发表机构
L’EMbeDS（L’EMbeDS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文分析并实现了遵循原始公式及前向能量准则的Seam Carving算法，支持多种图像缩放功能，可保留图像结构并在自然图像上验证了其效果。
AI 中文摘要
Seam carving是一种经典的内容感知图像缩放算子，通过反复移除或插入 seam（即具有局部最小重要性的8连通单调像素路径）来修改图像的宽度或高度。由于seam会绕开显著内容，而非对图像进行均匀缩放或裁剪，该算子可保留重要图像结构，同时丢弃或复制低能量区域。本文描述了该算子的C++实现，遵循Avidan与Shamir（2007）的原始公式，包含Rubinstein、Shamir与Avidan（2008）后续提出的可选前向能量准则。该实现支持图像缩小、通过有序seam插入实现图像放大、针对大缩放因子的多遍放大、用户提供的用于对象保护与移除的权重掩码，以及能量图的导出和seam的可视化。我们详细阐述了该算法、其参数及计算复杂度，讨论了相对于原始描述的设计选择，并在自然图像上展示了该算子的行为。
英文摘要
Seam carving is a classical content-aware image resizing operator that modifies the width or height of an image by repeatedly removing (or inserting) seams, i.e., 8-connected monotonic paths of pixels of locally minimal importance. Because seams bend around salient content rather than uniformly scaling or cropping it, the operator preserves vital image structures while discarding (or duplicating) low-energy regions. This article describes a C++ implementation of the operator that follows the original formulation of Avidan and Shamir (2007), including the optional forward-energy criterion subsequently introduced by Rubinstein, Shamir and Avidan (2008). The implementation supports image reduction, image enlargement via ordered seam insertion, multi-pass enlargement for large scale factors, a user-supplied weight mask for object protection and removal, along with dumping of energy maps and visualisation of seams. We detail the algorithm, its parameters and its computational complexity, discuss design choices with respect to the original descriptions, and illustrate the behaviour of the operator on natural images.
Commentsone column, 10 pages, 8 figures
