kruppa造句
例句與造句
- The number of independent kruppa constraints from n images
N幅圖像的kruppa方程的獨(dú)立個(gè)數(shù) - Step - by - step self - calibration algorithm of digital camera based on kruppa equations
方程的相機(jī)分步自標(biāo)定方法 - Under changeable intrinsic parameter circumstance , a linear camera self - calibration method is proposed in this paper
在可變內(nèi)參數(shù)模型下，本論文還提出了一種基于kruppa方程的線性自標(biāo)定方法。 - 4 zeller c , faugeras o . camera self - calibration from video sequences : the kruppa equations revisited . research report 2793 , inria , 1996
以上研究具有一定的理論價(jià)值: 1我們會(huì)知道要完全自標(biāo)定一個(gè)變參數(shù)的攝像機(jī)是不可能的 - However , to our knowledge , a formal proof of how many independent kruppa constraints exist out of these n ones is unavailable in the literature
那么，這些kruppa方程中，有多少是獨(dú)立的呢？或者說，由這些方程最多可以標(biāo)定多少個(gè)攝像機(jī)的內(nèi)參數(shù)呢？ - It's difficult to find kruppa in a sentence. 用kruppa造句挺難的
- Zhan - yi hu , yi - hong wu , fu - chao wu , and song - de ma national laboratory of pattern recognition , institute of automation , chinese academy of sciences beijing 100080 , p . r . china
暫缺自1992年faugeras , luong ,及maybank介紹kruppa方程以來, kruppa方程一直為攝像機(jī)自標(biāo)定領(lǐng)域中應(yīng)用最廣泛的技術(shù) - Secondly , it determines the camera " s intrinsic calibration parameters with a simple form of the kruppa equations ( self - calibration ) , which reduces the possibility of human involvement
其次，利用簡(jiǎn)化的kruppa方程進(jìn)行攝像機(jī)自標(biāo)定，估計(jì)攝像機(jī)內(nèi)參數(shù)矩陣，降低了人機(jī)交互的可能性。 - First , we summarize the recent development in related research areas : vr and ar , traditional graphics and ibmr . then we discuss self - calibration based on two photographs . we reach our aim via kruppa equation
然后討論了如何實(shí)現(xiàn)基于兩幅照片的攝像機(jī)自定標(biāo)，我們通過kruppa方程較好地解決了這個(gè)問題。 - 8 sturm p . a case against kruppa s equations for camera self - calibration . in proc . ieee international conference on image processing , chicago , illinois , october , 1998 , pp . 172 - 175
工程領(lǐng)域中的許多問題歸根結(jié)底轉(zhuǎn)化為方程組的求解問題,如果未知參數(shù)的個(gè)數(shù)多于獨(dú)立方程的個(gè)數(shù),在理論上,這些參數(shù)無法通過這個(gè)方程組求解得到 - Through the reasonable assumption of intrinsic parameters , we can avoid the general nonlinear and ambiguity in solving kruppa equation . at last , the camera self - calibration can be simplified to solving a quadratic equation
通過對(duì)攝像機(jī)內(nèi)參數(shù)的合理假設(shè)，避免了一般使用kruppa方程標(biāo)定時(shí)的非線性和多義性，并最終將固定內(nèi)參數(shù)下的標(biāo)定問題簡(jiǎn)化為一個(gè)二次方程的求解。 - In this paper , we prove that given n images captured by a pinhole camera with varying parameters and under general motion , the number of independent kruppa constraints is 5n - 9 , and it is less than that of independent constraints from the absolute quadric by only one
本文首次從代數(shù)上嚴(yán)格證明了，攝像機(jī)在通常運(yùn)動(dòng)下當(dāng)攝像機(jī)的內(nèi)參數(shù)變化時(shí),從n n 3幅圖像得到的所有kruppa方程中，只有5n - 9個(gè)是獨(dú)立的 - This one - constraint - less property of the kruppa equations is their inherent deficiency and is independent of camera motion . this deficiency is due to their failure of automatic enforcement of the rank - three - ness on the absolute quadric
從而, kruppa方程的獨(dú)立個(gè)數(shù)比基于絕對(duì)二次曲面或基于無窮遠(yuǎn)平面的自標(biāo)定方程的獨(dú)立個(gè)數(shù)少1 ,這是源于kruppa方程不能保證絕對(duì)二次曲面的秩為3 ,是由kruppa方程自身的特性決定的，而與攝像機(jī)的運(yùn)動(dòng)無關(guān)。 - In the first part , depending on three or more images , the main research work are listed as follows : ( l ) using svd decomposition to realize projective reconstruction ; ( 2 ) realizing camera self - calibration by solving kruppa ' s equation ; ( s ) recovering euclidean reconstruction from projective reconstruction . depending on only two images , the main researches are : ( l ) making out infinite plane homography matrix by using scene structure information , then recovering affine reconstruction from projective reconstruction ; ( 2 ) making out the absolute conic images by using scene structure information , and then recovering euclidean reconstruction from projective reconstruction
在第一部分中，針對(duì)三幅及三幅以上的圖像，主要研究：利用矩陣奇異值分解( svd )實(shí)現(xiàn)射影重構(gòu)，通過求解kruppa方程實(shí)現(xiàn)攝像機(jī)自標(biāo)定，由射影重構(gòu)恢復(fù)歐氏重構(gòu)；針對(duì)只有兩幅圖像的情況，主要研究：利用場(chǎng)景結(jié)構(gòu)信息求解無窮遠(yuǎn)平面的單應(yīng)矩陣，由射影重構(gòu)恢復(fù)仿射重構(gòu)，利用場(chǎng)景結(jié)構(gòu)信息求解絕對(duì)二次曲線的像(等價(jià)于標(biāo)定攝像機(jī)) ，由仿射重構(gòu)恢復(fù)歐氏重構(gòu)。 - In the self - calibration scheme , the thesis emphasizes the accuracy of camera intrinsic and extrinsic parameters . we presents an accurate f method based on corresponding point adjustment . the method adjusts coresponding points according to the fixedness of projective transformed cross ratio , then calculates f matrix accurately through linear and non - linear methods . when computing intrinsic parameter , a matrix , we simplify the step , and stress on the two important parameters of a . the result will be getten through solving kruppa equation based on svd decomposition . in order to compute extrinsic parameters , we use linear method to get initial r and t , then apply non - linear method to accurate them
提出了基于匹配點(diǎn)調(diào)整的f求精方法，先根據(jù)攝影交比不見性對(duì)手工選擇的匹配點(diǎn)進(jìn)行調(diào)整，再用線性、非線性結(jié)合的方法求精f矩陣；在計(jì)算內(nèi)部參數(shù)a中，進(jìn)行了一定的簡(jiǎn)化，把重心放在a中重要的兩個(gè)參數(shù)上，用svd分解法計(jì)算kruppa方程；在計(jì)算外部參數(shù)時(shí)，首先用線性法求解r 、 t ，然后再用非線性法迭代求精。 - We discuss the traditional camera self - calibration methods based on kruppa ' s equations and propose a new method for solving kruppa ' s equations - step method . ill first we use conjugate gradient method to estimate the unknown scale factors in kruppa ' s equations , then use the scale factors to solve kruppa ' s equations linearly and calibrate the camera intrinsic parameters
提出一種求解安徽大學(xué)博士論文kruppa方程的新方法一分步算法，先利用共軛梯度法估計(jì)knjpa方程中的未知比例因子，然后利用所確定的比例因子線性地求解knjpa方程，進(jìn)而標(biāo)定攝像機(jī)內(nèi)參數(shù)。