text analysis algorithms for e-mail itemization;automatic seed generation for weighted test pattern generation;a review of video stabilization algorithms;nuclei localization in pap smear images for ovarian cancer visualization;a survey on alleviating the naive bayes conditional independence assumption;human activity recognition using relative relationship between different human body regions;survey on various methods and algorithms used for plant pest and diseases;vibrating motor based wearable vibrating analog alarm;detection of Indian fake currency using image processing;smart traffic light control system using image processing;knowledge graph curation from text via ontologies;a review on natural language processing based automatic question generation;the role of convolutional neural network in vehicle detection on spatial - temporal road traffic data;and application programming interface (API) based student activity tracking system.

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Towards Automation of Reference Data Generation for ADAS/AD Functions Development - ALiVA Framework

Towards Automation of Reference Data Generation for ADAS/AD ...

引用

2024 International Automotive CAE conference on Road to Virtual World, RVW 2024

作者： Mardhekar, Amogh Pawar, Rushikesh Mohod, Rucha Shirudkar, Rohit Hivarkar, Umesh N. FEV.io India

The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video & point-cloud data. ALiVA is capable of processing large volumes of camera & lidar sensor data. Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms and annotation results review functionality. Automatic object detection functionality creates a precise bounding box around the area of interest and assigns class labels to annotated objects. Object tracking algorithms tracks detected objects in video frames, provides a unique object id for each object and performs distance ranging. A unique feature of cognitive algorithms is the elimination of non-realistic objects of interests which appear in billboards or advertisements on buses/trucks. The framework also has a feature of event detection like overtaking scenarios or pedestrians/animals crossing the roads. Annotation review functionality is provided where assessment and correction of auto annotated data can be done manually. The results can be saved in standard file formats such as txt, csv, Json and open ASAM, ensuring compatibility across different systems. ALiVA replaces traditional annotation methods, thereby reducing the effort, the need for skilled resources and the time required to annotate large datasets. This eliminates human biases, manual errors and inconsistencies. ALiVA is validated for numerous customer requirements and offers a large amount and variety of data to quantify the benefits offered. Some of the distinguishing features are models and functionalities that are optimi

关键词： image annotation

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A Novel Machine Learning Algorithm for Prostate Cancer image Segmentation using mpMRI

A Novel Machine Learning Algorithm for Prostate Cancer Image...

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2023 International conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Shukla, Tushar Dhar Kalpana, K. Gupta, Richa Kalpanadevi, D. Walid, Md. Abul Ala Keshav Kumar, K. Chandigarh University Uittr Department of Physical Education Punjab India Presidency University Yelahanka School of Cse and Is Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnan koil India Department of Computer Science and Engineering Khulna9203 India Department of Humanities and Mathematics Hyderabad India

ISBN: (纸本)9798350333602

Recently, the advancements in technology and the changes in lifestyle behaviors of people leads to a sedentary routine of everyday habits. For this reason, numerous cancers have been developed and causes death for millions of people every year. Although, cancer is a deadly disease, early detection can help for survival. Especially for prostate cancer (PCa), early detection helps to cure the disease. Several researches have been done in medical image processing using Artificial Intelligence (AI) algorithms, yet accuracy and computational complexities limits the performance. With the intension of introducing a novel model for PCa detection from multi-parametric Magnetic Resonance Imaging (mpMRI), this study introduces an enhanced image segmentation model using the efficiency of Machine Learning (ML) algorithm together with Moth Flame Optimization (MFO) Algorithm to eradicate the previous issues. Generally, segmentation of an image is a partition of the image into multiple regions which enhances the classification performances. The major phases in this research includes 1. Data Pre-processing, 2. Feature Extraction, and finally,3. Segmentation. In data pre-processing, noises in the input images are eliminated using Gaussian filtering. The efficiency of MFO is employed to extract the optimal features from the images, and the extracted images are further subjected for U-Net segmentation. Moreover, the performance of the proposed model is validated through a comparative analysis over state-of the-art models in terms of DSC. © 2023 IEEE.

关键词： Magnetic resonance imaging

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