Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
Automatic image retargeting with fisheye-view warping
Proceedings of the 18th annual ACM symposium on User interface software and technology
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
Optimized scale-and-stretch for image resizing
ACM SIGGRAPH Asia 2008 papers
Multi-operator media retargeting
ACM SIGGRAPH 2009 papers
Optimized image resizing using seam carving and scaling
ACM SIGGRAPH Asia 2009 papers
A system for retargeting of streaming video
ACM SIGGRAPH Asia 2009 papers
Energy-based image deformation
SGP '09 Proceedings of the Symposium on Geometry Processing
Image retargeting using importance diffusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Saliency detection for content-aware image resizing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A comparative study of image retargeting
ACM SIGGRAPH Asia 2010 papers
Visibility maps for improving seam carving
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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We propose a new energy function for seam carving based on forward gradient differences to preserve regular structures in images. The energy function measures the curvature inconsistency between the pixels that become adjacent after seam removal, and involves the difference of gradient orientation and magnitude of the pixels. Our objective is to minimize the differences induced by the removed seam, and the optimization is performed by dynamic programming based on multiple cumulative energy maps, each of which corresponds to the seam pattern associated with a pixel. The proposed technique preserves straight lines and regular shapes better than the original and improved seam carving, and can be easily combined with other types of energy functions within the seam carving framework. We evaluated the performance of our algorithm by comparing with the original and improved seam carving algorithms using public data.