Full metadata record
DC FieldValueLanguage
dc.contributor.author蔡志仁en_US
dc.contributor.authorTsai, Chi-Zinen_US
dc.contributor.author林昇甫en_US
dc.contributor.authorSheng-Fuu Linen_US
dc.date.accessioned2014-12-12T02:14:58Z-
dc.date.available2014-12-12T02:14:58Z-
dc.date.issued1995en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT840327009en_US
dc.identifier.urihttp://hdl.handle.net/11536/60263-
dc.description.abstract我們提出一個結合類神經網路及改良型鏈碼(chain code)特徵抽取技術的 自動目標辨識系統。我們將各種未遭遮蔽的目標物以及它們常遇到的遮蔽 情況(例如:目標物遭碉堡、 建築物、...等遮蔽) 建入資料庫(data base library),使得目標物在遭受遮蔽時仍可獲得高辨識率。整個系統的架構 包含前置處理(preprocessing),特徵抽取(feature extraction) 以及分 類器(classifier) 三個部份。前置處理包括影像分割及解決目標物旋轉 問題,使得此系統具有旋轉不變性。特徵抽取採用改良型鏈碼特徵抽取技 術。改良型鏈碼特徵主要是從物體局部邊界抽取更細部特徵藉以提高系統 辨識率及對遮蔽物的容忍度。由於改良型鏈碼特徵具有對目標物平移及大 小的不變性;因此整個系統具有旋轉,平移及大小的不變性。分類器採用 supervised fuzzy adaptive Hamming net,該神經網路具有線上學習的 功能。在實驗中,此系統具有不錯的辨識率且對遮蔽有不錯的容忍度。 We propose an automatic target recognition system that combines a neural net-work and an improved chain-code feature-extraction technique.The system's database library includes targets that are not occluded and targets with commonocclusions(such as blockhouses, buildings, etc.). The system is composed of three parts, preprocessor, feature extractor, and classifier. The preprocessorperforms image segmentation and makes the system results rotation invariant.The feature extractor uses an improved chain-code feature-extraction techniquethat extracts more detailed features from the target's local boundaries in order to increase the system's recognition rate.Because the extracted featuresare position and scale invariant, so the entire system is rotation, position, and scale invariant. The classifier uses a supervised fuzzy adaptive Hamming net that is able to learn on line. In experiments, the system achieved a good recognition rate and proved robust to occlusion.zh_TW
dc.language.isozh_TWen_US
dc.subject鏈碼zh_TW
dc.subject類神經網路zh_TW
dc.subject目標辨識zh_TW
dc.subjectChain codeen_US
dc.subjectNeural networken_US
dc.subjectTarget recognitionen_US
dc.title結合類神經網路及改良式鏈碼特徵抽取技術於自動目標系統zh_TW
dc.titleAn Automatic Target Recognition System Using an Improved Chain Code Feature Extraction Techniqueen_US
dc.typeThesisen_US
dc.contributor.department電控工程研究所zh_TW
Appears in Collections:Thesis