本文選題：三維激光掃描 + 點云��； 參考：《東華理工大學(xué)》2013年碩士論文

【摘要】：點云特征的提取在人臉識別，計算機視覺，數(shù)字娛樂，地形學(xué)等領(lǐng)域有非常重要的使用價值。而三維激光掃描的誕生，給予點云數(shù)據(jù)全新的生命，如何對點云數(shù)據(jù)進行特征解剖，是現(xiàn)在刻不容緩的技術(shù)要求。傳統(tǒng)的特征提取技術(shù)并不能滿足現(xiàn)在技術(shù)要求，如何改進算法來獲得更加完美的點云各要素特征，是本文主要的研究內(nèi)容。 針對激光掃描獲得的散亂數(shù)據(jù)，本文一方面介紹傳統(tǒng)的點、線、面特征提取方法，另一方面研究如何對特征進行快速、準(zhǔn)確的提取。主要研究內(nèi)容有： 1）敘述了貝塞爾、B-樣條和NURBS三種方法在曲線和曲面提取中的原理，并用Matlab分別作圖比較了三種方法提取線、面的優(yōu)缺點； 2）對基于曲率極值法及其改進法提取特征點進行闡述并進行實例比較分析； 3）敘述折線生長法，采用設(shè)定閥值減少候選特征點來改進特征線的提取，利用手掌數(shù)據(jù)進行實例研究分析； 4）分析傳統(tǒng)最小二乘的缺點，并對其加以改進，，采用高程分域，把點云數(shù)據(jù)分塊，然后對分塊數(shù)據(jù)進行最小二乘，相臨邊界用最小二乘值平均值擬合，最后加以光滑處理。采用Matlab對16個控制點進行擬合比較； 利用了2組不同類型的數(shù)據(jù)整體對其進行特征點、線、面的提取，對本文改進的算法進行分析驗證。從實驗例證可以得出：本文算法對表面復(fù)雜目標(biāo)的點、線提取更加有優(yōu)勢；在面提取分塊上有待進一步簡化，但整體連貫性好。
[Abstract]:Point cloud feature extraction is very important in face recognition, computer vision, digital entertainment, topography and other fields. The birth of 3D laser scanning gives a new life to point cloud data. How to dissect the point cloud data is an urgent technical requirement. The traditional feature extraction technology can not meet the current technical requirements. How to improve the algorithm to obtain more perfect features of the elements of the point cloud is the main research content of this paper. Based on the scattered data obtained by laser scanning, this paper introduces the traditional methods of feature extraction of points, lines and surfaces on the one hand, and studies how to extract features quickly and accurately on the other hand. The main research contents are as follows: 1) the principle of Besselger B-spline and NURBS in curve and surface extraction is described, and the advantages and disadvantages of the three methods are compared by Matlab. 2) the feature points are extracted based on curvature extremum method and its improved method, and the examples are compared and analyzed. 3) the method of broken line growth is described. The method of reducing candidate feature points by setting threshold value is used to improve the extraction of feature lines, and the palm data is used for case study and analysis. 4) the shortcomings of traditional least squares are analyzed and improved. The point cloud data is partitioned by elevation domain, then the block data is partitioned by least square method, and the boundary is fitted with the mean value of least square value, and finally the smooth processing is carried out. The 16 control points were fitted and compared by Matlab. Two groups of different types of data are used to extract the feature points, lines and surfaces, and the improved algorithm is analyzed and verified. From the experimental examples, it can be concluded that the algorithm has more advantages in line extraction for points with complex surface targets, and needs to be further simplified in the area of surface extraction, but the overall coherence is good.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P225.2

【引證文獻】

相關(guān)碩士學(xué)位論文 前1條

1 雒峫;文物三維模型特征線提取及外表面和斷裂面標(biāo)識研究[D];西北大學(xué);2014年

本文編號：1780584