10-09-2016, 12:48 PM
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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 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. 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.
I.INTRODUCTION
The diversity and versatility of display devices today imposes new demands on digital media. For instance, designers must create different alternatives for web-content and design different layouts for different devices. Moreover, HTML, as well as other standards, can support dynamic changes of page layout and text. Nevertheless, up to date, images, although being one of the key elements in digital media, typically remain rigid in size and cannot deform to fit different layouts automatically.Other cases in which the size, or aspect ratio of an image must change, are to fit into different displays such as cell phones or PDAs, or to print on a given paper size or resolution. Standard image scaling is not sufficient since it is oblivious to the image content and typically can be applied only uniformly. Cropping is limited since it can only remove pixels from the image periphery More effective resizing can only be achieved by considering the image content and not only geometric constraints. We propose a simple image operator, we term seam-carving, and that can change the size of an image by gracefully carving-out or inserting pixels in different parts of the image. Seam carving uses an energy function defining the importance of pixels [1].
A seam is a connected path of low energy pixels crossing the image from top to bottom, or from left to right. By successively removing or inserting seams we can reduce, as well as enlarge, the size of an image in both directions [1].
For image reduction, seam selection ensures that while preserving the image structure, we remove more of the low energy pixels and fewer of the high energy ones. For image enlarging, the order of seam insertion ensures a balance between the original image content and the artificially inserted pixels. These operators produce, in effect, a content-aware resizing of images. We illustrate the application of seam carving and insertion for aspect ratio change, image retargeting, image content enhancement, and object removal.Furthermore, by storing the order of seam removal and insertion operations, and carefully interleaving seams in both vertical and horizontal directions we define multi-size images. Such images can continuously change their size in a content-aware manner. A designer can author a multi-size image once, and the client application, depending on the size needed, can resize the image in real time to fit the exact layout or the display. Seam carving can support several types of energy functions such as gradient magnitude, entropy, visual saliency, eye-gaze movement, and more[1].
The removal or insertion processes are parameter free; however, to allow interactive control, we also provide a scribble based user interface for adding weights to the energy of an image and guide the desired results. This tool can also be used for authoring multi-size images [1].
Simple methods such as scaling and cropping have clear drawbacks. Scaling the image in horizontal or vertical direction can be performed in real-time using interpolation and will preserve the global visual effects. However, scaling causes obvious distortion if the aspect ratio is different between the input and the output. The second approach is to crop the output to a window of the input image [5].
This method will discard too much information of interest if the output resolution is significantly lower than the input resolution. In this paper, we propose a new content-aware image resizing algorithm, which can resize an image by performing seam carving and scaling coherently. We define an image distance measure for quantifying the quality of a resizing result. The measure is useful for two purposes:
• As an objective function within an optimization process to generate a well-resized image
• To quantitatively compare and evaluate resized images generated by different methods[5].
Our optimized content-aware image resizing algorithm starts from the seam carving operation on the original image. After each seam is removed, we directly scale the current image to the target size and compute the distances to the original image[5]. Existing resizing methods are effective for single image. However, these methods do not give enough protection to the important content, and using them for stereo images will distort the depth perception[1].For stereo images, the resizing operator considers not only the distortion of shape, but also the distortion of depth. Depth perception is derived from the small differences in the location of homologous, or corresponding, points in the image pair incident on the retina of the eyes [1].
A general stereo image is composed of two planar images of the same scene from different viewpoints. The difference of corresponding points in the viewpoints generates disparity in the stereo image[1]. When the left and right eyes respectively view different viewpoints, the observer perceives depth depending on stereo image disparity. Clearly, stereo image resizing should consider the relevancy of stereo image pairs, retargeting both images without distorting the depth perception of the main content[1].
II.EXISTING SYSTEM
The seam carving method does not work automatically on all images. This can be corrected by adding higher level cues, either manual or automatic. Other times, not even high level information can solve the problem.ShaiAvidan and Ariel Shamir characterized two major factors that limit the seam carving approach. The first is the amount of content in an image. If the image is too condensed, in the sense that it does not contain ‘less important’ areas, then any type of content-aware resizing strategy will not succeed. The second type of limitation is the layout of the image content. In certain types of images, albeit not being condensed, the content is laid out in a manner that prevents the seams from bypassing important parts [1].Traditional methods of seam carving works only for a very small dataset. It works perfectly only for images in which large portions are occupied by the background without much change in the nature. With uniformity in the background the energy of the region decreases and the less interesting seams are removed. But if the background is smaller and has lots of edges within, in such a case the traditional method of seam carving fails. It also fails if the object of interest has large hollow regions in it as it decreases the overall energy and the region is eliminated from the image. In order to rectify these failures they need improvements in the traditional method
VII.RESULT AND DISCUSSION
We discussed about operator for content-aware resizing of images using seam carving. Seams are computed as the optimal paths on a single image and are either removed or inserted from an image. This operator can be used for a variety of image manipulations including: aspect ratio change, image retargeting, content amplification and object removal.
The operator can be easily integrated with various saliency measures, as well as user input, to guide the resizing process. In addition, we define a data structure for multi-size images that support continuous resizing ability in real time. There are numerous possible extensions to this work.
We would like to extend our approach to other domains, the first of which would be resizing of video. Since there are cases when scaling can achieve better results for resizing, we would like to investigate the possibility to combine the two approaches, specifically to define more robust multi-size images. We would also like to find a better way to combine horizontal and vertical seams in multi-size images