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A Novel 3-D Image Retargeting by using Stereo Seam Carving with Disparity Map Acquisition (DMA) Algorithm

机译:立体缝拼接结合视差图获取(DMA)算法的新型3-D图像重定向

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Gaining popularity in digital world imposes the challenge to preserve the semantic significances in the original image while display on any arbitrary device irrespective of its size or aspect ratio is the well-known ‘Image Retargeting’. When the image is to be tailored by focusing on its objectives tenacity, then the insignificant portions of the images are identified and wipe out. The historical method envisages the due respect to the pixels by considering its bottom to top style. On the contrary, the projected method builds by using top-down tactic. This appraisal is fused by ‘Classification guided Fusion Network (CFN). The feature is widely applied on 3D images which fuses left as well as right eye images which are having different viewpoints and differently designed. The disparity map acquisition algorithm fuses the images with semantic collage of the images.
机译:在数字世界中越来越流行,众所周知的“图像重定向”是在原始图像中保留语义含义的挑战,同时在任意设备上显示而不管其大小或纵横比如何。如果要通过专注于其目标韧性来裁剪图像,则可以识别图像的不重要部分并清除掉。历史方法通过考虑像素从下到上的样式来设想对像素的应有的尊重。相反,该投影方法是使用自上而下的策略构建的。这项评估与“分类指导的融合网络(CFN)”进行了融合。该功能已广泛应用于融合了不同视点和不同设计的左眼图像和右眼图像的3D图像。视差图获取算法将图像与图像的语义拼贴融合在一起。

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