基於不均勻變形之影像拼接

Abstract

本論文提出了一基於不均勻變形(non-homogeneous warping)之影像拼接(image stitching)演算法。本方法自動拼接多張取自不同視角之照片，且不需利用影像分層或混合技術即可合成一張具多透視點之全景圖。我們基於一以形變為基礎之影像比對演算法，藉由限制相對應之特徵點於同一位置求得初始形變，接著進行粗略至精細(coarse-to-fine)比對策略以改進拼接結果，最後反覆優化求得多透視點全景圖。

This thesis proposes an automatic approach to multi-perspective image stitching. Differently from previous stitching methods using global transformation such as homography, we frame non-homogeneous warping into Lucas-Kanade approach and optimize for multi-perspective images. Our algorithm consists of two stages: alignment stage and aggregation stage. The former uses non-rigid alignment after applying feature-based registration, the latter iteratively solves for multi-perspective results. Experiments on the numbers of images taken from different view-points showed that our algorithm improves the image stitching without using layering or blending techniques.