Method for increasing number of feature points in weak texture region of image

Abstract

The invention belongs to the technical field of computer visions, and discloses a method for increasing the number of feature points in a weak texture region of one image. The method comprises the steps: carrying out the detail extraction of one image, and obtaining the detail texture map of one image; building a Gaussian difference pyramid for an original image, and detecting corners; carrying out the parallel computing of the detail extraction and the Gaussian difference pyramid, carrying out the corner extraction of the detail map, and finally forming a feature point set through merging; generating a binary descriptor with rotation invariance on the corresponding image for the extracted feature points; carrying out the matching and filtering of the descriptors of two images, and obtaining a correct matching point set. The method employs a binary operator, and can generate a description vector through simple summation and comparison. Meanwhile, the method employs the parallel computing of corner detection and spatial extreme point detection, greatly improves the stability of feature points, is low in coupling degree of calculation steps, is high in parallelism degree, and is very suitable for hardware implementation.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for increasing the number of feature points in an image weak texture area. Background technique [0002] Image matching is an extremely important technology in the field of computer vision, and its applications include pattern recognition, automatic navigation, 3D reconstruction, impact stitching and other fields. Currently, image matching mainly uses matching methods based on image local features. An excellent local feature needs to have the properties of small amount of calculation, good robustness, and insensitivity to image changes. The performance of image local features has become the technical bottleneck of these applications, and the result directly affects the effect of its application. With the rapid rise of the high-definition video processing industry, the application of high-definition technologies such as 4K and even 8K is becoming more...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the existing problems in the prior art are: the SIFT algorithm is highly complex, requires a large amount of floating-point calculations and buffer space, and cannot meet real-time requirements; the binary algorithm has low complexity, high algorithm efficiency, and poor performance. The weak texture area of ​​the image cannot effectively extract features

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