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检索条件"机构=Institute of Computer Science II - Computer Graphics"

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Ensuring Safety Drive through a Smart Helmet design using IOT 7

Ensuring Safety Drive through a Smart Helmet design using IO...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Akshara, C.V. Aakash, S. Padma Suresh, L. Usha Nandini, D. Paul Selvan, Mercy Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The smart helmet is a kind of protection gear that improves the rider's safety while operating a bike. The primary motive is to keep the motorcyclist safe. The attributes including alcohol detection, global positioning system, accelerometer, and fall detection can be employed to achieve this. As a consequence, it acts as a smart helmet. The first condition needs to be fulfilled for the bike to ignite, is that the person should have not consumed alcohol, followed by that the eye blink sensor detects the motion of eyeballs and if the driver falls is tired and closes eye for a period of time, then the sensor alerts the driver and when the accelerometer detects the change in motion, it alerts the GPS and GSM and it posts message to their nearby hospital and relatives. The advancement in our projects includes the temperature sensor which alerts during long hours of wearing helmet. Smart helmets can also help prevent head injuries. Some smart helmets can provide real-time information about the force, frequency and severity of head impacts. © 2024 IEEE.

关键词： Global positioning system

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Analysis and Performance Evaluation of various Artificial Intelligence techniques for Disease Prediction 7

Analysis and Performance Evaluation of various Artificial In...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Jany Shabu, S.L. Rishi, S. Padma Suresh, L. Praveen, S. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The framework of the proposed system utilizes AI, machine learning procedures and data handling methodologies to analyze, gather data and reports from clinics or patient facilities, and prognosticate reports that assist the outcomes used to foresee various kinds of infection and disease. The most recurrently occurring disease and its infection is the dominant source for the human's death. Clinical endlessly reports from different documents have been taken out to analyze and diagnose breast malignant growth or cancer, coronary or heart illness, and diabetes which are very frequent in present day people, and their wellbeing is significant in the finding. Today, propels in clinical innovation are assisting individuals with improving on the manner in which they give medical clinics and doctors' the most straightforward method for treating, oblige, and treat any infection they need. We have brought this sort of framework into our application, which can give positive and negative outcomes to make individuals' lives simpler to anticipate the sickness by bringing explicit data into their reports, and backing the theory of the infection with the choice of looking for exhortation good clinics and great specialists close to clients or guardians in our future enactments. © 2024 IEEE.

关键词： Logistic regression

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Assessment and Evaluation of Music Genre Classification by employing various AI Techniques 7

Assessment and Evaluation of Music Genre Classification by e...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Adithya, S. Aijith Prabhakar, U.T. Ankayarkanni, B. Anto Praveena, M.D. Padma Suresh, L. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Music genre classification, the task of automatically assigning audio recordings to specific genres, offers exciting possibilities for music recommendation systems, personalized playlists, and music analysis. This abstract explores the potential of (K-NN), a versatile Artificial Intelligence (AI) algorithm, in tackling this challenge- NN excels in its simplicity and interpretability. It classifies new music by identifying its closest neighbors within a labeled dataset based on predefined features, such as tempo, timbre, or spectral content. These neighbors then collectively "vote"on the genre of the new piece. The abstract delves into the feature engineering process, highlighting the crucial role of selecting relevant musical characteristics for accurate classification. Furthermore, the abstract discusses the impact of the "K"hyper parameter, which determines the number of neighbors considered in the voting process. Choosing the optimal "K"value balances accuracy and sensitivity to genre nuances. While K-NN offers advantages in ease of implementation and understanding, the abstract acknowledges its potential drawbacks. The storage requirements can be high for large datasets, and computational costs may increase with data size. It concludes by acknowledging the need for further research to address computational challenges and explore advanced feature engineering techniques to unlock the full potential of K-NN in this exciting domain. © 2024 IEEE.

关键词： Audio recordings

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Implementation of various AI techniques for Autism Spectrum Disorder Detection 7

Implementation of various AI techniques for Autism Spectrum ...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Raj, Aditya Ramani, Bhavyam Vijay Padma Suresh, L. Prince Mary, S. Mary Posonia, A. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

A complex neurological disorder affecting behavior, communication, and social interactions is known as autism spectrum disorder (ASD). For people with ASD, improving outcomes requires primordial detection and intervention. The diagnosis of ASD has been transformed by the combination of AI and ML, particularly in young children. In order to create reliable autism screening tools, researchers have explored a variety of machine learning algorithms, including Decision Trees, XGBClassifier, Random Forest, AdaBoost Classifier, CatBoost Classifier, KMeans to Artificial Neural Networks (ANN). Artificial Neural Networks, energised by the intricacies of the human brain, stand out for their remarkable ability to decipher complex patterns within data, thus proving effective in ASD prediction tasks. Nonetheless, ANN models often necessitate substantial data and computational resources for training. It is imperative to undertake rigorous comparative analyses based on accurate and efficient results to determine the most suitable algorithm for ASD prediction. Furthermore, continuous refinement and validation of these varied algorithms along with the representative and comprehensive datasets are vital to ensure their factuality and applicability in real world problems. © 2024 IEEE.

关键词： Random forests

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Validating ML algorithms for efficient Breast Cancer Prediction using K-fold 7

Validating ML algorithms for efficient Breast Cancer Predict...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Shabu, S. L. Jany Karthik, Janapati Vardhan, A. Harsha Suresh, L. Padma Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Breast cancer has always been a disease that affects a lot of people. There is vast range of factors that may lead to breast cancer and this makes it more difficult to detect at an early stage. The tissues called benign or malignant present in the breast profile are so identical that it makes detecting cancer very complex for the doctors. Performing various tests like uniformity of cell shape and thickness of tissues may result in no valuable insights. This gives rise to machine learning and artificial intelligence that can be used in the prognosis of breast cancer. Here we attempt at correlating diverse machine learning algorithms to effectively predict breast cancer over a dataset. The five diverse machine learning algorithms employed are Naïve Bayes, Decision Tree, Neural Networks (Multilayer Perceptron), SVM and K-nearest Neighbour. An effective comparative study has been done to filter out the best Algorithm based on their Performance. © 2024 IEEE.

关键词： Lung cancer

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Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection 31

Are Explainability Tools Gender Biased? A Case Study on Face...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Huber, Marco Fang, Meiling Boutros, Fadi Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (纸本)9789464593600

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Face recognition

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Unsupervised Face Recognition using Unlabeled Synthetic Data 17

Unsupervised Face Recognition using Unlabeled Synthetic Data

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Boutros, Fadi Klemt, Marcel Fang, Meiling Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Tu Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350345445

Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data. The training code and pretrained model are publicly available under https://***/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data. © 2023 IEEE.

关键词： Face recognition

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany TU Darmstadt Department of Computer Science Darmstadt Germany

ISBN: (纸本)9798350302493

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available https://***/meilfang/***. © 2023 IEEE.

关键词： Face recognition

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Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction 46th

Bigger Isn’t Always Better: Towards a General Prior for M...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Glaszner, Lukas Zach, Martin Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031851803

Diffusion models have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse problems

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Free-Viewpoint Visual Inspection via 3D Gaussian Splatting for Direct Template Matching 50

Free-Viewpoint Visual Inspection via 3D Gaussian Splatting f...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ito, Kenta Ueda, Shiori Mori, Shohei Sugano, Junichi Adachi, Hideyuki Saito, Hideo Information and Computer Science Keio University Yokohama Japan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria ViSCO Technologies Corporation Tokyo Japan

ISBN: (纸本)9781665464543

Machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component. © 2024 IEEE.

关键词： Template matching

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