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Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields 8

Investigation on Relative Pose Regression for Multi-Camera S...

引用

8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Kullmann, Timo Hübner, Patrick Wirth, Tristan Kuijper, Arjan Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Mathematical and Applied Visual Computing Darmstadt Germany Fraunhofer IGD Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Interactive Graphics Systems Darmstadt Germany

Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera's pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have chosen Nerfacto in the Nerfstudio framework. The pose correction model learns the necessary relative translation and rotation adjustments for the secondary camera solely through the NeRF loss. This allows us to integrate our correction model directly into the Nerfacto training pipeline without altering the core functionality. Through extensive experiments on different camera configurations in a synthetic scene, we rigorously evaluate our model's performance across diverse scenarios, pushing it to its limits. Our findings reveal that our model can effectively learn pose correction parameters within a constrained range, with increased sensitivity to larger translations and particular challenges in rotation corrections. This research highlights the potential of NeRFs for machine learning-driven 3D reconstruction on dual- and multi-camera platforms, expanding the applicability of NeRFs to more complex, real-world setups despite the inherent challenges. Copyright: © 2024 Timo Kullmann et al.

关键词： 3D reconstruction

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The 2-MAXSAT Problem Can Be Solved in Polynomial Time

The 2-MAXSAT Problem Can Be Solved in Polynomial Time

引用

2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Chen, Yangjun The University of Winnipeg Dept. Applied Computer Science WinnipegMB Canada

ISBN: (纸本)9798350320282

By the MAXSAT problem, we are given a set V of m variables and a collection C of C clauses over V. We will seek a truth assignment to maximize the number of satisfied clauses. This problem is NP-hard even for its restricted version, the 2-MAXSAT problem by which every clause contains at most 2 literals. In this paper, we discuss a polynomial time algorithm to solve this problem. Its time complexity is bounded by O(n2m3). Hence, we provide a proof of P=NP. © 2022 IEEE.

关键词： Polynomial approximation

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A New Algorithm to Find the KthSmallest Element in an Unordered List (Efficient for Big Data) 2

A New Algorithm to Find the KthSmallest Element in an Unorde...

引用

2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Ryalat, Mohammad Hashem Al-Balqa Applied University Dept. of Computer Science As-Salt Jordan

ISBN: (纸本)9781665436052

Finding the kth smallest element in an unsorted list of values is of vital importance in several disciplines and applications such as image and signal processing, machine learning, feature extraction, statistics, trend analysis, genome sequencing and many others. Different algorithms that find the kth smallest element in an unsorted list of values were presented in literature and used in industry. The speed and utilization of memory (time and space) are considered as the main factors on which researcher depend to select one of the algorithms instead of the other. This paper presents a proposed algorithm that is developed to find the kth smallest element in an unsorted array of elements. The proposed algorithm is compared with other five well-known efficient algorithms (quick sort, merge sort, radix sort, counting sort, and quick-select algorithms). The outcome indicates that the proposed algorithm exceeds the other algorithms in terms of speed and memory utilization. The proposed algorithm achieves more superiority when it is applied over large sets of data which makes this algorithm competent for big data. © 2022 IEEE.

关键词： Big data

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An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions. 2

An Efficient Framework for classifying Cancer diseases using...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Metipatil, Prabhuraj Bhuvaneshwari, P. Basha, Syed Muzamil Patil, Siddanagouda S REVA University School of CSE Bengaluru India Dept. of Computer Science & Applied Mathematics UAS Bengaluru Bengaluru India

ISBN: (纸本)9798350320923

In recent years, a significant number of deaths worldwide have been due to cancer diseases. Analysis of Microarray gene expressions and protein interaction data facilitates early cancer identification. The accurate prediction of information for thousands of genes is made possible by using DNA microarray technology. Protein-Protein Interactions (PPIs) are the crucial protein activities involved in the cell cycle that replicates the DNA and cellular signaling. Determining whether a pair of proteins interacts is crucial for diagnosing an illness in molecular biology is therefore important. In existing machine learning classifiers have two-class problem that is limited and only be used to solve binary class problems, additionally, they can be prone to overfitting, as the classification framework may also become too specialized to the training data and not generalized to the varied data. To overcome this problem, this paper proposes an ensemble machine learning technique;ensembling combines the strengths of both classifiers that allow more robust and accurate framework. The better combination of both Support Vector machine and Naïve Bayes ensemble provides better performance in terms of various performance parameters. The proposed SVM-NB Ensemble classifier outperforms the existing classifiers by 15-20% over various performance parameters like classification accuracy, time taken for classification, precision, recall, and F-measure. The results were drawn by comparing the proposed ensemble (SVM+NB) classifier with the existing most applied classifiers like Logistic Regression (LR), Support Vector Machine and Naive Bayes techniques. © 2023 IEEE.

