Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image fusion for context enhancement and video surrealism
Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Scale-Invariant Contour Completion Using Conditional Random Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
GrabcutD: improved grabcut using depth information
Proceedings of the 2010 ACM workshop on Surreal media and virtual cloning
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved video segmentation by adaptive combination of depth keying and mixture-of-gaussians
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Interactive 3D modeling of indoor environments with a consumer depth camera
Proceedings of the 13th international conference on Ubiquitous computing
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Depth-supported real-time video segmentation with the Kinect
WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Coherent Spatiotemporal Filtering, Upsampling and Rendering of RGBZ Videos
Computer Graphics Forum
Structure guided fusion for depth map inpainting
Pattern Recognition Letters
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Automatic object segmentation is a fundamentally difficult problem due to issues such as shadow, lighting, and semantic gaps. Edges play a critical role in object segmentation; however, it is almost impossible for the computer to know which edges correspond to object boundaries and which are caused by internal texture discontinuities. Active 3-D cameras, which provide streams of depth and RGB frames, are poised to become inexpensive and widespread. The depth discontinuities provide useful information for identifying object boundaries, which makes automatic object segmentation possible. However, the depth frames are extremely noisy. Also, the depth and RGB information often lose synchronization when the object is moving fast, due to different response time of the RGB and depth sensors. We show how to use the combined depth and RGB information to mitigate these problems and produce an accurate silhouette of the object. On a large dataset (24 objects with 1500 images), we provide both qualitative and quantitative evidences that our proposed techniques are effective.