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Gradient-Based Approach for Fine Registration of Panorama Images

机译:基于梯度的全景图像精细配准方法

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This paper studies the application of gradient-based motion detection techniques (i.e., optical flow methods) for registration of adjacent images taken using a hand-held camera for the purposes of building a panorama. A general 8-parameter model or a more compact 3-parameter model is commonly used for transformation estimation. However, both models are approximations to the real situation when viewpoint position is not absolutely fixed but includes a small translation, and thus distortion and blurring are sometimes present in the final registration results. This paper proposes a new 5-parameter model that shows better result and has less strict requirement on good choice of unknown initial parameters. An analysis of disparity recovery range and its enlargement using Gaussian filter is also given.
机译:本文研究了基于梯度的运动检测技术(即光流方法)在配准使用手持相机拍摄的相邻图像时的应用,以建立全景图。通用的8参数模型或更紧凑的3参数模型通常用于变换估计。但是,当视点位置不是绝对固定而是包含很小的平移时,这两种模型都是真实情况的近似值,因此最终的配准结果有时会出现失真和模糊。本文提出了一个新的五参数模型,该模型显示出更好的结果,并且对未知初始参数的良好选择没有严格的要求。还给出了视差恢复范围及其使用高斯滤波器进行放大的分析。

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