Demonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications

This paper proposes frequency-domain correlation filtering to solve object recognition of three-dimensional (3D) targets. We perform a linear correlation in the frequency domain between an input frame of the video sequence and a designed filter. This operation measures the correspondence between the two signals. In order to produce a high matching score, we design a bank of correlation filters, in which each filter contains unique information of the target in a single view and statistical parameters of the scene. In this paper, we demonstrate the feasibility of correlation filters used to solve 3D object recognition and their robustness to different image conditions such as noise, cluttered background, and geometrical distortions of the target. The evaluation performance presents a high accuracy in terms of quantitative metrics.

[1]  Vitaly Kober,et al.  Accuracy of location measurement of a noisy target in a nonoverlapping background , 1996 .

[2]  B. V. K. Vijaya Kumar,et al.  Correlation filters with controlled scale response , 2006, IEEE Transactions on Image Processing.

[3]  Vitaly Kober,et al.  Accurate three-dimensional pose recognition from monocular images using template matched filtering , 2016 .

[4]  Fernand S. Cohen,et al.  Synthesis and identification of three-dimensional faces from image(s) and three-dimensional generic models , 2017, J. Electronic Imaging.

[5]  Vitaly Kober,et al.  Target tracking in nonuniform illumination conditions using locally adaptive correlation filters , 2014 .

[6]  Jiangping Mei,et al.  Monocular vision for pose estimation in space based on cone projection , 2017 .

[7]  B. Kumar,et al.  Performance measures for correlation filters. , 1990, Applied optics.

[8]  Oscar Montiel,et al.  Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering , 2018 .

[9]  Silvestar Sesnic,et al.  Stochastic Collocation Applications in Computational Electromagnetics , 2018 .

[10]  Ting Liu,et al.  Visual measurement system for roadheaders pose detection in mines , 2016 .

[11]  Scott T. Acton,et al.  Manifolds for pose tracking from monocular video , 2015, J. Electronic Imaging.

[12]  Jifeng Sun,et al.  Monocular three-dimensional human pose estimation using local-topology preserved sparse retrieval , 2017, J. Electronic Imaging.

[13]  Vitaly Kober,et al.  Real-time tracking of multiple objects using adaptive correlation filters with complex constraints , 2013 .

[14]  Bahram Javidi,et al.  Design of filters to detect a noisy target in nonoverlapping background noise , 1994 .