Anti-rotation HDO local feature description method for target robust identification

Abstract

The present invention discloses an anti-rotation HDO local feature description method for target robust identification. The method comprises the following steps: setting a circular detection region of an image; calculating a similar RGT gradient of the image; calculating a structure tensor, and obtaining a main direction and consistency of each pixel point; dividing a circular image into a plurality of ring-shaped sectors to be used as cells, combining every four adjacent cells into a block, and calculating a feature vector of each Block; and combining the feature vector of each Block together to form an HDO feature description. According to the method disclosed by the present invention, an RGT conversion technology is introduced, and the structure tensor is constructed during construction of a pixel point circular neighbourhood, so as to ensure that the main direction and consistency of each pixel point do not change when a picture rotates, and then a main direction histogram of each Block is calculated, and by adopting a spatial pool division manner of the ring-shaped sectors, the obtained local feature has an excellent differentiation ability and anti-rotation conversion ability.

Description

technical field [0001] The invention relates to a local feature description method, in particular to an anti-rotation HDO local feature description method for target robust recognition. Background technique [0002] Local feature description is widely used in image object detection and recognition. In the past two decades, researchers have proposed a large number of local feature description operators. In order to deal with changes in scale, illumination, contrast, and rotation in images, feature descriptions are usually required to be invariant to illumination, scale, and rotation. The well-known description operators with illumination, scale and rotation invariance include SIFT and SURF operators, but their construction process is relatively complicated and the amount of calculation is large. The Histogram of Gradient Orientation (HOG) proposed by Dalal in 2005, due to the simple calculation, can better capture the shape characteristics of the image, and obtain good detec...

AI Technical Summary

Problems solved by technology

The well-known description operators with illumination, scale and rotation invariance include SIFT and SURF operators, but their construction process is relatively complicated and the amount of calculation is large

The Histogram of Gradient Orientation (HOG) proposed by Dalal in 2005, due to the simple calculation, can better capture the shape characteristics of the image, and obtain good detection performance in face and pedestrian detection, but HOG is in the case of large noise and chaotic image background. Therefore, in order to overcome these weaknesses of HOG, Wonjun Kim proposed a more robust HDO feature in 2014. The HDO feature can robustly describe the local main direction structure information of the image, and has strong resistance to noise and illumination. Robustness, at the same time, it focuses on the description of the main direction structure, similar to the human visual system, and can also ensure good discrimination in complex scenes, so it can be used for various target detection problems, but the original HDO features do not have rotation invariance, thus limiting its general application to a large extent

Of course, the feature description has rotation invariance, which does not guarantee that the feature must have good robust recognition performance. Therefore, it is necessary to consider making the feature description method able to resist the possible rotation transformation in the image under the premise of ensuring the identification ability.

[0004] In the existing anti-rotation transformation local feature description technology, SIFT and SURF operators estimate the local main gradient direction as the reference direction, and then perform appropriate direction calibration to achieve anti-rotation transformation, but there may be ± 20° error due to gradient direction estimation , so it is easy to lead to misjudgment

In order to obtain the rotation-invariant feature mode of LBP feature, the pattern feature with the minimum value is found through the sorting strategy. This processing strategy can also be borrowed in the gradient-based direction histogram, for example, find the maximum gradient direction, and then use this as a benchmark, press Rearrange the gradient direction histogram clockwise or counterclockwise, but because it is susceptible to noise and other factors, it is sometimes difficult to obtain a stable sorting relationship, which will cause trouble for the final detection and recognition

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