feature vector extraction 中文意思是什麼

feature vector extraction 解釋
特徵向量抽取
  • feature : n 1 形狀，外形；特色；（特指）好看的外表；〈pl 〉臉形；五官；面目，容貌，面貌，相貌。2 臉面的一部...
  • vector : n 1 【數學】向量，矢量，動徑。2 【航空】飛機航線；航向指示。3 【天文學】幅，矢徑。4 【生物學】帶...
  • extraction : n. 1. 抽出，拔出。2. 【化學】提取（法）；萃取（法）；回收物，提出物；精煉。3. 精選，摘要。4. 血統，家世，出身。5. 【數學】開方，求根。
  1. Second, we design a chinese text clustering model ctcm and research main aspects of ctcm such as feature presentation, feature extraction, the adjust of feature vector and clustering algorithm. third, we lay emphasis on the study of text clustering algorithm

    然後，我們設計了一個中文文本聚類模型ctcm （ chinesetextclusteringmodel ） ，並針對模型中涉及到的特徵表示、特徵提取、特徵向量調整和聚類演算法等問題進行了研究。
  2. In feature extraction step, we apply homogeneity into text detection, and we compare using the gradient, edge extract and homogeneity mapping to enhance corners and texture features, and then use a slip window to get different kinds of texture features as the feature vector, and then after comparing the accuracy result of svm and bp neural network, we choose svm as the classifier

    在特徵提取步驟中，本文把一致性h應用到文本區域提取領域，使用邊緣空間映射和一致性h空間映射兩種方法得到特徵圖像，並比較了兩種空間對于文本提取的影響;對得到特徵圖像，使用滑動窗口比較了提取不同維數的紋理特徵作為特徵向量的結果。
  3. In these two methods a vector pattern is firstly partitioned into a set of sub - patterns, i. e. each sub - pattern in this set is only a part of the original vector pattern. after the partition, traditional pca & flda are used on these sub - pattern sets for sub - feature extraction

    它首先將模式數據適當的分成若干個獨立的子模式，然後分別對其子模式集使用pca和flda方法進行特徵的提取，最後將所有獲得的子特徵作為模式的最後特徵並用於分類。
  4. Pca & flda ), knowledge - based methods and neural - networks based methods, etc. in this thesis two novel classes of feature extraction methods are proposed, i. e. matrix - pattern - based and vector subpattern - based representation methods respectively

    在本文中，我們在pca和flda方法的基礎上提出了兩類特徵提取新方法，即基於矩陣模式和基於子向量的特徵提取方法，並隨後用于模式的分類。
  5. An image segmentation method based on velocity feature vector for moving target extraction

    基於速度特徵矢量提取運動目標的圖像分割方法
  6. This paper discussed the feature vector extraction methed, the realization of baum _ welch algorithm and the principle of hmm method

    論文論述了特徵矢量的獲取， hmm的原理和baum _ welch演算法的實現方法。
  7. 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

    目標識別在工農業生產、國防建設中具有極其重要的地位，目前目標識別的演算法常用的有兩種，一種是由下而上的數據驅動型策略，即不管目標屬於何種類型，一律先對原圖像進行一股性的分割、標記和特徵抽取等低層次處理，然後將每個帶標記的已分割區域的特徵矢量與目標模型相匹配；另一種是由上而下的知識驅動型策略，即先對圖像中可能存在的特徵提出假設，根據假設進行有目的地分割、標記和特徵抽取，在此基礎上與目標模型進行精確匹配。
  8. In this article, the wavelet transform is used to improve the recognition ability of vehicle ' s characters recognition system, because the " microscope " feature and similar - human vision feature can be used in the image analyze. three problems have been solved in this article : ( l ) the core method of recognition is to realize the extraction of stable feature of characters, the algorithm of wavelet feature vector has been given based on the directivity decompose of 2 - dimention wavelet transform, varied grid feature vector has been built too according to the multi - mode owned by the characters

    本文選用小波變換作為數學工具，利用小波的「顯微鏡」特性和類人視覺特點解決車牌字元的分析和識別問題，以提高車牌字元的識別能力，主要研究了三個方面的問題： （ 1 ）識別研究方法的核心是完成字元穩定性特徵的提取，採用圖像小波變換的方向性分解構造出小波特徵向量，並根據字元本身具有的多模態性提出了一種變網格小波特徵向量。
  9. The main achievements are summarized as follows : 1. based on deeply studying the feature of the gabor transformation, a characteristic vector for describing points " feature is defined with the gabor transformation to realize feature point extraction

    主要工作如下： 1 、深入研究了gabor變換的性質，利用gabor變換實現特徵點的提取，並且定義了一種描述點特徵的特徵向量。
  10. We raised a new model that we disassemble the character into several parts, which could be recognized by computer topologically based on the high - frequency wavelet coefficients vector, disregarding the traditional extraction method that used the statistical or structural feature based on the individual pixel in the 2 - dim plane of character. moreover, the concept of multi - dim cognizing feature model was put forward by encoding the character, according to its " location and run - length information. the information confusion and redundancy could be largely eliminated, as leaded to the improving of the preciseness when recognizing the character

    克服以往結構、統計方法在字元特徵提取中無法剔除噪聲、偏移等冗餘信息的不足，以認知的新思路分析圖像，給出基於小波子圖的筆劃定義，給出一種注重反映字元部分最為重要的筆劃的類型、數量、遊程、位置特徵，改進了基於字元二維圖像的統計與結構特徵提取方法因變形，畸變造成信息混淆和冗餘；給出了提取多屬性字元認知特徵的方法和識別機制，實驗表明，該方法能有效的識別字元； 3
  11. This algorithm applied to real system produces a good result. third we investigate the method for feature extraction, and put forward two methods base on wavelet analysis : coefficients character obtained by wavelet multi - resolution analysis and energy spectrum analysis using wavelet pack technique, from which a feasible feature vector is created

    研究了基於小波的特徵提取方法，提出了兩種基於小波變換的特徵提取方法：基於小波變換系數特徵和基於小波包分解的能量特徵，從而得到了故障信號的時域和頻域特徵。
  12. The prediction model of silicon content in hot metal tapped from blast furnace based on self - organized experience evolution is composed of following components : feature extraction and vector quantification of temporal process data, memory, storage, accumulation and evolution of the prediction experience

    高爐鐵水硅含量自組織經驗進化預測系統主要由動態數據特徵提取、動態模式自組織分類量化以及預測經驗的記憶、存儲、積累和進化三部分組成。
  13. For chinese phrase segmentation, a method of chinese phrase segmentation based on index was proposed and implemented, which has greater classifying efficiency compared with the traditional mechanical classification. for feature extraction, the method of mutual information was used, the disadvantages of traditional method of mutual information was analyzed, as well as the improving measures were brought out. for mail presentation, improved upon the traditional vector space model, a presentation method was proposed which is more suitable for the bayes computing

    在中文分詞方面，設計並實現了一種基於索引的中文分詞方法，提高了傳統的機械分詞法效率;在特徵提取方面，採用互信息值的方法，分析了傳統互信息方法的缺陷並給出了改進措施;在郵件的表示方面，對傳統的向量空間模型進行了改進，提出並採用了一種更加適合於貝葉斯計算的表示方法;用大量的測試樣本對引擎進行了測試，並就結果進行了討論分析。
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