Curvature Scale Space Application to Distorted Object Recognition and Classification

Contour classification methods which operate directly on an image are greatly affected by small magnitude transformations to the image. In this paper, a contour classification method is developed which takes advantage of curvature scale space (CS²) and a linear support vector machine (SVM) classifier. The CS² representation boasts invariance to transformations including: scaling, rotation, translation and noise. In addition, the linear SVM is a robust tool for classification problems involving multiple labels. The combination of these tools produces a classifier well suited for object recognition in photographs where distortion is present.