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G. Bebis et al. (Eds.): ISVC 2011, Part I, LNCS 6938, pp. 291300, 2011. Springer-Verlag Berlin Heidelberg 2011
 

Summary: G. Bebis et al. (Eds.): ISVC 2011, Part I, LNCS 6938, pp. 291300, 2011.
Springer-Verlag Berlin Heidelberg 2011
Depth Map Enhancement Using Adaptive Steering
Kernel Regression Based on Distance Transform
Sung-Yeol Kim, Woon Cho, Andreas Koschan, and Mongi A. Abidi
Imaging, Robotics, and Intelligent System Lab,
The University of Tennessee, Knoxville, TN 37996, USA
Abstract. In this paper, we present a method to enhance noisy depth maps us-
ing adaptive steering kernel regression based on distance transform. Data-
adaptive kernel regression filters are widely used for image denoising by consi-
dering spatial and photometric properties of pixel data. In order to reduce noise
in depth maps more efficiently, we adaptively refine the steering kernel regres-
sion function according to local region structures, flat and textured areas. In this
work, we first generate two distance transform maps from the depth map and its
corresponding color image. Then, the steering kernel is modified by a newly-
designed weighing function directly related to joint distance transform. The
weighting function expands the steering kernel in flat areas and shrinks it in tex-
tured areas toward local edges in the depth map. Finally, we filter the noise in
the depth map with the refined steering kernel regression function. Experimen-
tal results show that our method outperforms the competing methods in objec-

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee
Koschan, Andreas - Imaging, Robotics, and Intelligent Systems, University of Tennessee

 

Collections: Computer Technologies and Information Sciences; Engineering