Recognition of continuous Korean sign language using gesture tension model and soft computing technique
Cited 1 times inCited 2 times in
- Recognition of continuous Korean sign language using gesture tension model and soft computing technique
- Kim, JB; Bien, Zeungnam
- Continuous gesture; Gesture recognition; Sign language recognition; Gesture tension model; Soft computing
- Issue Date
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E87D, no.5, pp.1265 - 1270
- We present a method for recognition of continuous Korean Sign Language (KSL). In the paper, we consider the segmentation problem of a continuous hand motion pattern in KSL. For this, we first extract sign sentences by removing linking gestures between sign sentences. We use a gesture tension model and fuzzy partitioning. Then, each sign sentence is disassembled into a set of elementary motions (EMs) according to its geometric pattern. The hidden Markov model is adopted to classify the segmented individual EMs.
- Appears in Collections:
- EE_Journal Papers
- Files in This Item:
- There are no files associated with this item.
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.