Classification of Depth and Surface Edges with Deep Features

Abstract

Edges in 2D images fall into two categories: depth edges and surface edges, depending on if the edge corresponds to an abrupt change in depth (the distance from the camera). This edge type is an efficient, robust, and effective information in many applications. In this paper we study the problem of automatic classification of the two types of edges. We use features discovered by deep convolutional neural networks to predict the edge type. The labeled sample edges for training our classifiers are semiautomatically generated via a technique of the edge-segment geometrical duality. Experiments are carried out to demonstrate the effectiveness of the proposed edge type classification methods.