Template Matching Score

Implement a template matching technique to compute the similarity between a given template and an image patch. This task involves calculating a score that measures the degree of similarity between the two.

Template matching is a fundamental concept in computer vision that involves locating a smaller image, called the template, within a larger image. The Sum of Squared Differences (SSD) is a widely used metric for this purpose, which calculates the sum of the squared differences between corresponding pixel values in the template and the image patch. This metric is based on the idea that a lower SSD value indicates a better match between the template and the image patch.

To compute the SSD, follow these steps:

Iterate over each pixel in the template and the corresponding pixel in the image patch.
Calculate the difference between the pixel values.
Square the difference.
Sum up the squared differences.

SSD = \sum_{i,j}(I_{i,j} - T_{i,j})^2

This technique is widely used in object recognition systems.

To compute the SSD, follow these steps:

Iterate over each pixel in the template and the corresponding pixel in the image patch.
Calculate the difference between the pixel values.
Square the difference.
Sum up the squared differences.

SSD = \sum_{i,j}(I_{i,j} - T_{i,j})^2

This technique is widely used in object recognition systems.

Example:

Constraints:

Test Results

Template Matching Score

Example:

Constraints:

Test Results