Region Feature Descriptor Adapted to High Affine Transformations
This work addresses a domain-specific problem in computer vision for improving feature matching accuracy under affine transformations, representing an incremental advancement.
The paper tackled the problem of feature descriptors losing accuracy under high affine image transformations by proposing a region feature descriptor that simulates affine transformations via classification and combines grayscale histograms with normalized position information. Experimental results showed the proposed descriptor achieved higher precision and robustness in feature matching compared to classical descriptors.
To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.