logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Gesture recognition using the multi-PDM method and Hidden Markov Model

delete2000-08-01
delete11
PRE
AI
C
Chung‐Lin Huang *
W
Wu, MS
S
Sheng-Hung Jeng
DOI：10.1016/S0262-8856(99)00042-6delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This paper introduces a multi-Principal-Distribution-Model (PDM) method and Hidden Markov Model (I-m IM) for gesture recognition. To track the hand-shape, it uses the PDM model which is built by learning patterns of variability from a training set of correctly annotated images. However, it can only fit the hand examples that are similar to shapes of the corresponding training set. For gesture recognition, we need to deal with a large variety of hand-shapes. Therefore, we divide all the training hand shapes into a number of similar groups, with each group trained for an individual PDM shape model. Finally, we use the HMM to determine model transition among these PDM shape models. From the model transition sequence, the system can identify the continuous gestures representing one-digit or two-digit numbers. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
gesture recognition
multi-PDM method
Hidden Markov Model
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Image and Vision Computing cover
Image and Vision Computing
IF:
4.2
Papers:
4.0K
Citations:
6.7K

Organization

No organization information available