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Platform for Interactive Audiovisual Productions and Generative Art Using processing 1

Multidisciplinary International conference of Research Applied to Defense and Security, MICRADS 2022

作者： D‘Amato, Juan P. Leon, Diana Karina PLADEMA—UNCPBA Institute Campus Argentino Tandil Argentina National Scientific and Technical Research Council Tandil Argentina

ISBN: (数字)9789811976896

ISBN: (纸本)9789811976889

Through computers, artists have found a way to enhance their production, discovering new ways for communicating their productions and devising new forms of expression. Being able to make the most of these facilities requires interdisciplinary groups that seek to enhance creativity and at the same time generate new forms of creation. In particular, an artist takes advantage of the different tools available, from sound to draws in order to provide immersive experiences. This paper presents a platform that implements image and audio processing algorithms, to facilitate the artist's task, in what is called generative art. With the platform it is possible to create animated digital images in real time that adapt to the sound’ properties and that can be used in live audiovisual performances. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Audio acoustics

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image-Based Pothole Detection System Using YoloV8 Algorithm

Image-Based Pothole Detection System Using YoloV8 Algorithm

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2024 International conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Tripathy, Asutosh Rajalakshmi, T. Santhosh Kumar, R. Vasudevan Suryakala, S. SRM Institute of science and Technology College of Enginerring and Technology Department of Electronics and Communication Engineering Kattankulathur603203 India SRM Institute of Science & Technology College of Enginerring and Technology Dept of Electronics and Communication Engineering Kattankulathur603203 India

ISBN: (纸本)9798350354539

A pothole is a significant disadvantage in the roadways, resulting from depressions formed due to the improper quality of road materials and external conditions, such as extreme weather forces and the heavy vehicles passing over them. This leads to soil erosion beneath the road surface. Potholes are responsible for various damages including punctures, wheel damages, dents on vehicles, damage to the vehicle floor, collisions, and even severe accidents. The number of deaths and injuries stands at 1481 and 3103 respectively. Therefore, accurate and fast pothole detection is crucial for improving ITS (Intelligent Transport systems). Hence, this proposed study aims to overcome this major issue by implementing the YOLOv8 algorithm to detect the potholes present on the roads, which has the highest accuracy level of 98.18 percent, compared to existing algorithms. The study under consideration includes a comparison with the YOLOv5 algorithm, and the results demonstrate that the proposed approach outperforms the current method. © 2024 IEEE.

关键词： image processing

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Entropy and Correlation Coefficients of Neutrosophic and Interval-Valued Neutrosophic Hypersoft Set with application of Multi-Attributive Problems

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Neutrosophic Sets and systems 2022年 49卷 341-356页

作者： Jafar, Muhammad Naveed Saeed, Muhammad Ghani, Tahir Department of Mathematics University of Management and Technology C-II Johar Town Lahore54000 Pakistan Department of Mathematics Lahore Garrison University DHA Phase-VI Sector C Lahore54000 Pakistan

In computational intelligence, machine learning, image processing, neural networks, medical diagnostics, and decision analysis, the ideas of correlation coefficients and entropy have practical applications. By applying hypersoft set (HSS) in neutrosophic environment provides a good model for describing and addressing uncertainties. In statistics, the correlation coefficient between two variables is crucial. Furthermore, the accuracy of the correlation assessment is dependent on data from the discourse set. The main focus of this study, is to develop entropy for (NHSS) and generalized correlation coefficient interval-valued neutrosophic hypersoft-set (IVNHSS). We proposed some theorems on entropy along with algorithms based on correlation and weighted correlation coefficients in the context of NHSs and IVNHSS. The validity and superiority are presented along with application and also comparison is made with existing approaches. © 2022, University of New Mexico. All rights reserved.

