Survey on lie group machine learning

Mei, Lu; Li, Fanzhang

doi:10.26599/bdma.2020.9020011

Cited by 39 publications

(15 citation statements)

References 54 publications

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“…The advent of algorithms such as deep learning algorithms is just one reason for the progress in the field of hippocampal segmentation [ 120 , 121 ] . The increased availability of public datasets is also an important reason – many computer scientists outside the field of neuroimaging have developed many new algorithms using public datasets.…”

Section: Discussionmentioning

confidence: 99%

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

Pan

Liu

et al. 2021

Chinese J of Electronics

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The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the in‐depth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size, irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently, some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally, objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

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Section: Discussionmentioning

confidence: 99%

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

Pan

Liu

et al. 2021

Chinese J of Electronics

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“…It is more stable with respect to the variation of number and is computationally more efficient, with enormous potential in studying human motion prediction. Another representation is to utilize the Lie Group [61], which is a Riemannian manifold structure. In [28] and [60], they both adopted this method to encode the human pose.…”

Section: Motion Trajectorymentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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“…In the second phase we apply the data obtained and processed from phase 1 to create the required model, creating algorithms that will lead us on the right path [19].…”

Section: B Create Modelmentioning

confidence: 99%

Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

Huamaní¹,

Cunyarachi²

2021

IJETAE

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Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

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Survey on lie group machine learning

Cited by 39 publications

References 54 publications

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

3D Human Motion Prediction: A Survey

Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

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