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33rd British machine vision Conference Proceedings, BMVC 2022

作者： Barath, Daniel Noskova, Jana Eichhardt, Ivan Matas, Jiri Computer Vision and Geometry Group ETH Zurich Switzerland Visual Recognition Group FEE CTU in Prague Czech Republic Algorithms and Applications Department Eotvos Lorand University Budapest Hungary

We introduce novel methods that speed up the pose-graph generation for global Structure-from-Motion algorithms. We replace the widely used "accept-or-reject" strategy for image pairs, where often thousands of RANSAC iterations are wasted on pairs with low inlier ratio or on non-matchable ones. The new algorithm exploits the fact that every unsuccessful RANSAC iteration reduces the probability of an image pair being matchable, i.e., it reduces its inlier ratio expectation. The method always selects the most promising pair for matching. While running RANSAC on the pair, it updates the distribution of its inlier ratio probability in a principled way via a Bayesian approach. Once the expected inlier ratio drops below an adaptive threshold, the method puts back the pair in the processing queue ordered by the updated inlier ratio expectations. The algorithms are tested on more than 600k real image pairs. They accelerate the pose-graph generation by an order-of-magnitude on average. The source code is available at https://***/danini/pose-graph-creation. © 2022. The copyright of this document resides with its authors.

关键词： Iterative methods

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Gender Prediction Using Real-time Convolutional Neural Network

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Procedia Computer Science 2025年 258卷 497-506页

作者： Emmanuel O. Balogun Sunday Adeola Ajagbe Abidemi E. Adeniyi Temitope O. Olayinka Elijah Adetoyese Adeogun Gbadegesin Adetayo Taiwo Ogheneruona Maria Esegbona-Isikeh Pragasen Mudali ICT Unit Ajayi Crowther University Oyo Oyo Nigeria Department of Computer Engineering Abiola Ajimobi Technical University 200255 Nigeria Department of Computer Science Bowen University Iwo Nigeria Department of Computer Science University of Maine Orono United State Department of Computer Science Ladoke Akintola University of Technology Ogbomoso Nigeria School of Computing Science & Engineering University of Salford Manchester United Kingdom Amey Services Limited United Kingdom

This work presents real time gender prediction using Convolutional Neural Network. Automatic classification of gender has become an area that has garnered importance due to the emergence of breakthroughs in the world of computing particularly with the advent of machine learning and Artificial intelligence. The goal of gender prediction in computer vision involves accurate prediction of gender from visual data. Gender prediction is an indispensable biological metric as it plays a significant role in many human applications. The application varies from immigration, border access, law enforcement, defence and intelligence, citizen identification and banking. image processing combined with machine learning algorithms have been employed to build solutions from image representation of biological attributes such as facial images, human skeletal radiographs (most notably skull and pelvic bones), gait, smiles and non-biological features such as social media activities, names and other means that could be employed to determine gender by extracting regions of interest, applying necessary filters and normalizing the matrix values obtained. For the purpose of this work, a total of 6760 hand-bone radiographs were acquired from the Radiological Society of North America Repository. The dataset was divided into 70% training datasets and 30% test dataset using Random Sampling Cross-Validation Method. The acquired data images were pre-processed using image cropping, histogram equalization and segmentation techniques. Performance evaluation was carried out on the developed system using the metrics: Accuracy, False Positive Rate (FPR), Recognition Time, Specificity and Sensitivity, these metrics were evaluated at threshold values of 0.25, 0.35, 0.5 and 0.75. Optimal values were gotten at optimum threshold value of 0.75, the values are; 4.79, 93.67, 95.21, 94.53 and 137.87 respectively for metrics (FPR, Sensitivity, Specificity, Accuracy and Time).

关键词： Convolutional Neural Network Gender Prediction Computer vision image processing Classifiers image Recognition

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A Review on Quantum machine Learning in Different Computer vision Fields

A Review on Quantum Machine Learning in Different Computer V...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Md Majedul Islam Jing Selena He Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

Quantum machine Learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field.

关键词： Computer vision image segmentation Quantum computing Reviews High dimensional data Face recognition Generative adversarial networks Hardware Satellite images Biomedical imaging

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An Improved Depth image Clustering Algorithm Based on Spatial Information

An Improved Depth Image Clustering Algorithm Based on Spatia...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Li Yan Shan Su Xiao Hu Yuquan Zhou Changjun Chen School of Geodesy and Geomatics Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed.

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image retrieval from remote sensing big data: A survey

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INFORMATION FUSION 2021年 67卷 94-115页

作者： Li, Yansheng Ma, Jiayi Zhang, Yongjun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-increasing remote sensing imaging sensors on these platforms, has led to the formation of rapidly-growing earth observation data with the characteristics of large volume, large variety, large velocity, large veracity and large value, which raises awareness about the importance of large-scale image processing, fusion and mining. Unconsciously, we have entered an era of big earth data, also called remote sensing (RS) big data. Although RS big data provides great opportunities for a broad range of applications such as disaster rescue, global security, and so forth, it inevitably poses many additional processing challenges. As one of the most fundamental and important tasks in RS big data mining, image retrieval (i.e., image information mining) from RS big data has attracted continuous research interests in the last several decades. This paper mainly works for systematically reviewing the emerging achievements for image retrieval from RS big data. And then this paper further discusses the RS image retrieval based applications including fusion-oriented RS image processing, geo-localization and disaster rescue. To facilitate the quantitative evaluation of the RS image retrieval technique, this paper gives a list of publicly open datasets and evaluation metrics, and briefly recalls the mainstream methods on two representative benchmarks of RS image retrieval. Considering the latest advances from multiple domains including computer vision, machine learning and knowledge engineering, this paper points out some promising research directions towards RS big data mining. From this survey, engineers from industry may find skills to improve their RS image retrieval systems and researchers from academia may find ideas to conduct some innovative work.

