Portrait segmentation has gained more and more attractions in recent years due to the popularity of selfie images. Compared to general semantic segmentation problems, portrait segmentation focuses on facial areas with higher requirements especially over the boundaries. To improve the performance of portrait segmentation, we propose a boundary-sensitive deep neural network (BSN) for better accuracy among the portrait boundaries. BSN introduces three novel techniques. First, an individual boundary-sensitive mask is proposed by dilating the contour line and assigning the boundary pixels with multi-class labels. Second, a global boundary-sensitive mask is employed as a position sensitive prior to further constrain the overall shape of the segmentation map. Third, we train a boundary-sensitive attribute classifier jointly with the segmentation network to reinforce the network with semantic boundary shape information. We have evaluated BSN on the state-of-the-art public portrait segmentation datasets, i.e., the PFCN dataset, as well as the portrait images collected from other three popular image segmentation datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior quantitative and qualitative performance over state-of-the-arts on the evaluated datasets, especially obtains better visualization effect on the portrait boundary region.
is generally regarded as a sub-problem of semantic segmentation, and it is different from traditional segmentation in two aspects. First, the foreground object is limited to only people which provides additional prior information. Meanwhile, portrait segmentation has higher precision requirement on boundary area. Shen al @cite_9 fine-tuned a portrait segmentation system from a pre-trained FCN network with portrait images. To provide more portrait-specific information to the network, two normalized @math and @math position channels and one mean mask shape channel are added to the input image. Shen al @cite_7 proposed a joint correspondence and segmentation estimation method by using extra information provided by dual-lens camera.
@cite_9 @cite_7
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Portraiture is a major art form in both photography and painting. In most instances, artists seek to make the subject stand out from its surrounding, for instance, by making it brighter or sharper. In the digital world, similar effects can be achieved by processing a portrait image with photographic or painterly filters that adapt to the semantics of the image. While many successful user-guided methods exist to delineate the subject, fully automatic techniques are lacking and yield unsatisfactory results. Our paper first addresses this problem by introducing a new automatic segmentation algorithm dedicated to portraits. We then build upon this result and describe several portrait filters that exploit our automatic segmentation algorithm to generate high-quality portraits. Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision. With dual-lens smart phones, such as iPhone 7Plus and Huawei P9, coming into the market, two images of slightly different views provide us new information to unify the two topics. We propose a joint method to tackle them simultaneously via a joint fully connected conditional random field (CRF) framework. The regional correspondence is used to handle textureless regions in matching and make our CRF system computationally efficient. Our method is evaluated over 2,000 new image pairs, and produces promising results on challenging portrait images.