C. Fu

Publications
Influence

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

X. Li, H. Chen, Xiaojuan Qi, Q. Dou, C. Fu, P. Heng
Computer Science, Medicine
IEEE Transactions on Medical Imaging
21 September 2017

We propose a novel hybrid densely connected UNet, which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. Expand

Underexposed Photo Enhancement Using Deep Illumination Estimation

R. Wang, Q. Zhang, C. Fu, Xiaoyong Shen, W. Zheng, J. Jia
Computer Science
IEEE/CVF Conference on Computer Vision and…
1 June 2019

We introduce intermediate illumination in our network to associate the input with expected enhancement result, which augments the network's capability to learn complex photographic adjustment from expert-retouched input/output images. Expand

Solid texture synthesis from 2D exemplars

J. Kopf, C. Fu, D. Cohen-Or, O. Deussen, Dani Lischinski, T. Wong
Computer Science
SIGGRAPH '07
2007

We present a novel method for synthesizing solid textures from 2D texture exemplars by combining non-parametric texture optimization with histogram matching. Expand

Direction-Aware Spatial Context Features for Shadow Detection

X. Hu, Lei Zhu, C. Fu, J. Qin, P. Heng
Computer Science, Medicine
IEEE/CVF Conference on Computer Vision and…
12 May 2018

This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner and achieves 97% accuracy and 38% reduction on balance error rate. Expand

Recurrently Aggregating Deep Features for Salient Object Detection

X. Hu, Lei Zhu, J. Qin, C. Fu, P. Heng
Computer Science
AAAI
2018

We propose a novel deep saliency network equipped with recurrently aggregated deep features (RADF) to more accurately detect salient objects from an image by fully exploiting the complementary saliency information captured in different layers. Expand

Depth-Attentional Features for Single-Image Rain Removal

X. Hu, C. Fu, Lei Zhu, P. Heng
Computer Science
IEEE/CVF Conference on Computer Vision and…
1 June 2019

We design an end-to-end deep neural network, where we train it to learn depth-attentional features via a depth-guided attention mechanism and regress a residual map to produce the rain-free image output. Expand

Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection

Lei Zhu, Zijun Deng, +4 authors P. Heng
Computer Science
ECCV
8 September 2018

This paper presents a network to detect shadows by exploring and combining global context in deep layers and local context in shallow layers of a deep convolutional neural network (CNN). Expand

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

Lequan Yu, Shujun Wang, X. Li, C. Fu, P. Heng
Computer Science
MICCAI
16 July 2019

We present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images by additionally leveraging the unlabeled data. Expand

A handle bar metaphor for virtual object manipulation with mid-air interaction

P. Song, W. Goh, William Hutama, C. Fu, Xiaopei Liu
Computer Science
CHI
5 May 2012

A novel handle bar metaphor is proposed as an effective visual control metaphor between the user's hand gestures and the corresponding virtual object manipulation operations. Expand

Joint Bi-layer Optimization for Single-Image Rain Streak Removal

Lei Zhu, C. Fu, Dani Lischinski, P. Heng
Computer Science
IEEE International Conference on Computer Vision…
1 October 2017

We present a novel method for removing rain streaks from a single input image by decomposing it into rain-free background layer B and a rain-streak layer R. Expand

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