Edge feature extraction for ATR using the Helmholtz principle and level set methods
Arjuna Flenner · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Edge features are often used in computer vision for image exploitation algorithms. A method to extract edge features that is robust to contrast change, translation, rotation, noise and scale change is presented. This method consists of the following steps: decompose the image into it's level set shapes, smooth the shapes, locate sections of the shape borders that have nearly constant curvature, and locate a key point based on these curve sections. The level sets are found using the Fast Level Set Transform (FLST). An affine invariant smoothing technique was then applied to the level set shape borders to reduce pixel effects and noise, and an intrinsic scale was estimated from the level set borders. The final step was key point location and scale estimation using the Helmholtz principle. These key points were found to be more resilient to large scale changes than the SIFT key points.