Detecting Shapes in 2D Point Clouds Generated from Images

We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, that are in turn extracted from images. In this model-based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a past stochastic models on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.