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Optical Engineering

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor
Author(s): Kajal Sharma; Inkyu Moon; Sung Gaun Kim
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Paper Abstract

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

Paper Details

Date Published: 1 October 2012
PDF: 11 pages
Opt. Eng. 51(10) 107002 doi: 10.1117/1.OE.51.10.107002
Published in: Optical Engineering Volume 51, Issue 10
Show Author Affiliations
Kajal Sharma, Chosun Univ. (Korea, Republic of)
Inkyu Moon, Chosun Univ. (Korea, Republic of)
Sung Gaun Kim, Kongju National Univ. (Korea, Republic of)


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