CASAIR: Content and Shape-Aware Image Retargeting and Its Applications
This paper proposes a novel image-retargeting algorithm that can retarget images to a large family of nonrectangular shapes. Specifically, we study image retargeting from a broader perspective that includes the content as well as the shape of an image, and the proposed content and shape-aware image-retargeting (CASAIR) algorithm is driven by the dual objectives of image content preservation and image domain transformation, with the latter defined by an application-specific target shape. The algorithm is based on the idea of seam segment carving that successively removes low-cost seam segments from the image to simultaneously achieve the two objectives, with the selection of seam segments determined by a cost function incorporating inputs from image content and target shape. To provide a complete characterization of shapes that can be obtained using CASAIR, we introduce the notion of bhv-convex shapes, and we show that bhv-convex shapes are precisely the family of shapes that can be retargeted to by CASAIR. The proposed algorithm is simple in both its design and implementation, and in practice, it offers an efficient and effective retargeting platform that provides its users with considerable flexibility in choosing target shapes. To demonstrate the potential of CASAIR for broadening the application scope of image retargeting, this paper also proposes a smart camera–projector system that incorporates CASAIR. In the context of ubiquitous display, CASAIR equips the camera– projector system with the capability of retargeting images online in order to maximize the quality and fidelity of the displayed images whenever the situation demands.
PROJECT OUTPUT VIDEO:
Early retargeting work, such aim to preserve important and salient objects in the image by segmentation, cropping and pasting. While laying the groundwork for future development, the effectiveness of these methods, particularly in an automated and unsupervised setting, is somewhat limited due to their simplicity. For instance, important notions such as preserving scene consistency are more difficult to manage using operations such as cropping and pasting that usually alter the image in a non-incremental way. Seam carving introduced in is perhaps the first of the new-generation image retargeting methods that go much beyond the limit of these earlier methods. The algorithm incrementally alters the image by carving away seams, sequences of low-energy pixels running vertically across the image. Its simplicity allows many extensions. Image warping has been applied to image retargeting and it has generated a whole new family of retargeting algorithms. The idea of image warping and retargeting is to achieve an optimal deformation of the image that preserves salient regions using a mesh associated with the image, the vertices of which are acquired by computing the global minimum of a quadratic energy function. A less geometrical but more analytical approach is the idea of shift-map first introduced The method aims to manipulate values in the ‘shift-map’ in order to rearrange the image content, and this particular feature makes it ideally suited for image retargeting.
DISADVANTAGES OF EXISTING SYSTEM:
Selection based on the longest interval often produces results with better preservations of the overall image structure
The display area is detected by projecting predesigned patterns and matching the corresponding features points between the patterns and their captured images.
In presents the details of CASAIR, the proposed algorithm that retargets an image based on its content to a user-specified target shape. The retargeting algorithm takes in two inputs: an image I and a target shape ST given as a binary rectangular image with the target shape defined by ST = 1. We will denote by D the rectangular image domain of I with w, h denoting its width and height. Without loss of generality, we will also assume that the rectangular image domain DT for the binary image ST has smaller width and height than D. Additionally, the user can provide a saliency map s for the image I as the optional third input. A saliency map is a nonnegative function s : D → R + whose value at a given pixel indicates its importance. 1) Shape Placement: The binary shape image ST is placed on the image I based on the total saliency value presented in the specified shape. 2) Initial Retargeting: The image is first retargeted to the rectangular domain DT ⊂ D of the target shape using seam carving. 3) Seam Segment Carving: The image is then retargeted to the desired shape by removing seam segments. The first step aims to place the shape ST at an advantageous location to initiate the retargeting, and a simple guiding principle for determining the placement is to consider the total saliency value in the area covered by the target shape.
ADVANTAGES OF PROPOSED SYSTEM:
The algorithm is simple in concept and in implementation, and in experiments, we have demonstrated its efficiency and effectiveness the lowest-cost seam segment can be determined efficiently using dynamic programming as in other seam carving-based methods.
System : Pentium Dual Core.
Hard Disk : 120 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 1GB.
Operating system : Windows 7.
Coding Language : MATLAB
Tool : MATLAB R2013A
Shaoyu Qi, Yu-Tseh (Jason) Chi, Adrian M. Peter, and Jeffrey Ho, “CASAIR: Content and Shape-Aware Image Retargeting and Its Applications”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 25, NO. 5, MAY 2016.