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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

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A Spoof Detecting Fingerprint Reader Based on a Combination of Total Internal Reflection and Direct image Capture 4

A Spoof Detecting Fingerprint Reader Based on a Combination ...

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4th IEEE international conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Linsangan, Noel B. Caridad, Bob Laurence V. De Vera, Ar Jay B. Juanatas, Roben A. School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines National University Manila Philippines

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

this study aims to develop a Spoof Resistant Fingerprint Reader based on a combination of Total Internal Reflection and Direct image Capture. the system is built using a Raspberry Pi and uses two cameras connected to a multi-camera adapter to capture the picture of the fingerprint. To thoroughly examine the minutiae of the fingerprint, the project uses a series of methods using computer vision to enhance the Histogram Equalization in digital image processing. the data for this involves 60 fingerprint molds constructed by using 3 different material combinations. these being Hot Glue-Glue, Candle Wax-Wood Glue, and Clay-Gelatin. the Hot glue & Glue and Candle Wax & Wood Glue molds were tested 5 times per mold while the Clay & Gelatin mold was tested 3 times because it melts quickly. the system garnered a 95% accuracy in determining whether a fingerprint was a spoof or not. © 2022 IEEE.

关键词： computer vision

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Multilevel Crop image Segmentation Using Firefly Algorithm and Recursive Minimum Cross Entropy 4th

Multilevel Crop Image Segmentation Using Firefly Algorithm a...

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4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Kumar, Arun Kumar, A. Vishwakarma, Amit PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9789819900466

image segmentation plays an important role in computer vision technology and agriculture is one of their applications. the crop images present near the vicinity are complex and dense. Hence, multilevel thresholding of such crop images is a tedious task. In this paper, we propose multilevel thresholding of crop images using recursive minimum cross entropy and firefly algorithm. the firefly is based on the social behavior of the swarm of fireflies and bioluminescent information-sharing phenomena. It is a swarm-based algorithm, which offers a better search mechanism to find the optimum threshold value. the performance of the proposed method is observed over ten complex background crop images and compared with the wind-driven optimization algorithm. the better fidelity parameters evidence the superiority of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Investigating the Role of image processing to Identify pattern for Industrial Automation 11

Investigating the Role of Image Processing to Identify Patte...

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11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaur, Gagandeep Balu, Vincent Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

In situations when both the output and the result are graphical, classification is used. the science's name was decided upon because of its concentration on image analysis. Imaging, satellite data, contrasts amplification, and voice signal production all could be done on the architecture here. A image or video cannot be recognized until a certain component or feature is found. In order to spot patterns, Cad system is used in this study. An area of study was the function of image processing in quality assurance. Even at high speeds., excellent ray tracing enables faultless record. Corporate machine learning is often used in process automation. computer tech vision equipment includes things like camera-activating displays and movable video recorded tracks. © 2022 IEEE.

关键词： image recognition

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

4th International Conference on Advanced Computational and C...

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

ISBN: (纸本)9789819942831

the proceedings contain 44 papers. the special focus in this conference is on international conference on Advanced Computational and Communication Paradigms. the topics include: Time-Related Natural Language Query Handling to Extract Autism Spectrum Disorder Information from Temporal Database;emotion-Based Music Recommendation;a Comprehensive Study on Natural Language processing, It’s Techniques and Advancements in Nepali Language;quality Evaluation of Enterprise Architecture Frameworks;natural Language Query processing System to Extract Autism Spectrum Disorder Information from Database;spiking Neural Network in computer vision: Techniques, Tools and Trends;a Quantitative Study on the FaceNet System;Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of Machine Learning Classifiers;comparative Analysis of Minimax Algorithm with Alpha–Beta Pruning Optimization for Chess Engine;designing of Advanced Machine Learning-Based Predictive Model for Maternal Mortality;a Modified Microwave Based System Design for Early-Stage Breast Cancer Detection;research Review and Literature Perception Towards Medicinal Plants Classification Using Deep Learning Techniques;features Fusion-Based Gait recognition with Covariate Conditions;computational Modelling of Glucocerebrosidase Signalling Pathways in Parkinson’s Disease;COVID-19 Lung Patch Segmentation Using COVSeg-NET;research Study on Leukemia Detection Using Deep Learning Techniques;automated Classification of Diabetic Retinopathy Using Deep Learning Architecture;Deep Learning-Based Tumor Segmentation from CT images;DIVS: A Real-Time Video Stabilization Framework for Consumer Camera;Saliency-Driven ‘Evidence CNN’ for Fine-Grained recognition of Twins;multi-criteria-Based Page Ranking Using Metaheuristic Swarm Optimization;IoT-Based Landslide Monitoring System Using LiDAR Sensors: A Review;Change Detection in LULC for Durgapur: An Efficient GIS-Based Framework;morphological Operation-Based Backgrou

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Dynamic Layer-Wise Token Pruning for Sequence-to-Sequence Transformer Inference 4th

Dynamic Layer-Wise Token Pruning for Sequence-to-Sequence T...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Keom, Ji Hun Kim, Yeachan Lee, Sung Ju Lee, Sang Keun Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787043

