One-Dimensional Vector based Pattern Matching
This addresses the problem of efficient and accurate pattern matching in image analysis, but it appears incremental as it builds on existing similarity measures with a transformation approach.
The paper tackles the problem of template matching in image analysis by proposing a method that transforms both the template and reference image sub-windows into one-dimensional vectors, using similarity measures like SAD, SSD, and Euclidean distance to find matches. The result is superior performance over conventional methods across various template sizes, as shown in experimental results.
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.