Efficient template matching algorithm based on interval estimations on correlation

Takahiro Mae; Shun'ichi Kaneko; Takayuki Tanaka

doi:10.1117/12.645623

6 December 2005 Efficient template matching algorithm based on interval estimations on correlation

Takahiro Mae, Shun'ichi Kaneko, Takayuki Tanaka

Proceedings Volume 6051, Optomechatronic Machine Vision; 605102 (2005) https://doi.org/10.1117/12.645623
Event: Optomechatronic Technologies 2005, 2005, Sapporo, Japan

Abstract

We propose an efficient template matching algorithm for binary image search. When we use template matching techniques, the computation cost depends on size of images. If we have large size images, we spend a lot of time for searching similar objects in scene image to template image. We design a scanning-type upper limit estimation that can be useful for neglect correlation calculation. For calculating the scanning-type upper limits, template and scene images are divided into two regions: R-region and P-region. In R-region, an upper limit of correlation coefficients can be derived as an interval estimation based on mathematical analysis of correlations of the object image and a pivot image. In P-region, another upper limit is formalized based on the number of white and black pixels in a template and the object image. By use of these upper limits, the scanning-type upper limit estimation of correlation coefficients can be formalized for the efficient matching algorithm. This upper limits estimation isn't over true values of correlation, so the accuracy of search by conventional search is the same as one by conventional search. The experiments with document images show the effectiveness and efficiency of the proposed matching algorithm. In these experiments, computation time by the proposed algorithm is between 5 and 20% compare of the conventional search.

Citation Download Citation

Takahiro Mae, Shun'ichi Kaneko, and Takayuki Tanaka "Efficient template matching algorithm based on interval estimations on correlation", Proc. SPIE 6051, Optomechatronic Machine Vision, 605102 (6 December 2005); https://doi.org/10.1117/12.645623

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