Corner Detection Based on Improved Shearlet Transform

Image corners have important theoretical significance and application value in the fields of image processing and analysis, machine vision, etc., however, the existing multi-scale analysis corner detectors fail to make full use of the multi-scale and multi-direction structural information, which affects their detection accuracy and diagonal positioning ability. Based on the basic principle of shear wave transform, the limitations of multi-scale and multi-direction image decomposition are analyzed, and the scale function is improved to the Butterworth type with a relatively flat falling edge, and then a new non-zero integrable function is obtained for the auxiliary function, and then a new multi-scale and multi-directional image decomposition method based on improved shear wave is proposed. Experimental results show that the proposed method not only partially improves the small-scale texture effect obtained after multi-scale image decomposition, but also solves the problem of large difference and messy texture between the previous small-scale local image and the original image of the clipping wave, and the image reconstruction effect is also improved and has strong robustness. In the field of image corner detection, the algorithm can make full use of multi-scale and multi-direction structural information to improve the positioning accuracy of image corner detection, reduce the error detection and missed detection rate, and has good robustness.