Browsing by Author "Pan, Xiao"

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    Coarse to Fine:Weak Feature Boosting Network for Salient Object Detection
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhang, Chenhao; Gao, Shanshan; Pan, Xiao; Wang, Yuting; Zhou, Yuanfeng; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Salient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well-designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post-processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state-of-the-art methods.
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    SRF-Net: Spatial Relationship Feature Network for Tooth Point Cloud Classification
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Qian; Wei, Guangshun; Zhou, Yuanfeng; Pan, Xiao; Xin, Shiqing; Wang, Wenping; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    3D scanned point cloud data of teeth is popular used in digital orthodontics. The classification and semantic labelling for point cloud of each tooth is a key and challenging task for planning dental treatment. Utilizing the priori ordered position information of tooth arrangement, we propose an effective network for tooth model classification in this paper. The relative position and the adjacency similarity feature vectors are calculated for tooth 3D model, and combine the geometric feature into the fully connected layers of the classification training task. For the classification of dental anomalies, we present a dental anomalies processing method to improve the classification accuracy. We also use FocalLoss as the loss function to solve the sample imbalance of wisdom teeth. The extensive evaluations, ablation studies and comparisons demonstrate that the proposed network can classify tooth models accurately and automatically and outperforms state-of-the-art point cloud classification methods.