Mapping from Air Photographs

Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs

We describe an automatic procedure for building risk maps of unexploded ordnances (UXO) based on historic air photographs. The system is based on a cost-sensitive version of AdaBoost regularized by hard point shaving techniques, and integrated by spatial smoothing. The result is a map of the spatial density of craters, an indicator of UXO risk.

Distinguishing paintings from photographs

We addressed the problem of automatically differentiating photographs of real scenes from photographs of paintings. We found that photographs differ from paintings in their color, edge, and texture properties. Based on these features, we trained and tested a classifier on a database of 6000 paintings and 6000 photographs. Using single features results in 70–80% correct discrimination performanc...

Texture Synthesis From Photographs

The goal of texture synthesis is to generate an arbitrarily large high-quality texture from a small input sample. Generally, it is assumed that the input image is given as a flat, square piece of texture, thus it has to be carefully prepared from a picture taken under ideal conditions. Instead we would like to extract the input texture from any surface from within an arbitrary photograph. This ...

NUROP – Modeling from Photographs

In many areas, we need a virtual representation of our real world surroundings. However, reconstructing a model of an actual scene is usually a tedious process. In this report, we explore a novel workflow for architectural model reconstruction from photographs. We keep the interaction with our system at a 2D level to reduce the complexity of our user interaction using a mesh-grid that the user ...

Recognition of Tree Species on Air Photographs by Crown Characteristics