掌紋型 的英文怎麼說
中文拼音 [zhǎngwénxíng]
掌紋型
英文
palm pattern-
Previous researchers have always determined the sp atial distribution patterns ( sdp ) of castanopsis kawakamii with a sample - dis tance method. however, the distribution patterns may be affected by the quadrat si ze and, in the course of analysis, the density differences among the cluster plots are not considered ; therefore, differences of cluster plot size and the dispersi on degree among individuals of cluster plots can not be known. authers of this pa per have determined the spatial distribution patterns of castanopsis kawakamii population in different habitats by means of non - quadrat distance method and a nalysed the pattern intensity and grain of the sdp. the pattern intensity is defi ned with the relative density differences and the pattern grain can embody the d ispersion degree of the individuals in the plots, and the dispersion degree among the plots. the determined results are as follows. the intensities of the species range in order from strong to week : litsea mollifolia p. kawakamii i. purpure a r. cochinchinensis c. kawakamii c. carlessii d. oldphamii s. superba. the gra ins of the species queue in order from coarse to close : s. superba = litsea mollif olia r. cohinchinensis c. kawakamii = i. purpurea c. carlessii p. racemosam d. oldp hamii. these determined results tally basiclly with the results authers of this paper have got in determining the same plots by means of aggregate index access ing method. in view of this, it is held that the sdp of c. kawakamii is closely related to the habitats and biological features
前人都是採用樣方方法對格氏栲種群數量的空間格局進行測定，而格局分佈有可能受樣方大小的影響，且分析過程中沒有涉及聚塊間密度差的問題，因而無法掌握種群的聚塊大小差別及聚塊內個體間的離散程度.本研究採用無樣方距離法，測定不同生境的格氏栲種群空間格局，分析格氏栲種群格局的強度和紋理.強度以聚塊和間隙的密度差來定義，紋理則是體現聚塊內個體間的離散程度與諸聚塊間的分離程度.測定結果表明，格氏栲種群格局強度從高到低排列次序為：木姜子蚊母樹冬青茜草樹格氏栲米櫧虎皮楠木荷；格局紋理從粗到細的順序是：木荷=木姜子茜草樹格氏栲=冬青米櫧蚊母樹虎皮楠.這一測定結果與作者採用聚集度指標測定相同樣地格氏栲種群空間格局的結果基本相符.因此，格氏栲空間格局類型及分佈與格氏栲生物學特性及生境的關系密切Each kind of palmprint pattern is described by parameters in wavelet domain that learned form samples by estimation
通過樣本學習，利用參數佑計的方法，每種掌紋模式的理想模型表示為小波域內的特定參變量。The paper abstracts out the palmprint mainline characteristic owing to palmprint image gradation by the gray value characteristic property method, defining the online palmprint characteristic space with the form of the polar coordinate and compressing the dimension, at last we use the neural network to accomplish the design of identification authentication system with the on - line palmprint as the mating algorithm. based on this, a fast and reliable system of identification authentication can be built
採用基於掌紋圖像灰度特性的方法提取出掌紋的主線特徵，以極坐標的形式定義在線掌紋的特徵空間並降維處理，最後用神經網路作為匹配演算法完成在線掌紋身份驗證系統的設計，以此為基礎建立了快速、可靠的新型身份認證系統。In order to deal with the unknown transformations of samples as a result of preprocessing and improve the system recognition performance further, a palmprint feature extraction method based on template learning in wavelet domain is designed and proposed. this algorithm learns the ideal templates of different classes from palmprint samples, whose parameters are regarded as
該演算法從掌紋樣本中學習不同類別的理想模板，並將理想模板的參數作為特徵用於模式分類，由於考慮了子圖樣本內在存在的平移和旋轉變換，不同類型小波系數對特徵提取的貢獻，因此達到非常好的識別效果。分享友人