Semantic Segmentation of Urban Scenes Using Spatial Contexts

Semantic Segmentation of Urban Scenes Using Dense Depth Maps

In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps. Five viewindependent 3D features that vary with object class are extracted from dense depth maps at a superpixel level for training a classifier using randomized decision forest technique. Our formulation integrates multiple features in a Markov Random Field (MRF) framework to segme...

ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompete...

Semantic Similarity of Spatial Scenes

Measuring spatial - temporal of Yazd urban form using spatial metrics

Abstract Urban form can be affected by diverse factors in different times. Socio- economic, political and physical factors are among the main contributors. So, one of the most important challenges of urban planners is measuring and identifying urban development pattern in order to direct and strengthen it to sustainable pattern and right direction. The case study of the present paper is the ...

Segmentation of urban scenes from aerial stereo imagery

This paper presents a focusing strategy for the 3-D reconstruction of urban scenes from aerial stereo pairs. It consists in segmenting the scene into above-ground objects (buildings or vegetation), and it relies on 3-D and radiometric analyses. The classification is able to cope with extended above-ground, adjacent objects, slopes, and it is robust to image and scene variability.