关键词： Correlation Coefficients Entropy Fuzziness Hypersoft Set Interval-valued neutrosophic hypersoft set MCDM Neutrosophic hypersoft set Neutrosophic Set Soft Set

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Survey on One-Shot Learning for image Recognition using Machine Learning and Deep Learning Techniques 5

Survey on One-Shot Learning for Image Recognition using Mach...

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5th International conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kadam, Ganesh vitthal Dubey, Ghanshyam Prasad Computer Science Amity School of Engineering & Technology Gwalior Maharajpura Madhya Pradesh Gwalior474005 India Amity School of Engineering & Technology ASET-CSE Maharajpura Madhya Pradesh Gwalior474005 India

ISBN: (纸本)9798331504403

Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-shot learning restricting the number of data in each category is developed to attain enhanced results. Specifically, the traditional neural networks lead to over-fitting problems in training with a limited number of samples. In contrast, Machine Learning algorithms are capable of one-shot learning from limited samples like humans have the ability of oneshot learning. Hence, it is necessary to analyze one-shot object recognition techniques prevalent in image processing systems. This study will explore different one-shot learning techniques and methods from the state-of-the-art models and examine the merits and challenges of the approach with 25 literature papers. The effectiveness of the existing approaches will be evaluated by various techniques, datasets, and performance metrics are discussed in detail. At last, the paper discusses the potential future studies and challenges in achieving better detection accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Building surface damage recognition based on synthetic data 9

Building surface damage recognition based on synthetic data

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9th IEEE International conference on Information Technology and Nanotechnology, ITNT 2023

作者： Zherdeva, L.A. Minaev, E. Yu. Firsov, N.A. Samara National Research University Samara Russia Image Processing Systems Institute Samara Russia

ISBN: (纸本)9798350397338

To detect surface damage to buildings, it is necessary to involve workers who are at risk of industrial injuries when inspecting hard-to-reach areas of industrial premises. Attraction of special means, such as aerial platforms, safety systems, etc. increase the financial costs with this approach. The use of unmanned aerial vehicles, coupled with neural network algorithms, can simplify this procedure. Due to the inaccessibility, the problem of obtaining training data for neural networks arises, which can be solved by synthesizing them in a virtual environment. © 2023 IEEE.

关键词： Convolutional neural networks

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UAV to Cadastral Parcel Boundary Translation and Synthetic UAV image Generation Using Conditional-Generative Adversarial Network 1

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2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Khadanga, Ganesh Jain, Kamal Indian Inst Technol Roorkee Roorkee Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

The precise boundaries of the cadastral parcels from the Unmanned Aerial Vehicle (UAV) data are essential for any eGovernance application. The pix2pix, image-to-image translation using the conditional Generative Adversarial Network (cGAN) models, has emerged as an alternative to the traditional machine learning and image processing algorithms. It has been used and demonstrated for productive purposes in different domains without any change in the pix2pix network model and loss functions. The pix2pix model is implemented in this research for extracting the cadastral parcel boundaries using the existing UAV data set, and the corresponding digitised data. The input data set is prepared using the python modules. The model is also used to predict the synthetic UAV data from the map data. The predicted boundary of the model is very useful. The proposed model can reduce the manual labour and human interventions in outlining the parcel boundary from UAV data.

关键词： Pix2Pix image-to-image translation c-GAN CNN UAV Deep learning Parcel

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Review on Technological Advancement and Textual Data Management algorithms in NLP and CBIR systems 3rd

Review on Technological Advancement and Textual Data Managem...

