基於全局相似轉換的猜測之自然影像拼接

Abstract

此篇論文提出將多張影像盡可能自然拼接之方法，我們的方法是將影像鋪格子後使用局部的變形模型，而目標函式被設計成可用來決定變形模型的特性，像是影像對齊及最小化變形程度，除此之外，我們增加各影像之全局相似轉換的猜測至目標函式中，此猜測限制各影像之變形，使得各影像在整體上盡可能保留猜測之相似轉換，關於全局相似轉換的猜測對於拼接結果的自然度有至關重要的影響，我們提出一些方法選擇關於相似轉換中的縮放及旋轉之猜測。所有影像的變形模型將求解於最小化所有影像的整體變形，根據各個拼接結果顯示我們的方法皆優於目前幾項最先進的方法，包含 AutoStitch、APAP、SPHP 及 ANNAP。

This thesis proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for an image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.