Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4943504 | Expert Systems with Applications | 2017 | 13 Pages |
Abstract
Indoor scene classification is usually approached from a computer vision perspective. However, in some fields like robotics, additional constraints must be taken into account. Specifically, in systems with low resources, state-of-the-art techniques (CNNs) cannot be successfully deployed. In this paper, we try to close this gap between theoretical approaches and real world solutions by performing an in-depth study of the factors that influence classifiers performance, that is, size and descriptor quality. To this end, we perform a thorough evaluation of the visual and depth data obtained with an RGB-D sensor to propose techniques to build robust descriptors that can enable real-time indoor scene classification. Those descriptors are obtained by properly selecting and combining visual and depth information sources.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Cristina Romero-González, Jesus MartÃnez-Gómez, Ismael GarcÃa-Varea, Luis RodrÃguez-Ruiz,