Transformer-based language models have achieved outstanding results on a wide range of tasks, but often at the expense of high computational demands. In response, we introduce Seek2Skim, a novel token pruning method seamlessly integrated into the sequence-to-sequence architecture. Unlike previous efforts that dynamically adjust computations in either the encoder or decoder, Seek2Skim further improves computational efficiency by simultaneously enhancing both components. Our method incorporates trainable modules that assess token importance, pruning less crucial tokens to effectively reduce computational overhead. Additionally, we introduce dynamic cross-attention filtering to preserve model performance even with reduced token representation updates. Extensive experiments confirm the effectiveness of Seek2Skim, demonstrating substantial speed improvements across diverse tasks. Notably, Seek2Skim preserves over 99% of the backbone model’s performance with a 1.91× and 1.53× inference speedup for text classification and generation tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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A Survey of Low-Light image Enhancement 5

A Survey of Low-Light Image Enhancement

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wei, Wangjuan Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

image sensors imaging in low-light environments can cause problems such as high image noise, low contrast, and failure to represent large amounts of detailed information, which affect not only human eye observation but also applications related to computer vision and low-light image processing. the use of image enhancement methods and deep learning methods can improve the contrast of low-light images and improve the image quality. the article first introduces the traditional low-illumination image enhancement algorithms categorized and summarizes the improvement process of these algorithms in recent years, then introduces the low-illumination image enhancement methods based on deep learning, and finally introduces the existing low-illumination image datasets and the evaluation criteria of the enhanced images. © 2022 ACM.

关键词： image enhancement

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Dynamic Color Identification Using computer vision: OpenCV with Pandas

Dynamic Color Identification Using Computer Vision: OpenCV w...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： E. Mohanraj V. Kirubashini B. Gobika A.M. Archana Department of Artificial Intelligence and Data Science K.S. Rangasamy College of Technology Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

computer vision, the cornerstone of modern artificial intelligence. Moving forward with the development of new tools and techniques, OpenCV (Open Source computer vision Library) coupled with pandas' powerful data management capabilities has emerged as a versatile framework for real-world image processing tasks. this paper explores the application of OpenCV and pandas for real-time color recognition and OpenCV's enhanced functionality, such as video stream processing taking advantage of Pandas with efficient data management. the proposed system captures real-time images and processes user-defined interactions to identify the color. By combining pandas, the system optimizes access to the predefined color data stored in a CSV file, allowing for seamless color matching. this OpenCV and pandas together enables the system to dynamically display color names, RGB components and hex values in real time enhancing user interaction by double clicking. the combination of these technologies represents a powerful method for solving image-based problems for innovation and efficiency. Real world applications demonstrate their capabilities.

关键词： Deep learning computer vision Technological innovation Sentiment analysis image color analysis Streaming media Real-time systems Libraries

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MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model 27th

MCANet: Multimodal Caption Aware Training-Free Video Anomaly...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Dev, Prabhu Prasad Hazari, Raju Das, Pranesh Machine Learning Laboratory Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9783031781247

Towards Video Anomaly Detection (VAD), existing methods require labor-intensive data collection and model retraining, making them costly and domain-specific. the proposed method, termed as Multi-modal Caption Aware Network (MCANet), introduces a novel paradigm that identifies anomalies in video sequences without requiring prior domain knowledge. this training-free VAD approach dynamically generates and analyzes textual descriptions of video frames by utilizing off-the-shelf vision-language model (VLM), audio-language model (ALM) and large language model (LLM). MCANet has four primary modules. the first module utilizes image-text similarities to clean noisy captions generated by the image captioning model, while the second module applies audio-text similarities to refine noisy captions produced by the audio captioning model. the third module employs a LLM to consolidate scene dynamics over time. Finally, the fourth module enhances the results by aggregating scores from semantically similar frames based on video-text similarity. To validate the effectiveness of the proposed method, experiments are conducted on two large-scale benchmark datasets (UCF-Crime and XD-Violence). Experimental results demonstrate that MCANet surpasses existing unsupervised and one-class approaches without requiring any training or data collection. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Application and Technical Analysis of computer vision Technology in Natural Resource Survey 1

引用

4th international conference on Artificial Intelligence in China, AIC 2022

作者： Wang, Yuehong Luo, Hao Tianjin Natural Resources Investigation and Registration Center Tianjin300201 China

ISBN: (数字)9789819912568

ISBN: (纸本)9789819912551

Natural resources are the material basis for human survival and national economic and social development. Natural resources survey is related to major national decision-making and deployment, fully supports and serves the economic and social development and ecological civilization construction in the new era, and systematically grasps the quantity, quality, spatial distribution, development and utilization, ecological status, and dynamic changes of national natural resources and landmark substrates. computer vision technology uses computers to process, analyze and understand images to identify targets and objects in various patterns. the natural resources are photographed and imaged by industrial unmanned cameras or remote sensing satellites, and the characteristics are compared according to image processing, to realize the investigation and analysis of natural resources, improve the efficiency of natural resource management investigation, and provide a reference for planning decision-making and implementation. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Research on Few-Shot Object recognition

Research on Few-Shot Object Recognition

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Intelligent Communications and Computing (ICC), international conference on

作者： Qina Xie Anping Zeng Yilan Xue Kaixiao Zhu School of Computer Science and Technology Yibin University Yibin China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

Object recognition is a crucial task in the realm of computer vision, aimed at identifying and pinpointing a particular object within an image. In practical scenarios, a lack of sufficient training samples frequently hinders the training of deep learning models, presenting a significant challenge. FSOR was created to address this issue. In this paper, the basic concepts, main methods, challenges and latest research progress of few-shot object recognition are reviewed, and its potential in practical application is discussed.

关键词： Training Deep learning computer vision Computational modeling Object recognition

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