摘要
Through deducing the relationship between support vector machine （SVM） and correlation principle, the optimal hyperplane is proved as a correlation filter when the kernel function is the linear kernel. So a new correlation filter, named linear SVM correlation filter （LSCF）, is proposed. The filter has not only shift-invariance, but also SVM properties. The real images of laser radar are used as experiment data, and LSCF is used to solve the in-plane rotation invariance. The results show that the filter can recognize the different rotated objects, and the correlation output is stable. The filter is insensitive to the noise and gray change, and has good discrimination ability. In the same design way, LSCF is also suitable to solve other problems of correlation distortion.
Through deducing the relationship between support vector machine （SVM） and correlation principle, the optimal hyperplane is proved as a correlation filter when the kernel function is the linear kernel. So a new correlation filter, named linear SVM correlation filter （LSCF）, is proposed. The filter has not only shift-invariance, but also SVM properties. The real images of laser radar are used as experiment data, and LSCF is used to solve the in-plane rotation invariance. The results show that the filter can recognize the different rotated objects, and the correlation output is stable. The filter is insensitive to the noise and gray change, and has good discrimination ability. In the same design way, LSCF is also suitable to solve other problems of correlation distortion.
