Edge feature determination by using neural networks based on a blurred edge model

Abstract

This paper proposes a new model fit-type edge feature measurement method. The characteristic of the proposed method is that it introduces a blurred edge model which matches well with a gray-level pattern of an edge in an image actually observed. The blurred edge model is constructed by using not only edge features, which are the edge position and orientation within the pixel, but also the point spread function which expresses the image degradation during the image recording process as parameters. By using this model, the gray level of the multiple pixels near the edge for the various edge feature values is calculated, and a map is obtained from the edge features to the gray-level pattern. Next, the inverse map which obtains edge features from a gray-level pattern is obtained in advance through learning by using error backpropagation-type neural networks consisting of three layers. By using the obtained inverse map, the edge features are determined from the gray-level pattern of the actually observed image. Conventionally, since it was necessary to obtain this inverse map analytically, the edge model that could be used was restricted to the step-edge type. On the other hand, with the method being proposed which utilizes the neural networks, an arbitrary optimal edge model for an individual image recording device can be used. For this reason, edge features can be determined precisely with this method even from local information. Many measurement experiments which changed the edge position and orientation were performed and the effectiveness of this method was confirmed.