| dc.contributor | 資科系 | |
| dc.creator (作者) | Liao, Wen-Hung | |
| dc.creator (作者) | 廖文宏 | zh_TW |
| dc.date (日期) | 2012 | |
| dc.date.accessioned | 10-Apr-2015 16:38:23 (UTC+8) | - |
| dc.date.available | 10-Apr-2015 16:38:23 (UTC+8) | - |
| dc.date.issued (上傳時間) | 10-Apr-2015 16:38:23 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74477 | - |
| dc.description.abstract (摘要) | We present a systematic approach to reduce the dimensionality of the feature vector for local binary/ternary patterns. The proposed framework examines the distribution of uniform patterns in different image sets to formulate a procedure to assign dimensionality to uniform and non-uniform patterns. Unlike previous methods where all the information from non-uniform patterns is discarded or merged into a single dimension, the proposed commensurate dimensionality reduction (CDR) technique attempts to retain valuable information from all contributory factors. Experiments and comparative analysis have validated the efficacy of the newly defined CDR-ELTP descriptor in terms of noise resistance and texture classification. © 2012 ICPR Org Committee. | |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceedings - International Conference on Pattern Recognition | |
| dc.subject (關鍵詞) | Comparative analysis; Contributory factors; Descriptors; Dimensionality reduction; Feature vectors; Image sets; Local ternary patterns; Noise resistance; Non-uniform patterns; Texture classification; Software engineering; Pattern recognition | |
| dc.title (題名) | Commensurate dimensionality reduction for extended local ternary patterns | |
| dc.type (資料類型) | conference | en |
