One-Dimensional Vector based Pattern Matching

TL;DR

This paper introduces a novel pattern matching method that transforms 2D images into 1D vectors, enabling efficient comparison using similarity measures, and demonstrates its superior performance over traditional methods.

Contribution

The paper proposes a new 1D vector transformation approach for pattern matching, improving accuracy and efficiency over conventional 2D methods.

Findings

Superior performance on various template sizes

Effective use of SAD, SSD, and Euclidean measures

Improved accuracy over traditional methods

Abstract

Template matching is a basic method in image analysis to extract useful information from images. In this paper, we suggest a new method for pattern matching. Our method transform the template image from two dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and Euclidean are used to compute the likeness between template and all sub-windows in the reference image to find the best match. The experimental results show the superior performance of the proposed method over the conventional methods on various template of different sizes.