Compared with other semantic segmentation tasks, portrait segmentation requires both higher precision and faster inference speed. However, this problem has not been well studied in previous works. In this paper, we propose a lightweight network architecture, called Boundary-Aware Network (BANet) which selectively extracts detail information in boundary area to make high-quality segmentation output with real-time( >25FPS) speed. In addition, we design a new loss function called refine loss which supervises the network with image level gradient information. Our model is able to produce finer segmentation results which has richer details than annotations.
: Image matting has been applied for portrait image process for a long time. Traditional matting algorithms @cite_13 @cite_10 @cite_14 @cite_21 require a user defined trimap which limits their applications in automatic image process. Some works @cite_11 @cite_27 have proposed to generate trimap by using deep leaning models, but they still take trimap generation and detail refinement as two separated stages. In matting tasks, trimap helps to locate regions of interest for alpha-matte. Inspired by the function of trimap in matting tasks, we propose a boundary attention mechanism to help our BANet focus on boundary areas. Different from previous matting models, we use a two-branch architecture. Our attention map is generated by high-level semantic branch, and it is used to guide mining low-level features. DIM @cite_1 designs a compositional loss which has been proved to be effective in many matting tasks, but it is time-consuming to prepare large amount of fore-ground, back-ground images and high-quality alpha matte. @cite_17 presents a gradient-coinstency loss which is able to correct gradient direction on prediction edges, but it is not able to extract richer details. Motivated by that, we propose refine loss to obtain richer detail features on boundary area by using image gradient.
@cite_13 @cite_14 @cite_21 @cite_1 @cite_17 @cite_27 @cite_10 @cite_11
2604469346 2244837655 2740994353 2126369166
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems. Our deep model has two parts. The first part is a deep convolutional encoder-decoder network that takes an image and the corresponding trimap as inputs and predict the alpha matte of the image. The second part is a small convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values and sharper edges. In addition, we also create a large-scale image matting dataset including 49300 training images and 1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images. Experimental results clearly demonstrate the superiority of our algorithm over previous methods. This paper describes a new framework for video matting, the process of pulling a high-quality alpha matte and foreground from a video sequence. The framework builds upon techniques in natural image matting, optical flow computation, and background estimation. User interaction is comprised of garbage matte specification if background estimation is needed, and hand-drawn keyframe segmentations into "foreground," "background" and "unknown". The segmentations, called trimaps, are interpolated across the video volume using forward and backward optical flow. Competing flow estimates are combined based on information about where flow is likely to be accurate. A Bayesian matting technique uses the flowed trimaps to yield high-quality mattes of moving foreground elements with complex boundaries filmed by a moving camera. A novel technique for smoke matte extraction is also demonstrated. 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 image. 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. Video matting, or layer extraction, is a classic inverse problem in computer vision that involves the extraction of foreground objects, and the alpha mattes that describe their opacity, from a set of images. Modem approaches that work with natural backgrounds often require user-labelled "trimaps" that segment each image into foreground, background and unknown regions. For long sequences, the production of accurate trimaps can be time consuming. In contrast, another class of approach depends on automatic background extraction to automate the process, but existing techniques do not make use of spatiotemporal consistency, and cannot take account of operator hints such as trimaps. This paper presents a method inspired by natural image statistics that cleanly unifies these approaches. A prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground colour model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of such systems. The system is applied to several real image sequences that demonstrate the advantage that the unified approach can afford.