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A robust and fast approach for multiple image components stitching

机译:用于多个图像组件拼接的强大和快速方法

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In this paper, we present an algorithm for image stitching that avoids performance hindrance and memory issues in diverse image processing applications/ environments. High-resolution images could be cut into smaller pieces by various applications for ease of processing, especially if they are Sent over a computer network. Image pieces (from several highresolution images) could be stored as a single image-set with no information about the original images. We propose a robust stitching methodology to reconstruct the original high-resolution image(s) from a target image-set that contains components of various sizes and resolutions. The proposed algorithm consists of three major modules. The first step sorts image pieces into different planes according to their spatial position, size, and resolution. It avoids sorting overlapped pieces of the same resolution in the same plane. The second module sorts the pieces from different planes according to their content by minimizing a cost function based on Mean Absolute Difference (MAD). The third module relates neighboring pieces and determines output images. The proposed algorithm could be used at a pre-processing stage in applications such as rendering, enhancement, retrieval etc, as these cannot be carried out without access to original images as individual whole components.
机译:在本文中,我们提出了图像拼接,在不同的图像性能避免阻和内存问题处理的应用/环境的算法。高清晰度的图像可以切成小块由易于加工的各种应用,特别是如果他们被送到在计算机网络上。图像块(从几个高分辨率图像)可被存储为单个图像集而没有关于原始图像的信息。我们提出了一个强大的拼接方法,从包含各种尺寸和分辨率的组件的目标图像集重构原始的高分辨率图像(S)。该算法包括三个主要模块。第一步,根据它们的空间位置,大小和分辨率的图像进行排序块放入不同的平面。它避免排序在同一平面同一决议的重叠部分。第二模块根据通过最小化基于平均绝对差(MAD)的成本函数他们的内容进行排序,从不同的平面件。第三模块涉及相邻块,并确定输出图像。该算法可以在用于诸如渲染,增强,检索等的预加工阶段被使用,因为这些不能没有访问的原始图像作为单独的整体部件来进行。

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