Robust hand detection using arm segmentation from depth data and static palm gesture recognition

Augustauskas, Rytis; Lipnickas, Arūnas

doi:10.1109/idaacs.2017.8095174

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

2023

Publication Types

Select...

Other3

Book2

Article1

Relationship

Self Cite0

Independent6

Authors

Journals

Cited by 7 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most problem that frequently occur is the background is similar to the object [17]. Currently some images also have depth data, with this data we can make segmentation more easily as in some existing research [18] [19].…”

Section: Related Workmentioning

confidence: 99%

DBSCAN for Hand Tracking and Gesture Recognition

Aditya

Tolle

Shih

2020

JITeCS

View full text Add to dashboard Cite

Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

show abstract

Section: Related Workmentioning

confidence: 99%