Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66214
Title: An efficient automatic palm reading algorithm and its mobile applications development
Authors: Leung, KP
Law, NF 
Keywords: Android
Automatic
Health
Java
Mobile
OpenCV
Palm reading
Personality
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: ICSPCC 2016 - IEEE International Conference on Signal Processing, Communications and Computing, Conference Proceedings, 2016, 7753706 How to cite?
Abstract: Palm reading is a traditional practice in China for a few thousand years to tell ones' fortune. Currently, there is a lack of mobile applications that allow palm reading to be done automatically and efficiently. This study aimed at developing an effective palm reading algorithm which can run in an Android platform efficiently. OpenCV and Java were used for the implementation. Our palm reading algorithm uses an adaptive thresholding approach to segment the palm image from the background, extract the fingers and calculate their length, extract the three principal palm lines in which regression is applied to produce connected and continuous palm lines. The algorithm was implemented as an Android application. Results showed that the algorithm can be run within 2 to 4 seconds, and the automatic palm reading can be done on mobile platforms accurately. The study enriched existing market of mobile applications that aim at palm reading. With successful implementation of such platform, and by collecting more personal information of the users, such as personality and health status, this application can be applied for future research on the prediction of personality and health.
Description: 2016 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2016, Hong Kong, 5-8 August 2016
URI: http://hdl.handle.net/10397/66214
ISBN: 9781509027088
DOI: 10.1109/ICSPCC.2016.7753706
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

11
Checked on Sep 25, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.