An RST-Tolerant Shape Descriptor for Object Detection

In this paper, we propose a new object detection method that does not need a learning mechanism. Given a hand-drawn model as a query, we can detect and locate objects that are similar to the query model in cluttered images. To ensure the invariance with respect to rotation, scaling, and translation (RST), high curvature points (HCPs) on edges are detected first. Each pair of HCPs is then used to determine a circular region and all edge pixels covered by the circular region are transformed into a polar histogram. Finally, we use these local descriptors to detect and locate similar objects within any images. The experiment results show that the proposed method outperforms the existing state-of-the-art work.