In this paper we propose a simple and effective way to integrate structural information in
random forests for semantic image labelling. By structural information we refer to the …

There is an increasing interest in semantically annotated 3D models, eg of cities. The typical
approaches start with the semantic labelling of all the images used for the 3D model. Such …

High-quality urban reconstruction requires more than multi-view reconstruction and local
optimization. The structure of facades depends on the general layout, which has to be …

Ensembles of randomized decision trees, known as Random Forests, have become a
valuable machine learning tool for addressing many computer vision problems. Despite their …

We present an efficient scene layout aware object detection method for traffic surveillance.
Given an input image, our approach first estimates its scene layout by transferring object …

We propose a structured Hough voting method for detecting objects with heavy occlusion in
indoor environments. First, we extend the Hough hypothesis space to include both object …

Architects, urban planners, the film and gaming industry as well as map makers are all very
much interested in detailed, semantic city models. While generating a synthetic city has …

We present a context aware object detection method based on a retrieve-and-transform
scene layout model. Given an input image, our approach first retrieves a coarse scene …

In this work we introduce a novel solution to the semantic image labelling problem, ie the
task of assigning semantic object class labels to individual pixels in a test image …

In professional/consumer domains, video databases are broadly applied, facilitating quick
searching by fast region analysis, to provide an indication of the video contents. For realtime …