MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03896ntm a2200289 a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
vtls000099662 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251117113326.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
170502t2016 my da f am 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0001265(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905271527 |
| Level of effort used to assign nonsubject heading access points |
atie |
| Level of effort used to assign subject headings |
201710161243 |
| Level of effort used to assign classification |
aishah |
| -- |
201705021020 |
| -- |
saini |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FSKKP .S88 2016 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sutarman |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Correlation-based subset evaluation of feature selection for dynamic Malaysian sign language / |
| Statement of responsibility, etc. |
Sutarman |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 208 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD ROM |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computer Systems and Software Engineering |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Doctor of Philosophy in Computer Science) -- Universiti Malaysia Pahang – 2016 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography : p. 108-116 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Sign language is used for communication to the deaf and speech impaired. For communication between the common man and the deaf, sign language interpreter is needed for understanding natural language and vice versa. Sign Language Recognition (SLR) aims to translate sign language into text so that the communication between the deaf and the general public can be done comfortably. Research in Sign Language Recognition (SLR) has been widely done by researchers from many various countries using different datasets. In the existing work of Sign Language Recognition, most researchers divide the process in four main steps: image acquisition, pre-processing, features extraction and classification. The success for the classification process is determined by many factors. One factor is the quality of the data or information held. The process of data model extraction will be more difficult if the information held is irrelevant or contains redundancies, or if the data obtained contains high noise. Thus by adding processes before classification methods such as feature selection methods can provide better data input in the classification process, it is expected to improve the performance of the method of classification. Feature selection potential is used in SLR. Currently, there is no research work that used Feature Selection on Sign Language Recognition. In this study, the Correlation-based Feature Subset Evaluation (CfsSubsetEval) and Artificial Neural Network (ANN) has been proposed, in order to improve the accuracy rate on the recognition of sign language. The data samples tested were 15 dynamic signs taken from the Malaysian Sign Language (MySL). Pre-processing in this study was based on tracking the joints on a skeleton feature for generating 3D coordinates X, Y, Z. The sample of 3D data coordinates of X, Y, and Z axis is a value relative to the torso and head. In this study, the images has been captured using a kinect sensor based skeletal algorithms. The feature extraction was done by normalizing the position and size of the user, by taking eight out of 20 joints that contribute in identifying the movement of the hands; left hand, right hand, left wrist, right wrist, left elbow, right elbow, torso and head. CfsSubsetEval and Artificial Neural Network have been compared with Consistency-based Subset Evaluation (CSE) and Correlation-based Attribute Evalualtion (CorrelationAttributeEval) for performance analysis on result accuracy. In this study, spherical coordinate conversion process and segmentation frame using mean function were used. The experiments have achieved 95.56 % in accuration rates for Correlation-based Feature Subset Evaluation (CfsSubsetEval). |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computer Systems and Software Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and Colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://ecollib.ump.edu.my/25916/">http://ecollib.ump.edu.my/25916/</a> |
| Public note |
Library access only |