A Rotation Scale Invariant Scene Matching Method Based on Gradient Binarization

Abstract

A gradient-based rotational scale-invariant binarization scene matching method relates to the field of scene recognition. Based on the simple comparison of the gray intensity in the classic binary description BRIEF algorithm, the present invention proposes adding horizontal and vertical gradient value comparisons to save the texture information of the sampling area, thereby reducing the matching error rate. In addition, an image scale pyramid is established to perform image feature point detection and feature description in different scales, and the direction of the center of gravity vector is added to the descriptor calculation process, so that the binary descriptor has direction and scale invariance. Experiments show that the binarization-based rotation-invariant gradient sampling descriptor proposed in this paper has high robustness. In the case of large rotation and scale transformation of the scene image, the average matching accuracy is 73.06% higher than that of the BRIEF algorithm. .

Description

technical field [0001] The invention relates to the field of scene recognition, in particular to a gradient binarization-based rotation scale invariant scene matching method suitable for scene matching. Background technique [0002] Scene matching is often used to search for the same content in two scenes, and has a wide range of applications in the fields of scene recognition and object recognition. [0003] As a specific implementation method of scene matching, image feature point matching realizes the matching and recognition of the current scene by matching the feature points of the current image with the historical images in the database. Therefore, image feature point matching has become the focus of current research. Feature point matching includes three parts: feature point detection, feature point description, and feature point matching. First, search for stable feature points on the two images. These feature points can still be detected by the detector after scale...

AI Technical Summary

Problems solved by technology

Although SURF has greatly improved the calculation speed based on the idea of ​​SIFT algorithm, it still cannot meet the needs of real-time systems.

In order to further reduce the complexity of the algorithm, Calonder et al. proposed the BRIEF[3] descriptor based on binary description, which randomly selects a number of (usually 128, 256 or 512) sampling point pairs in the sampling area around the feature point , use 0 or 1 to represent the gray scale relationship between the sampling point and the two points, and finally form a 128, 256 or 512-dimensional binary feature descriptor. When matching, the Hamming distance is used for calculation, and the calculation speed is greatly improved. Meets the matching requirements for real-time systems, but BRIEF is not rotation invariant due to the lack of orientation normalization for descriptors

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