相鄰取樣 的英文怎麼說
中文拼音 [xiānglīnqǔyàng]
相鄰取樣
英文
acent sample-
Plenty of experiments show that it can effectively improve the synthetic speed of wl algorithm at the same quality. a method for automatic recognizing the size of neighborhood based on statistics is also presented, which can decrease the number of input parameters and be convenient for user ' s operating in texture synthetic algorithms sample - based using mrf model. finally, the future works about the technique of texture synthesis are introduced
通過大量的實驗表明，在合成質量與wl演算法相近的情況下，該方法使wl演算法的合成速度大幅度地提高，取得了良好的加速效果；運用統計分析的方法，針對採用mrf模型的基於樣圖的紋理合成演算法，給了一種鄰域大小自動識別的方法，進一步減少了紋理合成演算法的參數輸入數量，更方便了用戶的操作。Thirdly, we present a new algorithm on texture synthesis, which not only has rapid speed with the help of " synthesis consistency " but also gives pixels in the image vision - related weight to get good result. fourthly, we present an open framework about tsfs with " shadow texture ". finally, we describe a new texture synthesis method based on multiple samples, which integrates patch - based technique and the principle of minimum neighborhood error between pixels, and synthesizes in a repeat way
該演算法不僅利用「紋理塊的連貫性」 ，加快了紋理合成的速度，而且通過給像素附上與視覺相關的權值，得到了比以往更好的合成結果；作為進一步的研究，本文還提出了利用「伴隨紋理」進行紋理合成的開放式框架；最後本文介紹了一種新的多樣圖紋理合成演算法，該演算法基於塊匹配技術與像素的鄰域誤差最小原則，同時採用多次合成的方式，對大多數紋理都取得了較好的合成效果。3 ) the initial coastline data which are acquired from chart by digitizer is a series of sampling point. coastlines are simulated with polylinesby connecting two neibour sampling points
獲取的數據為一些離散的采樣點，通過連接前後兩相鄰采樣點構成折線圖形來模擬海岸線。The optimized feature set feeds a 3 - class classification module, which is based on the traditional binary svm classifier. and the proposed linear programming svm reduces the burden of the svm classifier and improves its learning speed and classification accuracy. a new algorithm that combined svm with k nearest neighbor ( knn ) is presented and it comes into being a new classifier, which can not only improve the accuracy compared to sole svm, but also better solve the problem of selecting the parameter of kernel function for svm
在研究了數據挖掘、支持向量機及其有關技術的基礎上，建立了實現三類水中目標識別的svm方法；採用線性規劃svm解決了傳統二次規劃svm在海量樣本情況下導致的時間和空間復雜度問題；提出了將最近鄰分類與支持向量機分類相結合的svm - knn分類器應用於水中目標識別的思想，較好地解決了應用支持向量機分類時核函數參數的選擇問題，取得了更高的分類準確率。分享友人