411 research outputs found
Fast Deep Matting for Portrait Animation on Mobile Phone
Image matting plays an important role in image and video editing. However,
the formulation of image matting is inherently ill-posed. Traditional methods
usually employ interaction to deal with the image matting problem with trimaps
and strokes, and cannot run on the mobile phone in real-time. In this paper, we
propose a real-time automatic deep matting approach for mobile devices. By
leveraging the densely connected blocks and the dilated convolution, a light
full convolutional network is designed to predict a coarse binary mask for
portrait images. And a feathering block, which is edge-preserving and matting
adaptive, is further developed to learn the guided filter and transform the
binary mask into alpha matte. Finally, an automatic portrait animation system
based on fast deep matting is built on mobile devices, which does not need any
interaction and can realize real-time matting with 15 fps. The experiments show
that the proposed approach achieves comparable results with the
state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read
Rotational motion deblurring of a rigid object from a single image
Most previous motion deblurring methods restore the degraded image assuming a shift-invariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2-D rigid body movement and propose a practical framework to deblur rotational motions from a single image. Our main observation is that the transparency cue of a blurred object, which represents the motion blur formation from an imaging perspective, provides sufficient information in determining the object movements. Comparatively, single image motion deblurring using pixel color/gradient information has large uncertainties in motion representation and computation. Our results are produced by minimizing a new energy function combining rotation, possible translations, and the transparency map using an iterative optimizing process. The effectiveness of our method is demonstrated using challenging image examples. anteed since the convolution with a blur kernel is noninvertible. To tackle this problem, additional image priors, such as the global gradient distribution from clear images [7], are proposed. Some approaches use multiple images or additional visual cues [2, 20] to constrain the kernel estimation. (a) (b
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Defocus Video Matting
Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.Engineering and Applied Science
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