A self-diagnosis under 2D projectivity for local descriptor base template matching

Hidehiro Ohki; Rin-ichiro Taniguchi; Tokihiro Kimura; Naomichi Sueda; Keiji Gyohten

doi:10.1117/12.2182925

30 April 2015 A self-diagnosis under 2D projectivity for local descriptor base template matching

Hidehiro Ohki, Rin-ichiro Taniguchi, Tokihiro Kimura, Naomichi Sueda, Keiji Gyohten

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95340W (2015) https://doi.org/10.1117/12.2182925
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

2D projectivity is an invertible mapping to present the perspective imaging of a world plane by projective translation, called homography. Good image feature have to be robust under 2D projectivity caused by any camera movements. In the standard performance evaluation of template matching, many real captured images of many scenes are ordinarily used. However it is not enough to evaluate the robustness under 2D projectivity in detail because the variations of real camera pose and position in the 3D world are limited and the capturing cost is expensive. During the early stage of the template matching development, an easy performance evaluation method is required to examine the behavior. We propose a self-diagnosis method to measure the robustness of local descriptor base template matching between a template image and reference images which are created by projective translation of the template image. We focus on the template matching consisting of a feature point extraction and a local descriptor matching. The proposed method evaluates the spatial accuracy of the feature points and the estimated template positions in the reference images with local descriptor matchings. Four metrics, feature point precision (PP), feature point recall (PR), local descriptor matching precision (MP) and local descriptor matching recall (MR) are introduced to evaluate the performance. The experiment results will be appeared in the final manuscript to show the effectiveness of our method.

Citation Download Citation

Hidehiro Ohki, Rin-ichiro Taniguchi, Tokihiro Kimura, Naomichi Sueda, and Keiji Gyohten "A self-diagnosis under 2D projectivity for local descriptor base template matching", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340W (30 April 2015); https://doi.org/10.1117/12.2182925

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KEYWORDS

Feature extraction

Cameras

Image processing

Digital image processing

Image resolution

Image retrieval

Tolerancing

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