A NCC Image Matching Method Based on Enzyme Numerical Membrane System

Abstract

The invention discloses an NCC image matching algorithm enzyme numerical film system, which includes a surface film for storing an image to be matched, a template image and an initialized enzyme, and simultaneously outputs position coordinates for calculating the matching position of the template image in the image to be matched; ComputeT film, used to calculate the parameters required for the template image to perform NCC calculation; ComputeMatchingCood film, used to calculate the matching position of the template image in the image to be matched; several ComputeNccValue lk Membrane, which is used to calculate the NCC value of the template image at each position in the original image after moving in parallel. The membrane system of the present invention has strong parallelism, so that the template image does not need to be slidingly matched on the original image, but calculates the value of NCC with all sub-images at the same time, allowing multiple time-consuming NCC calculations to be performed in parallel under serial calculations Execution, the computational time complexity is constant and has nothing to do with the data size, thus greatly reducing the time complexity of the entire NCC image matching algorithm.

Description

technical field [0001] The invention belongs to the technical field of image matching, and in particular relates to an NCC image matching method based on an enzyme numerical membrane system. Background technique [0002] The NCC algorithm is a classic algorithm in image matching. The definition of image matching means that there is a target image T, and the region that is the same target as T is found in the original image. Assuming that the size of the original image I is M×N, and the size of the template T is m×n, the traditional NCC image matching principle is: starting from the original image coordinates (0,0), the template is from left to right on the original image, from top to Swipe down, and every time you slide to a pixel, calculate the ncc value between the template and the sub-image corresponding to the current image, store the ncc values ​​calculated at all positions, and take the position corresponding to the maximum value of ncc as the template T in the origina...

AI Technical Summary

Problems solved by technology

Assuming that the size of the original image I is M×N, and the size of the template T is m×n, the traditional NCC image matching principle is: starting from the coordinates of the original image (0,0), the template is on the original image from left to right, from top to Swipe down, and every time you slide to a pixel, calculate the ncc value between the template and the sub-image corresponding to the current image, store the ncc values ​​calculated at all positions, and take the position corresponding to the maximum value of ncc as the template T in the original image The matching position on the , such as figure 1 As shown, a total of (M-m)*(N-n) NCC needs to be calculated. It can be seen that under the existing serial computing framework, the time complexity of the NCC algorithm increases with the resolution, and the calculation time increases polynomially.

When the resolution of the image to be matched and the template image is high, the existing serial architecture is difficult to achieve real-time calculation

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