Scene understanding is a widely studied problem in computer vision. Many works approach this problem in indoor environments assuming constraints about the scene, such as the typical Manhattan World assumption. The goal of this work is to design and evaluate a global descriptor for indoor panoramic images that encloses information about the 3D structure. This descriptor is based on the detection of representative lines of the scene, which encode the scene structure. Our work focuses on omnidirectional imagery, where observed lines are longer than in conventional images and the whole scene is captured in a single image. Experiments using two public datasets analyze the performance of the descriptor for scene categorization. We also analyze the influence of different parameters and show sample results for a navigation assistance application.
Publication Type: Conference Paper
Publication: International Conference on Image Processing (ICIP), IEEE (2014)