Depth-based descriptor for matching keypoints in 3D scenes

Authors

  • Karol Matusiak Lodz University of Technology
  • Piotr Skulimowski Lodz University of Technology
  • Pawel Strumillo Lodz University of Technology

Abstract

Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification.

References

T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors:

A Survey, Foundations and Trends in Computer Graphics and Vision.

Volume 3, Issue 3 , pp. 177-280 (2007)

ZED Stereo Camera, www.stereolabs.com, accessed 2018.03.27

Google Tango Project, get.google.com/tango, accessed 2018.03.27

Structure Sensor, structure.io, accessed 2018.03.27

D. R. dos Santos, M. A. Basso, K. Khoshelham, E. de Oliveira, N. L.

Pavan, G. Vosselman, Mapping Indoor Spaces by Adaptive Coarse-to-

Fine Registration of RGB-D Data, IEEE Geoscience and Remote Sensing

Letters, Volume 13, Issue 2 (2016)

O. Wasenmller, M. Meyer, D. Stricker, CoRBS: Comprehensive RGBD

benchmark for SLAM using Kinect v2, IEEE Winter Conference on

Applications of Computer Vision (2016)

M. Karpushin, G. Valenzise and F. Dufaux, Improving distinctivness of

BRISK features using depth maps, IEEE International Conference on

Image Processing (2015)

M. Bujacz, P. Skulimowski, and P. Strumillo. Naviton - a prototype

mobility aid for auditory presentation of three-dimensional scenes to the

visually impaired. J. Audio Eng. Soc, 60(9):696–708, 2012.

K. Matusiak, P. Skulimowski, and P. Strumillo. Object recognition in

a mobile phone application for visually impaired users. In 2013 6th

International Conference on Human System Interactions (HSI), pages

–484, June 2013.

K. Matusiak, P. Skulimowski, P. Strumillo, Unbiased evaluation of

keypoint detectors with respect to rotation invariance, IET Computer

Vision, Volume 11, Issue 7, 507-516 (2017)

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints,

International Journal of Computer Vision, Volume 60, Issue 2, 91-110

(2004)

H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features

(SURF), Computer Vision and Image Understanding, Volume 110, Issue

, 346-359 (2008)

E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient

alternative to SIFT or SURF, IEEE International Conference on Computer

Vision 2011

B. Steder, R. Bogdan, R. Kurt and K. W. Burgard, NARF: 3D Range

Image Features for Object Recognition, Workshop on Defining and Solving

Realistic Perception Problems in Personal Robotics at the IEEE/RSJ

Int. Conf. on Intelligent Robots and Systems (2010)

S. Leutenegger, M. Chliand and R. Y.Siegwart, BRISK: Binary Robust

invariant scalable keypoints, IEEE International Conference on Computer

Vision, pp. 2548 2555 (2011)

E. Rosten and T. Drummond, Machine Learning for High-Speed Corner

Detection, Computer Vision ECCV 2006, Volume 1, pp. 430-443 (2006)

S. Martull, M. P. Martorell, K. Fukui, Realistic CG Stereo Image Dataset

with Ground Truth Disparity Maps, ICPR2012 workshop TrakMark2012,

pp.40-42 (2012)

C. Choi, A. J. B. Trevor, H. I. Christensen, RGB-D edge detection

and edge-based registration, 2013 IEEE/RSJ International Conference on

Intelligent Robots and Systems (2013)

Q. Yu, J. Liang, J. Xiao, H. Lu, Z. Zheng, A Novel perspective

invariant feature transform for RGB-D images, Computer Vision and

Image Understanding, 167, 109-120 (2018)

E. R. Nascimento, G. L. Oliveira, M. F. M. Campos, A. W. Vieira

On the development of a robust, fast, lightweight keypoint descriptor,

Neurocomputing, 120, 141-155 (2013)

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Published

2018-07-20

Issue

Section

Image Processing