Probabilistic interpretation of population codes
Neural Computation
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Simultaneous Segmentation of Range and Color Images Based on Bayesian Decision Theory
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2006 Papers
Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Combining Color, Depth, and Motion for Video Segmentation
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Smoothed local histogram filters
ACM SIGGRAPH 2010 papers
Incremental computation of feature hierarchies
Proceedings of the 32nd DAGM conference on Pattern recognition
Segmenting color images into surface patches by exploiting sparse depth data
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
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Segmentation is an important preprocessing step in many applications. Compared to colour segmentation, fusion of colour and depth greatly improves the segmentation result. Such a fusion is easy to do by stacking measurements in different value dimensions, but there are better ways. In this paper we perform fusion using the channel representation, and demonstrate how a state-of-the-art segmentation algorithm can be modified to use channel values as inputs. We evaluate segmentation results on data collected using the Microsoft Kinect peripheral for Xbox 360, using the superparamagnetic clustering algorithm. Our experiments show that depth gradients are more useful than depth values for segmentation, and that channel coding both colour and depth gradients makes tuned parameter settings generalise better to novel images.