Method and system for extracting image feature information

Abstract

The embodiment of the invention discloses a method for extracting image feature information. The method comprises allocating an acquired image to be processed to a parallel computing module for parallel image processing, determining the scale information, the coordinate Information and the direction of a key point by calling a plurality of computing nodes in the parallel computing module, and extracting the feature information from the image to be processed according to the scale information, the coordinate information and the direction of the key point. By means of the unified scheduling of a CPU, the method performs distributed parallel computing on the a computing task, greatly saves the time for establishing the scale space, detecting the key points in each scale space and determining the key point direction of the image, improves the efficiency of a SIFT algorithm to extract the image feature information, relieves the load pressure of the CPU, and improves the overall system operating speed, is conducive to reliable and stable system operation. In addition, the embodiment of the invention further provides a corresponding system which further makes the method practical and which has corresponding advantages.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular to a method and system for extracting feature information of an image. Background technique [0002] With the advent of the Internet of Things and 5G communication era, data is growing explosively, especially in the field of image processing dominated by vision, which generates massive image data. Image data is more and more widely used in various industries. It is an important part of image application to perform feature detection on data to obtain key information in image data. [0003] SIFT algorithm (Scale-Invariant Feature Transform, scale-invariant feature transformation algorithm), is a kind of computer image feature detection algorithm with good robustness, which has scale, rotation and translation invariance. The algorithm first looks for extreme points in different scale spaces, and describes the position, scale and rotation invariance i...

AI Technical Summary

Problems solved by technology

[0005] Due to the establishment of multiple scale spaces, it takes too much time; it is also necessary to compare each feature point in each scale space to determine the key points, resulting in the entire SIFT algorithm taking more time and slower speed, and the efficiency of extracting feature points is low.

In addition, due to the lack of CPU (Central Processing Unit, central processing unit) cores of conven...

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