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3rd International conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Diviya, M. Karmel, A. SCOPE VIT Chennai Campus Tamilnadu Chennai India

ISBN: (纸本)9789811664472

Natural language processing (NLP) and content-based image retrieval (CBIR) systems functioned efficiently with textual input. This offers a broader framework for on-going work such as text processing applications that include spam identification, content visualization, and image retrieval for the text being queried. The preprocessing of the text handling algorithm includes part of speech tagging (POS), text encoding, and text extraction function using the word embedding encoding algorithms like Word2vec, boot strapping, hidden Markov model (HMM), and so on. The derived textual input or image attribute plays a crucial role. Following this processing, the future phase can include either of the following approach, such as content-based recovery, image recovery, or speech processing. The techniques, such as content-based retrieval, image retrieval or speech processing, each of these algorithms requires their own training period in continuation with the available technical orientations. Convolutional neural network (CNN) and recurrent neural network (RNN) play a prominent role in analyzing the input dataset when it comes to testing. Generative adversarial networks (GANs) have now launched a new era in managing NLP applications in its place. This survey provides a glimpse of various algorithms that respond to text data and return with prominent accuracy involving CBIR issues and applications for text-based image synthesis. The examination of the methodologies deployed paves way for researchers to analyze and process textual data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Female or male observers ? - Gender differences in subjective image quality assessment 23

Female or male observers ? - Gender differences in subjectiv...

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6th International conference on Networking, Intelligent systems and Security, NISS 2023

作者： El Mayel, Fatima Ezzahra Dahmouni, Abdellatif Ait Abdelouahad, Abdelkaher Laroseri Laboratory Faculty of Sciences Chouaib Doukkali University ELjadida Morocco

ISBN: (纸本)9798400700194

Objective image quality assessment measures were extensively used to evaluate the performance of different image processing and analysis algorithms. However, they are application-driven. In contrast, subjective assessment measures are general-purpose and considered as the ground truth. Several Subjective image Quality Assessment (SIQA) methods have been proposed in the literature. These measures require normalized environment and standardized protocols and are mainly based on the visual judgments of image quality. Nevertheless, these judgments are strongly influenced by human factors which might skew the precision of the assessment. Consequently, when selecting observers panel, researchers take into account several criteria such as age, sex, ethnic origin, socio-cultural and professional. Following an in-depth analysis of recent state-of-the-art work, we found that no one has investigated a gender difference in human judgment. In this paper, we aim to highlight the effect of this property in the subjective evaluation tests that are conducted on the TID2008 database. Obtained results show that men and women judge the same distorted images differently, with high precision in favor of women, outperforming in several cases the results provided by the TID2008 database. © 2023 ACM.

关键词： image quality

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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Improving Multispectral image Classification with Convolutional Networks and Hyperspectral Band 4

Improving Multispectral Image Classification with Convolutio...

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4th International conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Nagarajan, S. Nagaraju, G. Chandra, K. Ramesh Sharmila, K. Soni Santhosh, K.N.S.K. Vanaja, Koppada Shri Vishnu Engineering College for Women Department of Cse Bhimavaram India Department of Ece Bhimavaram India Vishnu Institute of Technology Department of Ece Bhimavaram India Aditya University Department of Csm Surampalem India Dnr college of engineering and technology Department of Ece Bhimavaram India

ISBN: (纸本)9798331529635

This research work proposes an efficient framework to improve multispectral image classification through convolutional neural networks (CNNs) with optimized hyperspectral band selection. Hyperspectral images contain extensive spectral information, but the high dimensionality introduces redundant data, impacting processing efficiency and model performance. The proposed approach employs a hybrid band selection technique that combines mutual information and clustering algorithms to identify the most discriminative spectral bands before feeding them into a CNN model. This process enhances classification accuracy while significantly reducing computational load. Experimental results on the Indian Pines and Salinas hyperspectral datasets demonstrate the effectiveness of the proposed method. With our optimized band selection, the CNN achieved an overall classification accuracy of 95.7% on the Indian Pines dataset, an increase of 6.4 percentage points over the full-band CNN baseline accuracy of 89.3%, and reduced training time by 43%. Similarly, on the Salinas dataset, classification accuracy improved from 90.8% to 96.1%, with a 47% reduction in computation time. These results highlight the potential of hyperspectral band optimization in CNN-based classification, providing a robust and efficient approach for high-accuracy, real-time multispectral imaging applications. © 2024 IEEE.

关键词： Convolutional neural networks

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