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| Title: | 三焦張量在多視角幾何中的計算與應用
Computation and Applications of Trifocal Tensor in Multiple View Geometry |
| Authors: | 李紹暐
Li, Shau Wei |
| Contributors: | 何瑁鎧
Hor, Maw Kae
李紹暐
Li, Shau Wei |
| Keywords: | 三焦張量
多視角影像
極線轉換
最小中值平方法
投影矩陣
trifocal tensor
multiple view images
epipolar transfer
LMedS
projection matrix |
| Date: | 2010 |
| Issue Date: | 2013-09-04 17:09:18 (UTC+8) |
| Abstract: | 電腦視覺三維建模的精確度，仰賴影像中對應點的準確性。以前的研究大多採取兩張影像，透過極線轉換(epipolar transfer)取得影像間基礎矩陣(fundamental matrix)的關係，然後進行比對或過濾不良的對應點以求取精確的對應點。然極線轉換存在退化的問題，如何避免此退化問題以及降低兩張影像之間轉換錯誤的累積，成為求取精確三維建模中極待解決的課題。
本論文中，我們提出一套機制，透過三焦張量(trifocal tensor)的觀念來過濾影像間不良的對應點，提高整體對應點的準確度，從而能計算較精確的投影矩陣進行三維建模。我們由多視角影像出發，先透過Bundler求取對應點，然後採用三焦張量過濾Bundler產生的對應點，並輔以最小中值平方法(LMedS)提升選點之準確率，再透過權重以及重複過濾等機制來調節並過濾對應點，從而取得精確度較高的對應點組合，最後求取投影矩陣進行電腦視覺中的各項應用。
實作中，我們測詴了三組資料，包含一組以3ds Max自行建置的資料與兩組網路中取得的資料。我們先從三張影像驗證三焦張量的幾何特性與其過濾對應點的可行性，再將此方法延伸至多張影像，同樣也能證實透過三焦張量確實能提升對應點的準確度，甚至可以過濾出輸入資料中較不符合彼此間幾何性的影像。
The accuracy of 3D model constructions in computer vision depends on the accuracy of the corresponding points extracted from the images. Previous studies in this area mostly use two images and compute the fundamental matrix through the use of the epipolar geometry and then proceed for corresponding point matching and filtering out the outliers in order to get accurate corresponding points. However, the epipoler transform suffers from the degenerate problems and, also, the accumulated conversion errors during the corresponding matches both will degrade the model accuracy. Solving these problems become crucial in reconstructing accurate 3D models from multiple images.
In this thesis, we proposed a mechanism to obtain accurate corresponding points for 3D model reconstruction from multiple images. The concept of trifocal tensor is used to remove the outliers in order to improve the overall accuracy of the corresponding points. We first use Bundler to search the corresponding points in the feature points extracted from multiple view images. Then we use trifocal tensor to determine and remove the outliers in the corresponding points generated by Bundler. LMedS is used in these processes to improve the accuracy of the selected points. One can also improve the accuracy of the corresponding points through the use of weighting function as well as repeated filtering mechanism. With these high
precision corresponding points, we can compute more accurate fundamental matrix in order to reconstruct the 3D models and other applications in computer vision.
We have tested three sets of data, one of that is self-constructed data using the 3ds Max and the other two are downloaded from the internet. We started by demonstrating the geometric properties of trifocal tensor associated with three images and showed that it can be used to filter out the bad corresponding points. Then, we successfully extended this mechanism to more images and successfully improved the accuracy of the corresponding points among these images. |
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[28] http://vision.middlebury.edu/mview/
[29] http://en.wikipedia.org/wiki/Epipolar_geometry
[30] http://phototour.cs.washington.edu/
[31] http://phototour.cs.washington.edu/bundler/
[32] http://cvlab.epfl.ch/~strecha/multiview/denseMVS.html |
| Description: | 碩士
國立政治大學
資訊科學學系
98753035
99 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0098753035 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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