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Over the past several years, deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this paper, we put forward a novel practical convolutional neural network for effective semantic segmentation. The network is comprised of three modules. The module of widening residual convolutional neural network is firstly used to generate a full-size segmentation with localization of object boundaries. Feature maps generated by widening residual network are integrated by the module called as refine residual feature pyramid. The module of rolling guidance edge reserved layer is used for improving segmentation boundary. The innovations of this paper consist of these points. Widening residual skipped connections are utilized to preserve low-level features allowing accurate boundary localization. The refine residual feature pyramid is incorporated to achieve the observation of existence of objects at multiple scale. For modeling long-range context dependence and increasing accuracy near object boundary, rolling guidance edge reserved layer are implemented. The experimental results, on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset, demonstrate the proposed method can offer an effective way of producing pixel-wise image labeling. [ABSTRACT FROM AUTHOR]

Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel‐wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low‐resolution encoder features to full resolution image with pixel‐wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive. [ABSTRACT FROM AUTHOR]

Random forest is a useful tool for face alignment/tracking. The method of regressing local binary features learned from random forest has achieved state-of-the-art performance both in fitting accuracy and speed. Despite the great success of this method, it has certain weaknesses: the number of available local binary features is rather limited and is not optimal for face alignment; the binary features inevitably lead to serious jitter when tracking a video sequence. To address these problems, we propose learning probability features from probabilistic random forest (PRF). The proposed PRF is the same as standard random forest except that it models the probability of a sample belonging to the nodes of a tree. By using the probability features, our method significantly outperforms the state-of-the-art in terms of accuracy. It also achieves about 60 fps for locating a few facial landmarks. In addition, our method shows excellent stability in face tracking. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a closed loop method to resolve the multi-view super-resolution problem. For the mixed-resolution multi-view case, where the input is one high-resolution view along with its neighboring low-resolution views, our method can give the super-resolution results and obtain a high-quality depth map simultaneously. The closed loop method consists of two parts: part I, stereo matching and depth maps fusion; and part II, super-resolution. Under the guidance of the estimated depth information, the super-resolution problem can be formulated as an optimization problem. It can be solved approximately by a three-step method, which involves disparity-based pixel mapping, nonlocal construction and final fusion. Based on the super-resolution results, we can update the disparity maps and fuse them into a more reliable depth map. We repeat the loop several times until obtaining stable super-resolution results and depth maps simultaneously. The experimental results on public dataset show that the proposed method can achieve high-quality performance at different scale factors. [ABSTRACT FROM AUTHOR]