关键词： Remote sensing (rs) big data Rs image retrieval methods Rs image retrieval applications Evaluation datasets and performance discussion Future research directions

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18th International Conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent Computing Systems and applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive applications in Edge Clouds;V2ICooper: Toward Vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles;towards Robust Internet of Vehicles Security: An Edge Node-Based machine Learning Framework for Attack Classification;device-Edge-Cloud Collaborative Video Stream processing and Scheduling Strategy Based on Deep Reinforcement Learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in Data Centers;multi-scale Data Reconstruction Based Policy Optimization Algorithm for Skill Learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement Learning;Secure Motion Verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement Learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis Datasets;low-Cost Robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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18th International Conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714634

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Analysis of Computer vision Applied in Martial Arts 2

Analysis of Computer Vision Applied in Martial Arts

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2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Pang, Yiqun Zhang, Changnian Wang, Yibing Wang, Qiurui Wang, Mingyang Institute of Artificial Intelligence in Sports Capital University of Physical Education and Sports Beijing China School of Dance and Martial Arts Capital University of Physical Education and Sports Beijing China Graduate School Capital University of Physical Education and Sports Beijing China

ISBN: (纸本)9781665408868

Computer vision has become an interdisciplinary subject integrating digital image processing, machine learning, computer science, and many other categories. It can provide contactless data collection and technical analysis for many sports. A lot of work in the field of martial arts benefits from computer vision. This paper reviews the applications of computer vision in martial arts. These applications include action recognition, pose estimation, intelligence judgment, etc., which bring more scientific training, more effective technical analysis and wider spread to martial arts. The investigation in this paper inspires future work on the basis of reviewing the existing research. © 2022 IEEE.

关键词： Computer vision

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Recent advances in image dehazing: Formal analysis to automated approaches

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INFORMATION FUSION 2024年 104卷

作者： Goyal, Bhawna Dogra, Ayush Lepcha, Dawa Chyophel Goyal, Vishal Alkhayyat, Ahmed Chohan, Jasgurpreet Singh Kukreja, Vinay Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Chitkara Univ Inst Engn & Technol Chandigarh Punjab India GLA Univ Dept Elect & Commun Engn Mathura India Islamic Univ Coll Tech Engn Najaf Iraq

images captured in hazy environments need to be processed to increase their contrast and colour integrity. Dehazing, sometimes referred to as haze removal is an important pre-processing step for image processing and computer vision applications. Numerous methods for dehazing images have been proposed in the literature. This study provides a complete evaluation of numerous image dehazing techniques and their notable standards. This study extracted and presented an important direction of the many algorithms to handle the challenges of dehazing such as model-based methods, transform domain methods, variational-based algorithms, learningbased algorithm and transformer-based algorithms. This study attempts to compile and evaluate the most important studies in the domain of image dehazing. A variety of factors have been considered necessary to provide a detailed information in this study. These factors include datasets that utilised in the literature, challenges faced by the prior researchers, motivations, and recommendations for reducing the drawbacks in the available literature. The systematic rules are utilized for searching all relevant papers on image dehazing using several keyword diversities along with a glance for assessment and the benchmark studies. image dehazing, which generally eliminates undesirable pictographic effects is often considered as an image enhancement method. A completely automated process, a valid assessment strategy, and databases based on diverse settings are needed for it to operate under real-time applications. Many relevant studies are conducted in order to achieve these substantial goals. We examined numerous image dehazing methods and assessed the objectivity of the results. The results of our study precisely reflect numerous observations on image dehazing regions in contrast to other review articles. We believe that the findings of the study can be a helpful set of recommendations for professionals looking for a full understanding of ima

关键词： Review image dehazing Multiscale Retinex theory machine learning Deep learning Transformer applications Quality assessment criteria Survey Research challenges

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Convolutional Neural Network for image Classification Research - Based on VGG16

Convolutional Neural Network for Image Classification Resear...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Ruoxi Yang College of Big Data Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, convolutional neural networks (CNNs) have increasingly become the predominant approach for image classification, demonstrating remarkable performance in this domain. Notably, VGG16 has gained widespread adoption due to its deep architecture and robust feature extraction capabilities. Consequently, this paper investigates the efficacy of employing the VGG16 convolutional neural network model for image classification on the CIFAR-10 dataset. The architecture of the VGG16 model and its distinctive application characteristics are first introduced, followed by an exploration of key steps including data preprocessing, model training, and hyperparameter tuning. 99.4% accuracy on the training set and 90.6% accuracy on the test set were attained after the training procedure was optimized. To improve the model's classification performance even more, this paper identifies limitations inherent in the current framework and proposes optimization strategies such as incorporating attention mechanisms and transfer learning. These strategies provide valuable insights for future applications of deep learning techniques on small-scale image datasets.

关键词： Training Accuracy Attention mechanisms Computational modeling Transfer learning Data models Convolutional neural networks Tuning Optimization image classification

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