Academic Journal
Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images.
| Τίτλος: | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images. |
|---|---|
| Συγγραφείς: | Ryselis, Karolis, Blažauskas, Tomas, Damaševičius, Robertas, Maskeliūnas, Rytis |
| Πηγή: | Sensors (14248220); May2022, Vol. 22 Issue 9, p3531-3531, 16p |
| Θεματικοί όροι: | Streaming video & television, Human body, Image sensors, Body image, Time perspective, Videos |
| Περίληψη: | The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time. [ABSTRACT FROM AUTHOR] |
| Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ryselis%2C+Karolis%22">Ryselis, Karolis</searchLink><br /><searchLink fieldCode="AR" term="%22Blažauskas%2C+Tomas%22">Blažauskas, Tomas</searchLink><br /><searchLink fieldCode="AR" term="%22Damaševičius%2C+Robertas%22">Damaševičius, Robertas</searchLink><br /><searchLink fieldCode="AR" term="%22Maskeliūnas%2C+Rytis%22">Maskeliūnas, Rytis</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); May2022, Vol. 22 Issue 9, p3531-3531, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Streaming+video+%26+television%22">Streaming video & television</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink><br /><searchLink fieldCode="DE" term="%22Image+sensors%22">Image sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Body+image%22">Body image</searchLink><br /><searchLink fieldCode="DE" term="%22Time+perspective%22">Time perspective</searchLink><br /><searchLink fieldCode="DE" term="%22Videos%22">Videos</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s22093531 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 3531 Subjects: – SubjectFull: Streaming video & television Type: general – SubjectFull: Human body Type: general – SubjectFull: Image sensors Type: general – SubjectFull: Body image Type: general – SubjectFull: Time perspective Type: general – SubjectFull: Videos Type: general Titles: – TitleFull: Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ryselis, Karolis – PersonEntity: Name: NameFull: Blažauskas, Tomas – PersonEntity: Name: NameFull: Damaševičius, Robertas – PersonEntity: Name: NameFull: Maskeliūnas, Rytis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 22 – Type: issue Value: 9 Titles: – TitleFull: Sensors (14248220) Type: main |
| ResultId | 1 |