748 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
Edge-enhancing Filters with Negative Weights
In [DOI:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by
projecting the noisy image to a lower dimensional Krylov subspace of the graph
Laplacian, constructed using nonnegative weights determined by distances
between image data corresponding to image pixels. We~extend the construction of
the graph Laplacian to the case, where some graph weights can be negative.
Removing the positivity constraint provides a more accurate inference of a
graph model behind the data, and thus can improve quality of filters for
graph-based signal processing, e.g., denoising, compared to the standard
construction, without affecting the costs.Comment: 5 pages; 6 figures. Accepted to IEEE GlobalSIP 2015 conferenc
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Matting Anything
In this paper, we propose the Matting Anything Model (MAM), an efficient and
versatile framework for estimating the alpha matte of any instance in an image
with flexible and interactive visual or linguistic user prompt guidance. MAM
offers several significant advantages over previous specialized image matting
networks: (i) MAM is capable of dealing with various types of image matting,
including semantic, instance, and referring image matting with only a single
model; (ii) MAM leverages the feature maps from the Segment Anything Model
(SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha
matte through iterative refinement, which has only 2.7 million trainable
parameters. (iii) By incorporating SAM, MAM simplifies the user intervention
required for the interactive use of image matting from the trimap to the box,
point, or text prompt. We evaluate the performance of MAM on various image
matting benchmarks, and the experimental results demonstrate that MAM achieves
comparable performance to the state-of-the-art specialized image matting models
under different metrics on each benchmark. Overall, MAM shows superior
generalization ability and can effectively handle various image matting tasks
with fewer parameters, making it a practical solution for unified image
matting. Our code and models are open-sourced at
https://github.com/SHI-Labs/Matting-Anything.Comment: Project web-page:
https://chrisjuniorli.github.io/project/Matting-Anything
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