Abstract
Effective resizing of images should not only use geometric constraints, but consider the image content as well. We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion. A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function. By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image. By applying these operators in both directions we can retarget the image to a new size. The selection and order of seams protect the content of the image, as defined by the energy function. Seam carving can also be used for image content enhancement and object removal. We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process. By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size.
Keywords
Affiliated Institutions
Related Publications
Seam carving for content-aware image resizing
Effective resizing of images should not only use geometric constraints, but consider the image content as well. We present a simple image operator called seam carving that suppo...
Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.
The stages of integration leading from local feature analysis to object recognition were explored in human visual cortex by using the technique of functional magnetic resonance ...
Rapid object detection using a boosted cascade of simple features
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This wor...
Recurrent Models of Visual Attention
Applying convolutional neural networks to large images is computationally ex-pensive because the amount of computation scales linearly with the number of image pixels. We presen...
Recurrent Models of Visual Attention
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present...
Publication Info
- Year
- 2007
- Type
- article
- Pages
- 10-10
- Citations
- 1369
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1145/1275808.1276390