Local Orthogonal Moments for Local Features

By introducing parameters with local information, several types of orthogonal moments have recently been developed for the extraction of local features in an image. But with the existing orthogonal moments, local features cannot be well-controlled with these parameters. The reason lies in that zeros distribution of these moments' basis function cannot be well-adjusted by the introduced parameters. To overcome this obstacle, a new framework, transformed orthogonal moment (TOM), is set up. Most existing continuous orthogonal moments, such as Zernike moments, fractional-order orthogonal moments (FOOMs), etc. are all special cases of TOM. To control the basis function's zeros distribution, a novel local constructor is designed, and local orthogonal moment (LOM) is proposed. Zeros distribution of LOM's basis function can be adjusted with parameters introduced by the designed local constructor. Consequently, locations, where local features extracted from by LOM, are more accurate than those by FOOMs. In comparison with Krawtchouk moments and Hahn moments etc., the range, where local features are extracted from by LOM, is order insensitive. Experimental results demonstrate that LOM can be utilized to extract local features in an image.

Automated summary

By introducing parameters with local information, a new framework called transformed orthogonal moment (TOM) is developed to overcome the limitations of existing orthogonal moments in controlling local features. The basis function's zeros distribution can be adjusted using a novel local constructor, resulting in the proposed local orthogonal moment (LOM) with more accurate feature extraction than fractional-order orthogonal moments (FOOMs). LOM also has an order-insensitive range compared to other moments. Experimental results demonstrate the effectiveness of LOM in extracting local features in images.