Augmented reality based segmentation of outdoor landscape images

The segmentation and classification of image regions are very important tasks in the field of computer vision, and yet they remain one of its greatest challenges. These challenges arise from the fact that the same objects can come in different colors, shapes and sizes, and can appear in different contexts and under different illumination. In an attempt to overcome these obstacles, in this paper we propose a system for segmentation and classification of image regions on outdoor landscape images based on augmented reality and CORINE land cover (CLC) classification. We compare the results obtained by the proposed system with the results obtained by the k-NN algorithm, and show that the proposed algorithm outperforms the k-NN one, and generally gives favorable segmentation and classification results.