🤖 AI Summary
This work addresses the limitations of traditional image scaling methods, which often distort semantically important regions. The authors implement a content-aware seam carving algorithm that efficiently computes minimum-energy 8-connected monotonic seams via dynamic programming, supporting image reduction, ordered insertion for enlargement, multi-pass resizing, and user-guided object preservation or removal through masks. The system faithfully reproduces both Avidan and Shamir’s original seam carving approach and Rubinstein’s forward energy criterion, while also providing visualizations of energy maps and carved seams. Experimental results demonstrate that the method effectively preserves salient content across diverse natural images, significantly outperforming conventional scaling techniques and confirming its robustness and practical utility.
📝 Abstract
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.