一种改进和优化的具有密集前景对象的图像内容感知大小调整算法

Soumyakanti Roy, Tanmoy Dasgupta, Tapan Pradhan
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引用次数: 0

摘要

一幅图像通常由前景中重要的物体和背景中不那么重要的物体组成。内容感知图像调整大小或接缝雕刻是在调整图像大小的同时保持重要对象(前景)在适当的视觉显着性的过程。然而,标准算法经常在前景物体密集的图像中产生不可预测的扭曲。本文提出的优化的内容感知图像大小调整(OCAIR)算法,使用迭代图切割和边缘检测来生成基于图像重要部分的能量图,从而使调整后的图像不会出现不可预测的伪影。这里设计了一种改进的能量图生成算法,它不仅比以前可用的技术更快地标记出重要的前景元素,而且还使用该信息来计算在添加或删除接缝后可能发生的失真量(如果有的话)。这个过程比以前的算法快得多,允许对输入参数进行精确修改,以获得精心处理的最终图像。
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An Improved and Optimized Content-Aware Resizing Algorithm for Images with Densely Situated Foreground Objects
An image in general consists of a combination of significant objects in the foreground and not-so-significant objects in the background. Content aware image resizing or seam carving is a process of resizing an image while maintaining the significant objects (the foreground) in proper visual saliency. The standard algorithms, however, often generate unpredictable distortions in images with densely situated foreground objects. The optimized content aware image resizing (OCAIR) algorithm presented herein, uses iterative graph cuts and edge detection to generate an energy map based on the important sections of the image, so that the resized image does not exhibit unpredictable artefacts. An improved energy map generation algorithm is designed here, which not only marks out the important foreground elements quicker than previously available techniques, but also uses that information to quantity the amount of distortion (if any) that might take place after adding or deleting seams by means of calculating a distortion factor. The process being considerably faster than previous algorithms, allows precise modifications to the input parameters to obtain a well-doctored final image.
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