label n. 1.紙條,貼條,標簽,簽條。 2.稱號,綽號。 3.標記,符號。 4.(字典中用的)說明性略語。 5.(有膠水的)郵票[印花稅票]。 6.【建筑】披水石。 7.〔古語〕布條,帶子;封文件的絲帶。 attach a label on ... 在…上加標簽。 union label 〔美國〕(證明產(chǎn)品確是工會會員制成或銷售的)工會標簽。 acquire the label of 得了…的綽號。 vt. (〔英國〕 -ll- ) 1.貼標簽于,用簽條標明。 2.把…叫做,把…列為。 3.(用放射性同位素)使(元素或原子)示蹤;(用示蹤原子)使(化合物等)示蹤。 label a trunk for Paris 給箱子貼上運往巴黎的標簽。 The bottle is label(l)ed “Poison”. 瓶上標明“有毒”。 label sb. (as) a turncoat 把某人叫做叛徒。
In the area at the left . if you do not have a pouch , affix the folded label using clear plastic shipping tape over the entire label area 來獲得。如果您沒有郵袋,則使用干凈的塑料運輸膠帶將已折疊的標簽粘貼在整個標簽區(qū)域上。
In the area at the left . if you do not have a pouch , affix the folded label using clear plastic shipping tape over the entire label area 索取ups標簽pak袋及其他項目。若您沒有透明塑膠袋,您可使用透明膠紙貼上摺疊的標簽,并固定在包裹上。
This paper elaborates the main idea and some details of the two approaches that include labeling areas , choosing the source node and the target node , searching the neighbor nodes , choosing the evaluation fimction and dealing with the failure 包括算法的基本思想以及算法實現(xiàn)中的幾個關(guān)鍵技術(shù):區(qū)域類型的標記、起始節(jié)點和目標節(jié)點的確定、相鄰節(jié)點的搜索、估計函數(shù)的選擇以及失敗的處理。
Now there are two basic target recognition strategies , such as processing from bottom to top , which is called data - driving method , and processing from top to bottom , which is called knowledge - driving method . the former begins with low layer processing for example , general segmentation , label and feature extraction , then judges whether the feature vector extracted from the labeled area is in accordance with the feature vector of the object model . the latter firstly brings forward a hypothesis on probably existed feature , secondly proceeds with purposeful segmentation , label and feature extraction , lastly judges whether the feature vector extracted from the labeled area is in accordance with the feature vector of the object model 目標識別在工農(nóng)業(yè)生產(chǎn)、國防建設(shè)中具有極其重要的地位,目前目標識別的算法常用的有兩種,一種是由下而上的數(shù)據(jù)驅(qū)動型策略,即不管目標屬于何種類型,一律先對原圖像進行一股性的分割、標記和特征抽取等低層次處理,然后將每個帶標記的已分割區(qū)域的特征矢量與目標模型相匹配;另一種是由上而下的知識驅(qū)動型策略,即先對圖像中可能存在的特征提出假設(shè),根據(jù)假設(shè)進行有目的地分割、標記和特征抽取,在此基礎(chǔ)上與目標模型進行精確匹配。