关键词： Support vector machines

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Increase the Degree of Accuracy by Employing A More Accurate Classification Approach 2

Increase the Degree of Accuracy by Employing A More Accurate...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Khan, Muhammad Farhan Sajid Farooq, Muhammad Joghee, Shanmugan University of Health Sciences Dept. of Forensic Sciences Lahore Pakistan Lahore Garrison University Dept. of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan Skyline University School of Business Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

Traditional artificial neural networks and machine learning have traditionally been used to classify images;however, these approaches have not only struggled to keep up with the processing demands of such datasets but have also performed inefficiently and with poor classification accuracy due to the processing demands of enormous image datasets during feature extraction and model training. Large photo collections make it challenging for machine learning and traditional artificial neural networks to keep up with their processing demands. Deep learning significantly surpasses other, more traditional techniques for photo classification. Another difficulty was that when dealing with really large pictures, standard artificial neural networks were forced to exceed their data storage limits. As a direct result of their research, the authors of the study developed a deep-learning model for image tagging. This model was developed as a tool to aid in the identification and categorization of pictures on a large scale. Following a brief explanation of neural network theory, the study's focus shifted to an examination of the various convolutional neural network types and the general strategy for employing such networks for picture categorization. The present model of a convolutional neural network was utilized to minimize noise and alter the parameters used in the feature extraction operation. This was supposed to improve the results' dependability. An upgraded convolutional neural network was used to develop the model's architecture. The resulting deep learning model improved operational effectiveness and data categorization accuracy significantly. To achieve this purpose, the structure must be modified so that it can work as effectively as possible. Experiments were carried out to evaluate if the number of times an image classification network model is put through its training process influences the level of accuracy obtained by the model. This was done to see how effectively

关键词： Image classification

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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Renewable Energy Efficiency of Unmanned Aerial Vehicles Operating with Wireless Sensor Networks and Mobile Ad Hoc Networks 2

Renewable Energy Efficiency of Unmanned Aerial Vehicles Oper...

引用

2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Ahmad, Wakeel Ahmed, Sarfaraz Ahmad, Adeel Siddiqui, Shams Tabrez Khamruddin, Mohammad Khan, Haneef Jazan University College of Applied Industrial Tech Deptartment of Electrical Engineering Technology Jazan Saudi Arabia Jazan University College of Computer Science and Information Technology Dept. of Cnet Jazan Saudi Arabia Aligarh Muslim University Faculty of Science Department of Computer Science U.P Aligarh India Jazan University College of Computer Science and Information Technology Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350383041

The amalgamation of Unmanned Aerial Vehicles (UAVs) into Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) provides significant prospects to improve network capabilities. This study examines the energy renewable efficiency and performance of UAVs operating in WSNs and MANETs. The assessment considers a variety of elements that influence energy consumption, including the possibility of integrating renewable energy, energy management measures, and the impact on overall network performance. This seeks to provide insights into optimizing energy usage and improving the operating efficiency of UAVs in these dynamic network situations through extensive analysis and evaluation. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The paper explores existing protocols such as LEACH, AODV and OLSR highlighting their benefits and challenges for UAV's. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

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Ontology population using CNN model: Application to COVID-19 diagnosis 6

Ontology population using CNN model: Application to COVID-19...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Mellal, Nacima Saighi, Asma Institute of Applied Science & Technology University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Networks And Telecommunication El Bouaghi Algeria University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Computer Science El Bouaghi Algeria

ISBN: (纸本)9798350350265

AI models and especially deep learning models have found applications across various medical domains. While many studies focus on using electronic health records (EHRs) data to train AI models, implementing the reverse process poses several challenges. Also, the heterogeneous nature of the used data in EHRs, such as diagnoses, clinical reports, test results, prescriptions, and medical images, makes their interpretation difficult for the practitioners which require a unified understanding. Medical ontologies are useful to improve data interpretability and semantics. In this paper, we aim to create a synergy between ontologies and deep learning techniques to enhance efficiency, data understanding, and provide healthcare quality. We propose a system in which our developed ontology is populated automatically using the predictions of an AI model. We have developed and trained a CNN model to aid in the COVID-19 diagnosis. The initial obtained results are promising with an accuracy of 98%. © 2024 IEEE.

关键词： COVID-19

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test

Prediction of Typhoid Using Machine Learning and ANN Prior t...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhuiyan, Md. Atik Rad, Sharaf Shahariare Johora, Fatema Tuj Islam, Abdullah Hossain, Md Ismail Khan, Aliza Ahmed Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Rhine-Waal University of Applied Sciences Dept. of Communication and Environment Kamp-Lintfort Germany

ISBN: (纸本)9798350348217

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy. © 2023 IEEE.

关键词： Neural networks

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