用智能路径删除保留图像关键结构，实现无畸变缩放。
An Analysis and Implementation of Seam Carving for Content-Aware Image Resizing
- 通过寻找低重要性像素路径动态调整图像尺寸
- 支持缩放、放大及对象保护，保持图像核心内容
- 提供能量图可视化与路径调试功能，适合图像处理研究者
缝合裁剪（Seam carving）是一种经典的自适应图像缩放方法，通过反复移除或插入8连通的单调像素路径（即“缝合线”），以局部重要性最低的区域为依据进行图像宽高调整。由于缝合线会绕过显著内容而避开均匀缩放或裁剪造成的失真，该方法能有效保留图像中的关键结构，同时丢弃或复制低能量区域。本文描述了一种基于Avidan和Shamir（2007）原始框架的C++实现，包含后续Rubinstein、Shamir和Avidan（2008）提出的可选前向能量准则。实现支持图像缩小、按序插入缝合线实现放大、多轮放大处理大尺度缩放、用户提供的权重掩码用于保护或移除特定对象，并支持能量图导出与缝合线可视化。文章详细阐述了算法流程、参数设置与计算复杂度，讨论了设计选择与原始描述的差异，并在自然图像上展示了该操作的行为表现。
原文摘要 · Abstract (English)
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.
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