Flexible view recognition for indoor navigation based on Gabor filters and support vector machines

Real-world scenes are hard to segment into (relevant) objects and (irrelevant) background. In this paper, we argue for view-based vision, which does not use segmentation, and demonstrate a practical approach for recognizing textured objects and scenes in office environments.

A small set of Gabor filters is used to preprocess texture combinations from input images. The impulse responses of the filters are transformed into feature vectors that are fed to support vector machines. Pairwise feature comparisons decide the classification of views.

We validate the approach on a robot platform using three different types of target objects and indoor scenes: people, doorways, and written signs. The general-purpose system can run in real time, and that recognition accuracies of practical utility are obtained.