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Appl. Sci. 2017, 7(6), 517;

An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

1,2,3,* , 2,* , 2
Department of Automation, University of Science and Technology of China, Hefei 230027, China
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
The Key Lab of Network and Intelligent Information Processing, Hefei University, Hefei 230601, China
Authors to whom correspondence should be addressed.
Academic Editor: Antonio Fernández-Caballero
Received: 12 March 2017 / Revised: 2 May 2017 / Accepted: 9 May 2017 / Published: 25 May 2017
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Recovering depth information of objects from two-dimensional images is one of the very important and basic problems in the field of computer vision. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object’s depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the most appropriate pair of straight line segments are chosen, and also reduce computation, convex hull theory and knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical. View Full-Text
Keywords: monocular image; image segment; SIFT; depth measurement; convex hull monocular image; image segment; SIFT; depth measurement; convex hull

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He, L.; Yang, J.; Kong, B.; Wang, C. An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature. Appl. Sci. 2017, 7, 517.

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