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Depth SEEDS: Recovering Incomplete Depth Data using Superpixels

Michael Van den Bergh, Daniel Carton and Luc Van Gool
January 2013


Depth sensors have become increasingly popular in in- teractive computer vision applications. Currently, most of these applications are limited to indoor use. Popular IR- based depth sensors cannot provide depth data when ex- posed to sunlight. In these cases, one can still obtain depth information using a stereo camera set up or a special out- door Time-of-Flight camera, at the cost of a reduced qual- ity of the depth image. The resulting depth images are often incomplete and suffer from low resolution, noise and miss- ing information. The aim of this paper is to recover the missing depth information based on an extension of SEEDS superpixels [11]. The superpixel segmentation algorithm is extended to take depth information into account where available. The approach takes advantage of the boundary- updating property of SEEDS. The result is a clean segmen- tation that recovers the missing depth information in a low- quality depth image. We test the approach outdoors on an interactive urban robot. The system is used to segment a person in front of the robot, and to detect body parts for interaction with the robot using pointing gestures.

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  author = {Michael Van den Bergh and Daniel Carton and Luc Van Gool},
  title = {Depth SEEDS: Recovering Incomplete Depth Data using Superpixels},
  booktitle = {WACV},
  year = {2013},
  month = {January},
  keywords = {}