CondNet: Conditional Classifier for Scene Segmentation

Classifier Conditional Posterior Probabilities

Classifiers based on probability density estimates can be used to find posterior probabilities for the objects to be classified. These probabilities can be used for rejection or for combining classifiers. Posterior probabilities for other classifiers, however, have to be conditional for the classifier., i.e. they yield class probabilities for a given value of the classifier outcome instead for ...

Scene Segmentation for Behaviour Correlation

This paper presents a novel framework for detecting abnormal pedestrian and vehicle behaviour by modelling cross-correlation among different co-occurring objects both locally and globally in a given scene. We address this problem by first segmenting a scene into semantic regions according to how object events occur globally in the scene, and second modelling concurrent correlations among region...

Conditional Random Field for Natural Scene Categorization

Conditional random field (CRF) has been widely used for sequence labeling and segmentation. However, CRF does not offer a straightforward approach to classify whole sequences. On the other hand, hidden conditional random field (HCRF) has been proposed for whole sequences classification by viewing the segment labels as hidden variables. But the objective function of HCRF is non-convex because of...

Visual Scene Segmentation

Piecewise Planar Segmentation for Automatic Scene Modeling

In this paper, we investigate the problem of the automatic creation of 3D models of man-made environments that we represent as collections of textured planes. A typical approach is to automatically compute a sparse feature reconstruction and to manually give their planememberships as well as the delineation of the planes. Textures are then extracted from the images while optimizing the model, t...