We present a novel method for shape detection in natural scenes based on incomplete contour fragments and nearest neighbour search. In contrast to popular methods which employ sliding windows, chamfer matching and SVMs, we characterise each contour fragment by a local descriptor and perform a fast nearest-neighbour search to find similar fragments in the training set. Based on this idea, we show how to learn robust object models from training images, to generate reliable object hypotheses, and to verify them. Despite its extreme simplicity and speed, our method produces competitive detection results on the challenging ETHZ dataset.
We present a novel method for shape detection in natural scenes based on incomplete contour fragments and nearest neighbour search. In contrast to popular methods which employ sliding windows, chamfer matching and SVMs, we characterise each contour fragment by a local descriptor and perform a fast nearest-neighbour search to find similar fragments in the training set. Based on this idea, we show how to learn robust object models from training images, to generate reliable object hypotheses, and to verify them. Despite its extreme simplicity and speed, our method produces competitive detection results on the challenging ETHZ dataset.